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In this paper, a hierarchical clustering selection based weighted random forests scheme is proposed for fault classification in complex industrial processes. Model diversity and the strength of each model are deemed to be two key issues for the performance of ensemble learning method. To improve the diversity between classification trees and the performance of individual classification trees in random forests, the hierarchical clustering method is applied for offline model selection in random forests, which can simultaneously reduce the online fault classification complexity. Meanwhile, the weighted voting rule is used in random forests instead of majority voting, in order to boost the good performance models and weaken the bad ones. Detailed comparative studies between proposed method and conventional methods have been carried out through the Tennessee Eastman (TE) benchmark process. [ABSTRACT FROM AUTHOR]

Modeling and recognizing faults and outages in a real-world power grid is a challenging task, in line with the modern concept of Smart Grids. The availability of Smart Sensors and data networks allows to “x-ray scan” the power grid states. The present paper deals with a recognition system of fault states described by heterogeneous information in the real-world power grid managed by the ACEA company in Italy. The pattern recognition problem is tackled as two-class classification problem using a Clustering-Evolutionary Computing approach and it is able to generate together with a Boolean decision also a score value. The last is computed through a fuzzy membership function and output values are interpreted as a reliability measure for the Boolean decision rule. As many real-world pattern recognition applications, the starting feature space is structured and the custom based dissimilarity measure adopted leads to a non-Euclidean dissimilarity matrix. Hence, a comparison of the classification performances between the proposed two-class classifier system and the well-known Support Vector Machine, on the data set at hands, is performed using a suitable kernel designed for the non-Euclidean case. [ABSTRACT FROM AUTHOR]

Fault detection and diagnosis (FDD) in chemical process systems is an important tool for effective process monitoring to ensure the safety of a process. Multi-scale classification offers various advantages for monitoring chemical processes generally driven by events in different time and frequency domains. However, there are issues when dealing with highly interrelated, complex, and noisy databases with large dimensionality. Therefore, a new method for the FDD framework is proposed based on wavelet analysis, kernel Fisher discriminant analysis (KFDA), and support vector machine (SVM) classifiers. The main objective of this work was to combine the advantages of these tools to enhance the performance of the diagnosis on a chemical process system. Initially, a discrete wavelet transform (DWT) was applied to extract the dynamics of the process at different scales. The wavelet coefficients obtained during the analysis were reconstructed using the inverse discrete wavelet transform (IDWT) method, which were then fed into the KFDA to produce discriminant vectors. Finally, the discriminant vectors were used as inputs for the SVM classification task. The SVM classifiers were utilized to classify the feature sets extracted by the proposed method. The performance of the proposed multi-scale KFDA-SVM method for fault classification and diagnosis was analysed and compared using a simulated Tennessee Eastman process as a benchmark. The results showed the improvements of the proposed multiscale KFDA-SVM framework with an average 96.79% of classification accuracy over the multi-scale KFDA-GMM (84.94%), and the established independent component analysis-SVM method (95.78%) of the faults in the Tennessee Eastman process. [ABSTRACT FROM AUTHOR]

Supervised learning for chemical process fault classifications need for a large amount of sampling data with fault labels which significantly consume expert manpower in general. It is seen that active learning is becoming an effective solution for this problem. To improve the quality of sampling data used for expert labeling, this paper proposes sampling evaluation metrics combing information entropy and similarity for active learning strategies in chemical process fault classifications. Specifically, in response to the insensitive direction of the cosine similarity, the Euclidean distance is merged with evaluate the sampling representativeness. Further, combing with information entropy, a comprehensive evaluation index (E-ECos) is established to select the most valuable samples for expert labeling, adding to the training set of classifiers, by labeling them by experts and adding them to the supervised learning training set of the classifier, the classification learning performance can be effectively improved with a relatively small number of actively labeled samples. On the TE process simulation platform, a deep learning network and an ensemble learning network based classifiers are employed to classify industrial process faults, respectively. Experimental results demonstrate the effectiveness of the proposed method. • This paper proposes a fault classification method with an improved active learning. To improve the quality of sampling data used for expert labeling, this method combing information entropy and similarity for active learning strategies in chemical process fault classifications. • Sample similarities: This paper comprehensively evaluates the similarity of samples by evaluating the cosine similarity and Euclidean distance between samples. On the one hand, Euclidean distance complements the problem that cosine similarity is insensitive to absolute direction. On another hand, the Euclidean distance is based on the absolute value of dimensional features. • E-ECOS: Taking advantage of the different sample selecting evaluation metrics of the two active learning algorithms, we establish a comprehensive evaluation index in terms of sample informativeness and sample representativeness. • Future potentiality: The following research is suggested to combine active learning with auto-machine labeling to further reduce the consumption of manpower for fault sample labeling. [ABSTRACT FROM AUTHOR]

In this work, a new fault classification and phase selection methodology for half-wavelength (HWL) transmission lines is developed. Initially, the basic principles of phase currents transformations are presented. Then, an innovative faulted phase selection methodology based on the variation of those currents is developed. The behavior of the electrical quantities related to short circuits in such long lines is very different when compared to conventional lines. The faulted phase selection method employed in this work is built on the fact that those decoupled currents are expected to not vary or vary less than the others, depending on how many phases are in short circuit. The robustness of the method was analyzed considering different load conditions and fault resistances. Furthermore, different sampling rates were considered for the phase currents. Extensive simulation studies showed that the developed method can accurately identify the fault types and the faulted phase with a relatively low sample rate. Moreover, the simulation results showed that the proposed method is not affected by the presence of inter-harmonics and responds precisely with high-resistance faults. The proposed algorithm presented errors smaller than 0.04%. • A new method for ground and phase fault classification based on decoupled currents by means of modal transformation was developed in this study. • The decoupled currents are obtained by means of a modal transformation. • The developed method is robust to faults with high resistance. • The developed method is not affected by the presence of interharmonics. [ABSTRACT FROM AUTHOR]

This paper proposes a hierarchical sparse artificial neural network for classifying the faults in dynamic processes base on limited labeled data. The Stacked auto-encoders (SAE) is developed to extract features from different faults. Each neural network in the proposed SAE is given a sparse constraint to learn a Sparse Stacked auto-encoders (SSAE). Then, the Dynamic time window is combined into SSAE to build Dynamic Sparse Stacked auto-encoders (DSSAE). DSSAE model based semi-supervised fault classification scheme is then formulated to classify the dynamic faulty data. Simulation studies on the Tennessee–Eastman (TE) benchmark process evaluate the performance of the developed method, which indicate that the DSSAE method performs better than both SAE and SSAE. [ABSTRACT FROM AUTHOR]

A large amount of data is generated through monitoring, maintenance, repair and diagnostics of power transformer. However, all these data cannot preindicate the exact type and probability of failure. To overcome the problem this paper presents artificial intelligence based methodology for power transformers fault detection and classification. The possibility of presented monitoring methodology is to assist the operator’s engineers in decision making about urgency of intervention and type of maintenance of power transformer. The article analyzes the application of Mamdani-model and Sugeno-model in fuzzy expert system for fault diagnosis based on the current state of the power transformer. Paper presents two case studies with one unique and five separate controllers. In the first case inputs of controller are results of on-line and off-line transformer tests: age, the overheating temperature of the hot spot, frequency response analysis, temperature of insulation, dissolved gas-in-oil analysis, tgδ and polarization index. Second case study in addition to the existing inputs includes previous measurements. A fuzzy controller (FC) is designed to characterize the operating condition and to determine the urgency of intervention with possibility to indicate probability of specific type of failure. Cumulative probability of occurrence of the faults is also observed in second case study. FCs are tested based on real measurements from Serbian transmission system. The results show acceptable effectiveness in detecting different faults and might serve as a good orientation in the power transformer condition monitoring. [ABSTRACT FROM AUTHOR]

A bearing is an essential component in rotating machinery, one of its principal cause of failure, and its health condition is directly related to the safety and effective operation of such machinery. To the best of our knowledge, it is the first time that a probabilistic fuzzy system is applied to bearing fault classification. The type of probabilistic fuzzy classifier considered is a parsimonious fuzzy rule based model where each rule can diagnose a set of faults each one with its probability. For this kind of real world application, it is desirable to develop interpretable and accurate MIMO fuzzy systems, able to deal with the dimensionality and uncertainty present in data (vibration signals). For parameter estimation we adopt a two steps sequential state-of-the-art data-driven method. First, the antecedents of the rules are estimated using an iterative supervised clustering algorithm. Based on the antecedents the consequent parameters are then estimated. For this, a new method for consequent estimation is proposed. This is based on the observation that for defining a rule consequent not all training data within the region of applicability of that rule are equality relevant. Two criteria for selecting a relevant region in the feature space for consequent parameter estimation are proposed. Results show a statistically significant improvement on the performance of probabilistic fuzzy diagnosers trained with the proposed method, independently of the criterion used for defining the relevant region, when compared with the above mentioned state-of-the-art method. Moreover, the proposed consequent parameter estimation method practically has no overhead on the overall training of the diagnoser. Results show that an equilibrium can be found between the model level of detail and its accuracy. However, when accuracy is the sole comparison criterion, the proposed probabilistic fuzzy systems systematically matches the performance of other data-driven models like distance based methods (K-nearest neighbors), connectionists (probabilistic neural networks), or maximum margin classifiers (support vector machines). [ABSTRACT FROM AUTHOR]

Performance of the most existing fault detection and classification methods can only be guaranteed when each of their own assumptions are met. In other words, a method works well in one condition may not perform well in another. In this paper, a new analytic hierarchy process (AHP) based fuzzy decision fusion system is proposed to tackle the fault classification problem. The AHP approach is introduced to determine the priorities of different classifiers, which are further utilized as the weights in ensemble system. Comparing to conventional equal weighted fusion system, the proposed fuzzy fusion system is able to provide more rational and convincing fault classification result. Effectiveness of the proposed fuzzy fusion system with model evaluation is verified through the Tennessee Eastman (TE) benchmark process. [ABSTRACT FROM AUTHOR]

This paper presents a non-unit protection scheme for series capacitor compensated transmission lines (SCCTL) using discrete wavelet transform and k-nearest neighbor (k-NN) algorithm. All the protective relaying functions such as fault detection, fault classification, faulty phase identification and fault location estimation have been considered in this work. Such a comprehensive work providing all protective relaying functions for protection of double circuit SCCTL utilizing k-NN has not been reported so far. The signal processing and feature extraction are done using discrete wavelet transform due to its capability to differentiate between high and low frequency transient components. For fault detection and classification, only approximate wavelet coefficient of current signal up to level 1 has been used; while for k-NN location estimation, both voltage and current signals of the two circuits are decomposed up to level 3 have been used. Finally, the standard deviation of one cycle pre-fault and one cycle post-fault samples of the approximate wavelet coefficients are calculated to form the feature vector for the k-NN-based algorithm. The performance of the proposed technique is evaluated for large number of fault events with variation in fault type including inter-circuit faults, fault inception angle, fault location and fault resistance. The change in position of series capacitor and different degree of compensation has been discussed. The accuracy of the proposed k-NN-based fault detection and classification module is 100% for all the tested fault cases with a decision period of less than half cycle. The k-NN-based fault location scheme estimates the location of fault with ≤1% error for most of the tested fault cases, which is an exceptional attribute of the proposed scheme as compared with 10–15% error of conventional distance relaying scheme. [ABSTRACT FROM AUTHOR]

Several methods for the location and classification of faults in power transmission lines using computational intelligence and digital signal processing techniques have been described in literature. Artificial neural networks (ANNs) and wavelet transform (WT) have drawn significant attention lately, but they present some drawbacks when dealing with power systems faults where data are often contaminated by noise. This paper proposes an approach by combining independent component analysis (ICA) with travelling wave (TW) theory and support vector machine (SVM). The approach is adequate to locate and recognize faults in high-voltage (HV) transmission lines, while the acquired signals are noisy. Experiments performed for distinct types and locations of faults in a real transmission line model have shown that the proposed combined methods are able to provide excellent performance in fault location. The obtained errors are lower than 1% and accuracy is 100% for the classification of fault signals with noise. It can be stated that this method presents better performance than those regarding the main conventional techniques such as wavelets and neural networks in the presence of noise. [ABSTRACT FROM AUTHOR]

In recent years, power quality has become the main concern in power system engineering. Classification of power system faults is the first stage for improving power quality and ensuring the system protection. For this purpose a robust classifier is necessary. In this paper, classification of power system faults using Empirical Mode Decomposition (EMD) and Support Vector Machines (SVMs) is proposed. EMD is used for decomposing voltages of transmission line into Intrinsic Mode Functions (IMFs). Hilbert Huang Transform (HHT) is used for extracting characteristic features from IMFs. A multiple SVM model is introduced for classifying the fault condition among ten power system faults. Algorithm is validated using MATLAB/SIMULINK environment. Results demonstrate that the combination of EMD and SVM can be an efficient classifier with acceptable levels of accuracy. [ABSTRACT FROM AUTHOR]

The fault classification of chemical processes based on supervised learning generally demands for enough labeled fault samples, which represents a big challenge for practical engineering issues. In the industrial field, when capturing an appearance of process abnormities, human operators usually take account of correlations of relevant variables so as to locate process variables associated with the abnormity before further classifying or diagnosing the corresponding faults. In this context, accurate abnormity localizations can be an effective help for classifying the fault type. Nowadays, deep learning based fault classifications have been extensively studied and widely circulated in the literature. However, the existing approaches rarely concern the supporting role of abnormity locations for fault classifications. Motivated by this observation, this paper proposes a multi-task learning (MTL) based convolutional neural network (CNN) model for concurrent abnormity locations and fault classifications for chemical processes. Specifically, fault samples are labeled with both abnormal variable locations and fault categories before constructing a multi-task learning CNN classifier performing an auxiliary task, abnormity locations, and a primary task, fault classifications. In addition, dynamic task weights are assigned to facilitate task contributions. This method can enjoy effectively reducing the necessary amounts of labeled fault samples in achieving satisfactory fault classification performances. Experiments are carried out on the TE process simulation platform and an industrial coal gasification plant along with comparisons with conventional fault classification methods, verifying the effectiveness of the proposed method. • A CNN model based on multi-task learning is proposed to better adapt to fault diagnosis of chemical industrial processes. • An adaptive multitask learning algorithm is proposed to balance the multi-task training process effectively. • Abnormity locations can improve the accuracy of fault classification and the convergence of the overall network training. • Abnormity locations task branches can share tags to the primary task thus reducing the impact of insufficient fault labels. • Future potentiality: Since MTL method doesn't require architectural constraints, it can be applied to other neural networks. [ABSTRACT FROM AUTHOR]

Rolling element bearings are essential components of a wide variety of industrial machinery and the leading cause of equipment failure. The prediction of Remaining Useful Life (RUL) and fault diagnosis is an essential component of equipment diagnosis and health management. Real-time Conditional Based Monitoring (CBM) faces a significant challenge in the Internet of Things and Industrial 4.0 eras due to automatically processing enormous quantities of data. A novel three-phase architecture based on deep learning has been proposed to perform real-time monitoring. The model is end-to-end adaptive, and the data is sequentially processed. The proposed hybrid approach incorporates change-point detection, RUL prediction, and fault classification. Adaptive data pre-processing is employed in conjunction with the unsupervised CPD module, followed by a model training on bearing data deterioration to increase efficiency. The autoencoder employed for CPD is also utilized to supply additional features from the bottleneck layer to acquire an enhanced Health Index (HI). The experiments were conducted on NASA's prognosis and diagnosis datasets, and the proposed method's effectiveness was compared to other benchmark approaches. The results show that the proposed methodology can execute real-time CBM efficiently and reliably, outperforming other methods. [Display omitted] • Real-time condition-based monitoring of bearings has been proposed. • The methodology can determine RUL as well as bearing fault categorization. • The methodology is based on a three-phase architecture that is end-to-end adaptive. • For real-time monitoring, data is sequentially processed between each phase. • Improved accuracy is achieved as the methodology integrates change-point detection. [ABSTRACT FROM AUTHOR]

• Fault classification and localization are carried out in a photovoltaic system. • A multi-output scalable machine- and deep-learning approach is developed. • A 50% reduction in the number of sensors needed per string is achieved. • Deep Learning models are more resilient to noise and are more accurate. • Accuracy up to 99.98% and cross-entropy loss as low as 0.002 are attained. Fault classification and localization are imperative to maintaining an efficient photovoltaic (PV) system. Due to the environmental factors that PV systems function in, they can be prone to multiple types of fault conditions such as string-to-string (SS), string-to-ground (SG), and open-circuit faults (OC). If left undetected, these faults can lead to power loss and eventually system failures. To help mitigate such outcomes, this paper presents an approach to detect, classify, and locate SS, SG, and OC faults using multi-output deep learning (DL) algorithms: convolutional neural networks (CNN), long short-term memory (LSTM), and bi-directional long short-term memory (Bi-LSTM) networks. The proposed approach reduces the number of sensors needed per string by 50% compared to the previous approaches in the literature. It also allows for increased scalability and the development of a multi-label dataset through an uncoupled modeling scheme. Fault classification and location are achieved with accuracies of 99.94% and 99.54%, respectively. Further, a comparison is conducted among the proposed DL and conventional machine learning algorithms using multiple evaluation metrics. The proposed method is validated by testing multiple datasets and adding noise. The results obtained reflect high reliability and demonstrate the effectiveness and scalability of the proposed multi-output DL approach. [ABSTRACT FROM AUTHOR]

The conventional distance relaying algorithms are unable to detect the inter-circuit faults, cross-country faults, high resistance faults which may occur in a double circuit line. This paper presents combined Discrete Wavelet Transform (DWT) and Support Vector Machine (SVM) based directional relaying and fault classification scheme including inter-circuit faults, cross-country faults and high resistance faults. SVM modules are designed for forward or reverse fault identification and fault classification using single terminal data. The 3rd level approximate discrete wavelet transform coefficients of three phase current signals only have been used. Proposed method is tested with variations in fault type, fault location, fault inception angle, fault resistance, inter-circuit faults, and cross-country faults. The proposed method based on SVM does not need any threshold to operate which is an exceptional attribute for a protective function. As SVMs are not based on comparing with some threshold, rather initially the SVMs are trained with the wide variety of fault patterns which is an offline process and then the trained SVMs are tested online to detect and classify the fault within short time. The test results show that all types of shunt faults can be identified within half cycle time. The proposed scheme offers both primary protection to 95% of the line section and also backup protection to 95% of the adjacent reverse and forward line section also. [ABSTRACT FROM AUTHOR]

As part of any Prognostics and Health Management (PHM) system for the shipping industry, the determination of the current health of marine systems is fundamental. As such, diagnostic analytics is performed; a process that is typically constituted by fault detection, fault isolation, and fault identification. Although some efforts have been made to distinguish the faults and malfunctions (fault detection) that can occur in marine systems, the implementation of fault identification to provide a description of any considered fault type and its nature is still an unexplored area due to the lack of fault data. To overcome this, a methodology for the identification of anomalies in marine systems is presented in this paper. The proposed approach aims to analyse the implementation of time series imaging through the application of the first-order Markov chain in tandem with an analysis of both ResNet50V2 and Convolutional Neural Networks (CNNs) as part of the image classification task. To highlight the performance of this methodology, anomalies have been simulated considering the power parameter of a diesel generator. Results demonstrated the potential of time series imaging and image classification approaches, as the Markov-CNN achieved an accuracy of 95% when performing the fault classification task. • On-board technologies for maintenance are a critical area for enabling smart shipping. • A methodology for the identification of anomalies in marine systems is presented. • Time series imaging (Markov) and classification (ResNet50v2 and CNNs) are analysed. • The diesel generator power parameter of a tanker ship is presented as a case study. • Markov-CNN achieves an accuracy of 95% when performing the fault classification task. [ABSTRACT FROM AUTHOR]

• As suggested by majority of the reviewers, Abstract, Introduction and Novelty and contribution, Conclusion of the manuscript is revised. • Flow chart of the proposed scheme is modified and also, qualities of all the figures are improved by decreasing the font of label of figures in the revised manuscript. • Results of new fault cases are summarized in Table 1 Table 2 and Table 3 of the revised manuscript. • The proposed method is compared with one more recently published work [41] of the revised manuscript apart from the earlier comparison results. • As suggested, results for a high resistance single-line to ground fault is included in the revised manuscript. The conventionally used single-ended overcurrent relaying schemes find limitation in providing secured protection to large-scale wind farm medium voltage collector lines due to sensitivity and selectivity problems. In this paper, a two criteria-based improved relaying algorithm is proposed for effective protection of these collector lines. In the proposed method, the faulted line is identified first in a multi-feeder collector line of a doubley-fed induction generator (DFIG)-based wind farm using the transient energies derived from the voltage and current measurements near the collector substation bus. Once the faulted collector line is identified, then a time-time transform assisted fault classfication technique is proposed using only the current measurements of the faulted collector line. The efficacy of the relaying algorithm is validated on numerous fault and non-fault test cases simulated on different collector lines of DFIG-based grid-connected wind farm test system and modified IEEE 39-bus New England system through MATLAB/Simulink®. The algorithm is also implemented on OPAL-RT (OP4510) make real-time digital simulation platform. The simulation results justify the applicability of the proposed relaying scheme for effective detection, classification and isolation of faults in wind farm collector lines. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) solar energy recorded an exponential growth, in worldwide scale, over the last decade. Inevitably, mature PV markets are becoming highly competitive, boosting the need for research and development (R&D) on efficiency and reliability optimization, maintenance and fault diagnosis of key components, such as the PV modules. Indeed, a significant number of studies and technical papers have been published up today, based on an extensive feedback from both laboratory and real (field) investigations of faults and advanced diagnosis applications, especially for crystalline silicon (c-Si) PV modules. Undoubtedly, such experience is of particular interest for current PV plant operators, future investors, maintenance engineers and the R&D sector of PV industry. However, up today, such research, published in the form of reports, technical papers or even books, remains mostly dispersed and unclassified. This paper represents a comprehensive effort to review and highlight recent advances, ongoing research and future prospects, as reported in the literature, on the classification of faults in c-Si PV modules and advanced diagnosis in the field, by means of the increasingly popular method of infrared thermal (IRT) imaging. In particular, the first main part of this paper, reviews the characteristics of the most common fault types of operating PV modules, in terms of electrical and thermal response. Then, the second part gives a thorough review of recently published research, as well as the state-of-the-art, in the fields of IRT-based fault diagnosis and thermal image processing. On the basis of these two individual though supplementary review parts, an overview table is presented, followed by a discussion on the future prospects and challenges, towards the understanding and diagnosis of faults and their propagation in operating PV modules. [ABSTRACT FROM AUTHOR]

Transmission systems (TS) plays a major role in electrical power systems. The interruption caused to the consumer because of TS faults is substantial. So TS needs a proper protection scheme to ensure continuous power supply to the consumers. This study proposes a novel scheme using wavelet technique for classification of faults in TS. In order to do so, the proposed scheme uses current measurements of just one side of the transmission line. Simulations are performed under different fault conditions using MATLAB. The stability of the proposed scheme under various cases is tested andreliable results have been obtained. [ABSTRACT FROM AUTHOR]

This paper proposes a new method of fault detection and classification in asymmetrical distribution systems with dispersed generation to detect islanding and perform protective action based on applying a combination of wavelet singular entropy and fuzzy logic. In this method, positive components of currents at common coupling points are decomposed to adjust detailed coefficients of wavelet transforms and singular value matrices, and expected entropy values are calculated via stochastic process. Indexes are defined based on the wavelet singular entropy in positive components and three phase currents to detect and classify the fault. This protection scheme is put forward for fault detection and is investigated in different types of faults such as single-phase to ground, double-phase to ground, three-phase to ground and line to line in distribution lines in the presence of distributed generations, and different locations of faults are verified when the distributed generation is connected to the utility. The major priority of the proposed protection scheme is its reduction in time (10 ms from the event inception) in distinguishing islanding and protection transmission lines in the presence of distributed generations. [ABSTRACT FROM AUTHOR]

Parallel transmission lines are difficult to protect due to mutual coupling between circuits. This paper proposes a Naïve Bayes classifier (NBC) based fault detection and classification technique for protection of parallel transmission line involving inter-circuit faults. NBC is a good classification tool for larger data sets as the training process takes less time with greater accuracy. Input given to the fault detection module is the fundamental components of three phase current signals of both circuits. Input given to the fault phase identification and fault classification module is the fundamental component of three phase current signals and zero sequence currents of both the circuits. Seven separate classifiers are designed for fault phase identification for A1, B1, C1, A2, B2, C2 and G. From fault phase identification module faults are classified. Accuracy of the proposed method is 100% for fault detection and 99.99% for classification of fault from all the tested fault cases. Response time of the proposed method is within 10 ms for all the fault cases studied. [ABSTRACT FROM AUTHOR]

This paper presents the transient performance analysis of self excited induction generator (SEIG) during both balanced and unbalanced faults using stationary frame d – q axis. Significance of fault detection and fault classification is also investigated in this study. Current signal of SEIG is extracted. Non stationary distorted current waveforms of SEIG during fault condition are considered as superimposition of various oscillating modes. To separate out these oscillating components known as intrinsic mode functions (IMFs), empirical-mode decomposition (EMD) is used. Hilbert transform (HT) is applied on the first four IMFs to extract instantaneous amplitude and frequency. Combination of EMD and HT is known as Hilbert-Huang transform. To classify different faults of SEIG system, least square support vector machine (LSSVM) is used. Finally the superiority of the proposed SVM is established through comparison with support vector machine and probabilistic neural network. [ABSTRACT FROM AUTHOR]

In this paper, time domain relaying schemes for complete protection of parallel transmission lines using wavelet and artificial neural network (ANN) are presented. Four different ANN networks are designed for detection of the fault, fault section identification, classification of fault and location of fault in time domain. The 3rd level approximate discrete wavelet transform (DWT) coefficients of signals of one end are used as input to ANN network. Proposed method is tested with varying fault location, inception angle, fault type and fault resistance. The test results show that the fault is detected and located within 5 ms time accurately. This scheme offers primary protection as well as backup protection to the lines. [ABSTRACT FROM AUTHOR]

This work studies the fault classification issue focused on complicated industrial processes. The basic multivariate statistical approaches, i.e. support vector machine (SVM) as well as principal component analysis (PCA), are studied for multi-fault classification purpose. The Tennessee Eastman (TE) challenging benchmark, which contains 21 abnormalities from real world, is finally utilized to show the effectiveness of the approaches. Such a conclusion can be drawn from the simulation results: although SVM is a powerful tool for multi-classification purposes, the standard PCA approach still shows satisfactory results with less computational efforts. [ABSTRACT FROM AUTHOR]

• A hybrid data-scaling method combining Min-max and Box-Cox transformation was proposed. • It yields accurate fault classification for unbalanced and multi-domain data in industrial fields. • The health index evaluates classification difficulty before generating complex classification models. • Various scaling methods were compared using box plots and t-SNE for selected feature values. • Various classification models (SVM, RF, KNN, and XGBoost) were compared. Fault diagnosis of compressors in air conditioners is challenging owing to the imbalance and nonlinearity of the vibration data because of the contrasting failure modes. This study proposes a hybrid data-scaling method combining Min-Max normalization and Box-Cox transform methods. The Min-Max normalization method was employed to scale the multi-domain data with different failure modes whereas the Box-Cox transformation method transformed the nonlinear distributions of features into normal distributions, thereby rendering the classification of unbalanced and insufficient data easier. The primary features for fault diagnosis were extracted using an embedded feature extraction method and were consequently used to generate fault classification models such as support vector machine (SVM), K-nearest neighbor (KNN), random forest (RF), and extreme gradient boosting (XGBoost) to classify refrigerant deficiencies and motor demagnetization defects. The proposed hybrid data-scaling demonstrates the most accurate and robust classification performance relative to conventional data-scaling methods, regardless of the types of classification models. [ABSTRACT FROM AUTHOR]

■ Fault detection, classification and location based on traveling wave. ■ Lightning and fault classification with current traveling wave. ■ Ultra high speed protection of double circuit transmission line using wavelet transform. ■ Very fast algorithm for fault classification of parallel transmission line with single phase tripping. ■ Traveling wave protection of transmission line. The injection or absorption of reactive power by the static synchronous compensator (STATCOM) may lead to the misoperation of protective relays and single-phase-tripping systems that operate based on impedance measurement. Also, high-frequency harmonic components caused by switching the internal circuitry of a STATCOM lead to unintended tripping of relays that work based on traveling waves. This paper proposes a single-terminal approach to detect, locate, and identify the faulty line(s) and phase(s) in parallel transmission lines equipped with STATCOMs using traveling waves. To that end, these processes were designed so that adding a STATCOM or different operating modes does not degrade their efficiency and accuracy. The faults and faulty phases were simultaneously detected using the first set of traveling waves received at the sending end of the parallel lines at a sampling frequency of 200 kHz. Furthermore, a sampling frequency of 1 MHz was used briefly to improve the fault location accuracy. The discrete wavelet transform (with BD4 mother wavelet) was adopted to extract the traveling waves. All simulations and analyses were conducted in the PSCAD environment. The results obtained from numerous simulations at different places and states of the networks verified the high accuracy and validity of the proposed methods. [ABSTRACT FROM AUTHOR]

Since industrial process data often presents strong correlations, high complexity, and nonlinear patterns, a proficient deep learning model is required for the fault classification task. Recent researches have shown that deep learning models like stacked autoencoder (SAE) are able to learn deep abstract features from complex process data. Nevertheless, a traditional SAE cannot extract the informative fault-relevant features and data distribution features from industrial process data, which are necessary for effective fault classification in industrial processes. Thus, this study proposes a semi-supervised Inter-Relational Mahalanobis SAE (IRM-SAE) model to learn inter-relational distribution and fault-relevant dynamic features of process data for fault classification. First, the Inter-Relational Mahalanobis loss is introduced into the original objective function of SAE to learn meaningful inter-relational distribution features within the data. Then, an active time frame technique is developed to preprocess the input data to capture the dynamic features of the data. Furthermore, to fully utilize both labeled and unlabeled data in industrial processes, the semi-supervised strategy is introduced to learn fault-related features for better fault classification. To validate the performance of the proposed model, it is applied on the Tennessee–Eastman process and a real-world industrial hydrocracking process. The experimental results show that the proposed model has higher fault classification performance compared to other deep learning models. • A novel semi-supervised Inter-Relational Mahalanobis SAE (IRM-SAE) model is proposed to learn inter-relational distribution and fault-relevant dynamic features of process data. • An active time frame technique is developed to preprocess the process data to capture the dynamic features of the data. • To fully utilize both labeled and unlabeled data in industrial processes, the semi-supervised strategy is introduced to learn fault-related features for better fault classification. • The experimental results on two industrial processes show that the proposed model has higher fault classification performance compared to other deep learning models. [ABSTRACT FROM AUTHOR]

As substantial parts of process monitoring, fault classification techniques have been widely utilized in modern industries. However, most methods can only perform well under specific condition, which indicates that it is always difficult to ensure the classification efficiency for complex industrial processes using only one method. In this paper a weighted decision fusion system is proposed where an extended analytic hierarchy process (EAHP) structure is designed to give a comprehensive explanation for the weights assigned to the original fault classification results. Firstly, the fault category information is embedded in the EAHP structure, which makes it possible to consider both fault-wise and classifier-wise information in the fault classification. Secondly, an overall priority (OP) matrix is proposed to provide full prior knowledge for all classifiers. Different from previous researches, traditional OP vectors are replaced by the new-designed OP matrix which can contain more information about fault category. Thirdly, another confidence matrix is carried out to update the classification results. Compared with previous state-of-art, the confidence matrix can be adjusted according to the specific original classification result Finally, the effectiveness of the proposed method is verified by a numerical example and the Tennessee Eastman process (TEP) where the proposed decision fusion system shows superior fault classification results in both experiments. [ABSTRACT FROM AUTHOR]

• A new deep learning method called Sparse Probabilistic Dynamic Network (SPDN) is proposed, which is based on Gaussian-Bernoulli Restricted Boltzmann Machine and the Long-Short Term Memory. • The proposed method takes into account the nonlinear and dynamic characteristics in process data, and deeply extracts the potential information in it. • The proposed method can obviously improve the classification accuracy in the case study. The dynamic and nonlinear characteristics of process data have become the major issue in data-driven process monitoring. Traditional data-driven methods are often only able to extract a single feature in process data. Therefore, how to effectively extract multi-dimensional features has become the focus of current research. Sparse probabilistic dynamic network (SPDN) is a deep learning model proposed in this paper for the purpose of fault classification. The method mainly takes the advantages of the sparse Gaussian-Bernoulli Restricted Boltzmann Machine (GRBM) and the recurrent neural network (RNN) with long-short term memory (LSTM) units. First, the sparse GRBM is used for nonlinear feature extraction in an unsupervised way. Then, LSTM is introduced to realize the modeling of sequence data which can effectively handle the dynamic feature of the data. In the Tennessee-Eastman benchmark process, the classification accuracies of the proposed method are proved to be far superior to MLP, RNN and PDN. Meanwhile, in order to prove the influence of the data dynamics and the internal parameters of the structure on the fault classification results, two additional experiments were carried out. [Display omitted] [ABSTRACT FROM AUTHOR]

Diagnosis aims at predicting the health status of components and systems. In photovoltaic systems, it is vital to guarantee energy production and extend the useful life of photovoltaic power plants. Multiple prediction and classification algorithms have been proposed for this purpose in the literature. The accuracy of these algorithms depends directly on the quality of the data and the features with which they are tuned or trained. In this paper, an innovative approach for predicting the health status of photovoltaic systems is proposed, which includes a feature selection stage. This approach first discriminates severely affected photovoltaic panels using basic electrical features. In a second step, it discriminates the other faulty panels using more elaborated time–frequency features and selecting the most relevant features through correlation and variance analysis. Finally, the approach predicts the health status of photovoltaic panels using a nonlinear regression method named partial least squares. This later is then combined with linear discriminant analysis and compared. The approach is validated with real current data from a photovoltaic plant composed of twelve photovoltaic panels with power between 205 and 240 Wp in three health states, namely broken glass, healthy, and big snail trails. The results obtained show that the proposed approach efficiently predicts the three health states. It determines the level of degradation of the panels, which indicates priorities to corrective and predictive maintenance actions. Furthermore, it is cost-effective since it uses only electrical measurements that are already available in standard photovoltaic data acquisition systems. Above all, the approach is generic and it can be easily extrapolated to other diagnosis problems in other domains. [ABSTRACT FROM AUTHOR]

• Review of fault location and classification methods in distribution grids. • The review includes methods developed for low-voltage grids. • The consideration of distributed generation in the available methods is studied. • The research trends of the fault diagnosis methods are presented. • Identification of research gaps and future challenges in the field of fault isolation. The evolution of the conventional power systems to smart grids has changed the way to conceive and operate them. The part of the grid evolving the most is the distribution grid where the installation of additional sensors and actuators has increased its observability and controllability. These have enabled the development of more accurate and automated processes including some critical ones such as the fault detection, isolation and restoration techniques. In this direction, unconventional methods, e.g. artificial intelligence, have been increasing in popularity over the last years. In this paper, fault location and fault classification methods are reviewed for both medium–voltage and the until recently unexplored case of low–voltage distribution grids. Different methods applied for both fault location and fault classification are being classified by the implemented technique. Such methods are explained and analyzed providing the main advantages and disadvantages of each category. Additionally, the research trends in both fields are analyzed and state–of–the–art methods from each category are thoroughly compared. Finally, the research gaps are identified. [ABSTRACT FROM AUTHOR]

Attention Mechanism has become very popular in the field of mechanical fault diagnosis in recent years and has become an important technique for scholars to study and apply. The introduction of Attention Mechanism can help models achieve efficient resource allocation, improve the remote information capture capability of models, and significantly improve the performance of models for various equipment health management tasks (fault classification, life prediction, etc.) The application of Attention Mechanism in machinery has achieved fruitful research results, but there is a lack of related reviews. In order to facilitate later scholars to quickly grasp the Attention Mechanism and select the appropriate technique, this paper reviews the relevant research and applications of Attention Mechanism in Intelligent Fault Diagnosis of Machinery. Based on the methods proposed in the collected literature, this paper classifies and analyzes them from multiple perspectives to help readers grasp the development status and trends in this field. We divide the collected technologies into three categories: Recurrent-based, Convolution-based, and Self-attention-based. We describe each attention technique and its application scenarios in detail. Finally, we summarize the advantages and disadvantages of various AM techniques, and further discuss the possible future directions of attention mechanisms in the mechanistic field. The purpose of this paper is to provide a comprehensive reference for researchers and to help them find further research directions. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) power generation is one of the remarkable energy types to provide clean and sustainable energy. Therefore, rapid fault detection and classification of PV modules can help to increase the reliability of the PV systems and reduce operating costs. In this study, an efficient PV fault detection method is proposed to classify different types of PV module anomalies using thermographic images. The proposed method is designed as a multi-scale convolutional neural network (CNN) with three branches based on the transfer learning strategy. The convolutional branches include multi-scale kernels with levels of visual perception and utilize pre-trained knowledge of the transferred network to improve the representation capability of the network. To overcome the imbalanced class distribution of the raw dataset, the oversampling technique is performed with the offline augmentation method, and the network performance is increased. In the experiments, 11 types of PV module faults such as cracking, diode, hot spot, offline module, and other classes are utilized. The average accuracy is obtained as 97.32% for fault detection and 93.51% for 11 anomaly types. The experimental results indicate that the proposed method gives higher classification accuracy and robustness in PV panel faults and outperforms the other deep learning methods and existing studies. • The Multi-scale network efficiently classifies photovoltaic panel anomalies. • Oversampling approach overcomes the imbalanced class distribution. • Multi-scale branches aim to improve the features extracted by each parallel block. • Proposed method is validated with a large dataset collected from six continents. [ABSTRACT FROM AUTHOR]

Faults in transmission lines can cause several problems to transmission utilities. An adequate diagnosis of such events can improve service restoration time and, consequently, asset availability. This work proposes a machine learning-based approach to diagnose transmission line faults. The oscillography files of the fault case are processed and applied to four different classifiers, which must identify: phases involved, electrical nature, impedance level, and, fault cause. The method, which is inherently robust regarding signal noise and lack of synchronization between relays, combines Decision Trees, feature engineering, and optimization to perform the fault diagnosis based only on relay measures. It was tested in two fault data sets, one containing 24,000 faults generated using a Real Time Digital Simulator and another one with 46 real faults. The results obtained were very promising, with accuracy values close to 100% for involved phase and fault nature, 90% for impedance level, and 80% for fault cause. [Display omitted] • A ML approach is proposed for comprehensive fault diagnosis in transmission lines. • Fault phases, nature, impedance, and cause are inferred from oscillographs. • The approach is very robust regarding noise and lack of synchronism between relays. • It relies on very few adjustable parameters. Most internal parameters are optimized. • Results on RTDS simulated and real fault cases are provided. [ABSTRACT FROM AUTHOR]

Transformer protection is an established area of research to find the fastest and efficient differential relay algorithm that isolates the transformer from remaining system causing least damage. Algorithm should also avoid mal-operation when differentiating between the operating conditions. Various differential algorithms were proposed in the past, allowing a scope for further research. In this paper, ANN is used as a pattern classifier which discriminates among normal, magnetizing inrush, over-excitation and internal fault currents in a power transformer. The proposed scheme has been realized through different ANN architectures including a new customized parallel-hidden layered design, which originates to be more accurate in differentiating between the normal wave and faulty wave despite the shape similarity. A combination of two ANNs in Master–Slave mode has also been discussed. Back Propagation (BP) and Genetic Algorithm (GA) are used to train the multi-layered feed forward neural network and their simulated results are compared. The neural network trained by GA gives more accurate results (in terms of mean square error) than by BP Algorithm. Simulated data are used as an input to the ANN to verify the accuracy of the algorithm. Thus, GA trained Master–Slave ANN based differential protection scheme provides faster, accurate, more secured and dependable relay for power transformers. [ABSTRACT FROM AUTHOR]

The paper presents the construction and application of multiple classifiers to increase the accuracy of the fault detection module in the diagnostic task. The structure of the ensemble of classifiers is presented and the applied voting mechanisms explained. Methods of storing knowledge in the intelligent diagnostic systems are introduced and their taxonomy provided. Next, the selected algorithms implemented in the fault detection operation are briefly described. Problems with the practical implementation of the proposed solution are considered. The scheme is used to detect faults in the analog part of the MEMS accelerometer. The paper is concluded with the possible prospects for the proposed scheme. [ABSTRACT FROM AUTHOR]

This paper proposes fault detection and classification scheme for transmission line protection using WT and linear discriminant analysis (LDA). Current signals of each phase are used for the detection and identification of faulty phases and zero sequence currents are used for the detection of ground. Current signals are processed using discrete wavelet transform with DB-4 wavelet up to level 3. Approximate coefficients are reconstructed using wavelet reconstruction. Performance of the proposed based scheme is tested by variations of parameters such as fault type, location, fault resistance, fault inception angle and power flow angle. The scheme is applicable for both single circuit and double circuit transmission line. All shunt faults and multi-location faults which occur in different locations at the same time are also detected and classified by the proposed scheme within one cycle time. The simulation results show that the proposed scheme is not affected by non-linear high impedance fault and CT saturation. [ABSTRACT FROM AUTHOR]

This work considers the application of classification algorithms for data-driven fault diagnosis of batch processes. A novel data selection methodology is proposed which enables online classification of detected disturbances without requiring the estimation of unknown (future) process behavior, as is the case in previously reported approaches. The proposed method is benchmarked in two case studies using the Pensim process model of Birol et al. (2002) implemented in RAYMOND . Both a simple k Nearest Neighbors ( k -NN) and complex Least Squares Support Vector Machine (LS-SVM) are employed for classification to demonstrate the generic nature of the proposed approach. In addition, the influence of different data pretreatment methods on the classification performance is discussed, together with a motivation for selecting the correct pretreatment steps. Finally, the influence of the number of available training batches is studied. The results demonstrate that a good classification performance can be achieved with the proposed data selection method even with a low number of faulty training batches by exploiting knowledge on the nature of the to-be-diagnosed faults in the data pretreatment. This provides a proof of concept for classification-based batch diagnosis and demonstrates the importance of incorporating process insight in the construction of data-driven process monitoring and diagnosis tools. [ABSTRACT FROM AUTHOR]

Faults in a rotor-bearing system due to bearings and unbalance have been classified using support vector machines (SVMs). Vibration signals on a rotor-bearing system were measured simultaneously at five different rotating speeds using seven transducers. The most sensitive feature of the vibration signals has been determined using compensation distance evaluation technique. Multi-class SVMs classification algorithm was then implemented for classification of the faults by considering SVMs created by the possible combinations of the most two sensitive features for each type of fault. By using optimal SVM parameters, the effective location of transducer among seven transducers for best classification of the faults has been investigated and found that any transducer provides a classification of 75% or better and this classification rate increases when more transducers are considered. This paper provides a robust SVM based technique using only time domain data without any additional preprocessing for classifying bearing and unbalance faults. [ABSTRACT FROM AUTHOR]

While Fisher discriminant analysis (FDA) has been widely used for classification, it highly relies on the label information of the training data, which means that its classification performance cannot be guaranteed if there are only a small number of labeled data samples available for use. For fault classification in industrial processes, unfortunately, there are always a much smaller number of faulty samples compared to the normal samples. In addition, even if the collected faulty samples are enough for modeling, it will still need expert experiences and prior process knowledge to label them into different types, which is time-consuming and costly. In this paper, a semi-supervised form of the FDA model is proposed and used for fault classification in industrial processes. The named semi-supervised FDA bridges the superior class separability of FDA and the unsupervised nature of principal component analysis (PCA). With the incorporation of additional unlabeled data samples for modeling, the fault classification performance has been greatly improved by the new model. Both of the semi-supervised modeling efficiency and the fault classification performance are evaluated through two case studies. [ABSTRACT FROM AUTHOR]

This article deals with automated fault classification of reciprocating compressors from vibration data. The genetic algorithm was applied to automate the process. A total of 15 fault cases based on practical observation of the machine faults was considered. Vibration data for the various fault cases were collected and processed using the time-frequency analysis, namely the short time Fourier transform (STFT), the smoothed pseudo Wigner-Ville distribution (SPWVD), and the reassigned smoothed pseudo Wigner-Ville distribution (RSPWVD), due to the non-stationary vibration characteristics of the system analyzed. The fault features for the formidable amount of time-frequency data were extracted first and fed into an artificial neural network for fault classification. It is demonstrated in this work that it is feasible to apply the genetic algorithm to automate the fault classification process and thereby minimize the requirement for intervention from the human experts. [ABSTRACT FROM AUTHOR]

Highlights: [•] Rough membership neural network (RMNN) approach is proposed for fault classification in transmission lines. [•] Wavelet multi-resolution analysis (MRA) is utilized for selecting time domain features and time-frequency domain features. [•] Rough neurons are used as input layer neurons and fuzzy neurons in hidden and output layer of the RMNNs. [•] The effectiveness and accuracy of proposed approach is analyzed, compared with the back-propagation neural network (BPNN). [Copyright &y& Elsevier]

• Various types of faults are classified by the proposed optimal intelligent method. • Wavelet packet transform is used successfully to extracts the set of features. • A ELM classifier based fault detection method is proposed for binary classification of fault and non-fault conditions. • Equilibrium optimizer algorithm is applied to enhance the ELM classifier performance. A novel intelligent scheme using the wavelet packet transform (WPT) and extreme learning machine (ELM) is proposed for fault event classification in the grid-connected photovoltaic (PV) system. The WPT is applied for preprocessing the cycle of the post-fault voltage samples at the point of common coupling (PCC) measurement to get the normalized logarithmic energy entropy (NLEE). The ELM is applied to classify the different fault cases. To enhance the performance of ELM for faults classification, a hybrid optimization mechanism based on an equilibrium optimization algorithm (EOA) is proposed to optimize the selection of input feature subset and the number of ELM hidden nodes. Furthermore, to evaluate the proposed scheme's performance, a comprehensive evaluation was conducted on a 250 kW grid-connected photovoltaic system. From simulation, the classification accuracy is recorded to be 100% under the no-noise condition, while at the signal-to-noise ratios (SNR) of 30, 35, and 40 dB, the accuracies are 98.96, 99.04, and 99.36%, respectively. Moreover, the practical performance of the EOA-ELM classifier is validated using IEEE 34 bus system. The obtained results validate the effectiveness of the proposed scheme in terms of robustness against measurement noise, computation time, and detection accuracy. [ABSTRACT FROM AUTHOR]

• A novel accommodative random weighted fusion algorithm is proposed. • More feature information can be retained by using BB-1D-TP feature extraction. • A new gear fault diagnosis method is constructed with high accuracy. As an essential component of a gearbox, gears can damage a structure or even an entire gear transmission system in case of failures. As a result, advanced fault diagnosis methods are crucial to system's operation. Currently, single-signal-driven gear fault diagnosis techniques have been applied in many fields, but multipath noise and single-sensor sampling errors inevitably affected the accuracy of diagnosis. This paper proposes a gear fault diagnosis method based on a novel accommodative random weighting theory and a balanced binary one dimension ternary pattern (BB-1D-TP) model. It can accurately diagnose the types of gear failures under the circumstances of multiple channels and strong background noise. The novel accommodative random weighting algorithm reduces the total mean-square error (MSE) by adaptively adjusting the proportional connection between a measured value at a present state and a historical state. Then the balanced binary algorithm extracts texture features of fault signals for signal enhancement. In the end, the classification is done by using Support Vector Machine (SVM) method. The result of experiments demonstrated that the method in this article effectively improves accuracy and efficiency of gear fault identification. [ABSTRACT FROM AUTHOR]

• Fault detection and classification based on traveling wave • Lightning and fault classification with current traveling wave • Ultra high speed protection of double circuit transmission line using wavelet transform • Very fast algorithm for fault classification of parallel transmission line with single phase tripping This paper presents a new and very fast fault detection (FD), fault classification (FC), and fault phase selection (FPS) method, which is just based on the first group of current traveling waves (cTW) in double circuit transmission lines (DCTLs). All internal, inter-circuit faults and lightning strikes on transmission line (TL) phases and shield wires are reviewed. By the proposed algorithm, FC and FPS are performed in the shortest possible time. In this study, wavelet transform (WT) is used to extract the traveling wave (TW) caused by the fault from the current signal of one DCTL terminal, and only the first group of these waves is used for FC and PFS to increase velocity. To classify various fault types, the zero components of TWs of the two lines, which can be calculated using the conventional modal transforms and another modal transform of 6*6 dimensions defined on all six phases of the DCTL, is used. All simulations are performed in PSCAD on a 230kV grid and a DCTL of 200km length. Also, the performance of the proposed algorithm is tested under various fault and lightning conditions in the presence of white noise with different incidence angles and various fault resistances. [Display omitted]. [ABSTRACT FROM AUTHOR]

• Propose a stacking based data-driven model, to identify the fault type and location in a distribution system, including PV units and their stochastic nature. • All types of faults including faulty phase or phases, along with the no fault conditions (NF) are classified in three-phase test distribution systems with superior performance. • Detect low and high impedance faults. • Develop a straightforward yet accurate approach which requires only voltage measurements in some buses to identify and determine the type and location of the fault (without the need to further preprocessing stage of the input data). • Enhance the accuracy of the predictions comparing to individual machine learning models. This paper proposes an ensemble learning-based method to determine the type and location of faults in a smart distribution network. The employed scheme is a stacking based method that consists of two main levels, each of which includes one or more independent classifiers. In the structure of the proposed method for first level, multiclass support vector machine, K-nearest neighbors and random forest models are exploited. Also, for the next (final) level, which is also called meta-classifier, a random forest based model has been used, which itself is a kind of ensemble learning method. In order to supply the data used to feed the model, the voltage and current measurements in some buses and lines have been used. The proposed model is tested using the data related to all different types and location of faults and operating conditions in the IEEE 123-bus network. Besides, the model is implemented on another distribution test system to consider wider range and specs for fault parameters and also to comprise with other methods applied to it; high impedance faults are also regarded in this part. The obtained results show the appropriate accuracy of the proposed model and its better performance than other considered methods. [ABSTRACT FROM AUTHOR]

Highlights: [•] A new method for multi-damage extraction and classification of gear system is proposed. [•] Using PCA and KPCA to extracte the signal feature of gear system. [•] Five kinds of fault signal are analyzed under 300rpm, 900rpm, 1200rpm and 1500rpm. [•] Proposed method can be used to identify various faults (multi-fault) and damage level. [Copyright &y& Elsevier]

Highlights: [•] We present a fault diagnosis expert system for newly assembled auto transmission. [•] Synchronous data acquisition algorithm of the vibration signal and the speed signal. [•] Improved FFT algorithm which is used for spectral transformation after a lot of tests. [•] Knowledge representation and knowledge renew algorithm for transmission. [•] Improved genetic algorithm for feature selection and fault classification with BP network. [Copyright &y& Elsevier]

Abstract: This article studies if machine faults can be effectively determined in a reduced dimensional space. When faults occur in machines, machine vibration signals will deviate from its normal signal pattern. Such changes can be reflected in the features constructed from the machine signals. In this article, 13-dimension feature data set is constructed to represent different health conditions of machines, and unsupervised learning algorithms are introduced to deal with feature data sets for feature extraction and fault classification. A weighted local and global regressive mapping (WLGRM) algorithm is proposed for machine fault classification. Two synthetic fault data sets and two experimental data sets are employed to validate the effectiveness of the proposed approach. Comparative analysis with other unsupervised learning algorithms, such as local and global regressive mapping, locality preserving projection, Isomap, principal component analysis, and Sammon mapping, are reported. The results show that different machine faults can be classified, the degree of fault severity can be captured, and WLGRM can achieve better performance than other algorithms in most cases of machine fault classification. [Copyright &y& Elsevier]