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Transmission line fault classification forms the basis of fault protection management in power systems. Because faults have adverse effects on transmission lines, adequate measures must be implemented to avoid power outages. This paper focuses on using the categorical boosting (CatBoost) algorithm classifier to analyse and train multiple voltage and current data from a 330 kV and 500 km-long simulated faulty transmission line model designed using Matlab/Simulink. From it, 93,340 fault data sizes were extracted. The CatBoost classifier was employed to classify the faults after different machine learning algorithms were used to train the same data with different parameters. The trainer achieved the best accuracy of 99.54%, with an error of 0.46% for 748 iterations out of 1000. The algorithm was selected for its high performance in classifying faults based on accuracy, precision and speed. In addition, it is easy to use and handles multiple data-sets. In contrast, a support vector machine and an artificial neural network each has a longer training time than the proposed method’s 58.5 s. Proper fault classification techniques assist in the effective fault management and planning of power system control thereby preventing energy waste and providing high performance.

Made available in DSpace on 2022-05-01T08:15:10Z (GMT). No. of bitstreams: 0 Previous issue date: 2021-11-01 This paper presents a tool for the detection and classification of short-circuits in electric power distribution systems, which is based on the combined use of multi-resolution analysis and Fuzzy-ARTMAP neural network. The multiresolution analysis allows the identification of singularities in the waveforms and the ART family network guarantees to the classifier the ability to continuously learn without losing the previously acquired knowledge. The entire diagnosis procedure is performed in a single step, reducing the computational effort. The efficiency of the system is verified by a direct accuracy analysis, and by a comparison between the Fuzzy-ARTMAP and the EuclideanARTMAP. Results show that the system is efficient and robust, being able to detect and classify 100% of the electrical faults considering the test that were performed. Instituto Federal de Educacao Ciencia e Tecnologia de Sao Paulo, Sao Paulo Universidade Estadual Paulista, Sao Paulo Universidade Estadual Paulista, Sao Paulo

Today, the classification and location of faults in electrical networks remains a topic of great interest. Faults are a major issue, mainly due to the time spent to detect, locate, and repair the cause of the fault. To reduce time and associated costs, automatic fault classification and location is gaining great interest. State-of-the-art techniques to classify and locate faults are mainly based on line-impedance measurements or the detection of the traveling wave produced by the event caused by the fault itself. In contrast, this paper describes the methodology for creating a database and a model for a complex distribution network. Both objectives are covered under the paradigm of the time-domain pulse reflectometry (TDR) principle. By using this technique, large distances can be monitored on a line with a single device. Thus, in this way a database is shared and created from the results of simulations of a real and complex distribution network modeled in the PSCADTM software, which have been validated with measurements from an experimental test setup. Experimental validations have shown that the combination of the TDR technique with the modeling of a real network (including the real injector and the network coupling filter from the prototype) provides high-quality signals that are very similar and reliable to the real ones. In this sense, it is intended firstly that this model and its corresponding data will serve as a basis for further processing by any of the existing state-of-the-art techniques. And secondly, to become a valid alternative to the already well-known Test Feeders but adapted to work groups not used to the electrical world but to the environment of pure data processing.

When using the classification algorithm to classify a single sample, the classification accuracy often cannot achieve an ideal effect. To solve this problem, the following two aspects of research work are carried out and presented in this paper. On the one hand, according to the memory characteristics of mechanical faults, a voting classification mechanism for the sample sequence to be classified is proposed. It is found that the classification mechanism of the sample sequence to be classified with memory has the Matthew effect of accumulated advantage. Using this effect, one can improve the accuracy of fault classification. On the other hand, because the length of the sample sequence to be classified increases, the delay of the classification results increases. To solve this problem, the classification algorithm is optimized to minimize the delay on the assumption that the classification accuracy meets the expected requirements.

Automatic detection of solar array faults reduces maintenance costs and increases efficiency. In this paper, we address the problem of fault detection, localization, and classification in utility-scale photovoltaic (PV) arrays using machine learning methods. More specifically, we develop a series of customized neural networks for detection and classification of solar array faults. We evaluate fault detection and classification using metrics such as accuracy, confusion matrices, and the Risk Priority Number (RPN). We examine and assess the use of customized neural networks with dropout regularizers. We develop and evaluate neural network pruning strategies and illustrate the trade-off between fault classification model accuracy and algorithm complexity. Our approach promises to elevate the performance and robustness of PV arrays and compares favorably against existing methods.

Aviation control software has become the core control decision-making unit of the aviation system. The Boolean conditional expressions are the main parts of the branch and loop control logic of aviation control software. This paper studies the fault classification and repair method of conditional expression of aviation control software. 1) a two-level Boolean conditional expression fault classification method is proposed; 2) based on the design of mutation operator, repair solution and stop condition of mutation repair, an automatic repair method of conditional expression is proposed; 3) a repair assistant tool for conditional expression is designed and implemented, and 155 fault expressions are repaired. The results show that 55.5% of fault expressions can achieve accurate repair, 44.5% of fault expressions can generate multiple correct versions after a repair, and some versions have operation redundancy, so it is necessary to select the optimal version manually.

Accurately evaluating the fault type and location is important for ensuring the reliability of the power distribution network. A mushrooming number of distributed generations (DGs) connected to the distribution system brings challenges to traditional fault classification and location methods. Novel AI-based methods are mostly based on wide area measurement with the assistance of intelligent devices, whose economic cost is somewhat high. This paper develops a super-resolution (SR) and graph neural network (GNN) based method for fault classification and location in the power distribution network. It can accurately evaluate the fault type and location only by obtaining the measurements of some key buses in the distribution network, which reduces the construction cost of the distribution system. The IEEE 37 Bus system is used for testing the proposed method and verifying its effectiveness. In addition, further experiments show that the proposed method has a certain anti-noise capability and is robust to fault resistance change, distribution network reconfiguration, and distributed power access.

The existing motor fault classification methods mostly use sensors to detect a single fault feature, which makes it difficult to ensure high diagnostic accuracy. In this paper, a motor fault classification method based on multi-source information fusion Naive Bayes classification algorithm is proposed. Firstly, this paper introduces the concept and advantages of multi-source information fusion, as well as its problems of miscellaneous information and inconsistent data magnitude. For example, as this paper classifies the fault of generators, there are many physical quantities, such as voltage, current and temperature, which are not in the same dimension, therefore it is difficult to fuse. Then, aiming at the corresponding problems, this paper uses a PCA dimension reduction method to remove redundant information and reduce the dimension of multi-dimensional complex information. Aiming at the problem of unequal data magnitude, the interval mapping method is adopted to effectively solve the misjudgment caused by unequal data magnitude. After the initial multi-source information processing, the classical Naive Bayes classification algorithm is used for fault classification, and the algorithm diagnosis and verification are carried out according to the statistical fault data. Use of the algorithm increases accuracy to more than 97%.

In this paper, a fault protection diagnostic scheme for a power distribution system is proposed. The scheme comprises a wavelet packet decomposition (WPD) for signal processing and analysis and a support vector machine (SMV) for fault classification and location. The scheme is tested on a reduced Eskom 132 kV power line. The WPD is used to extract fault signatures of interest and the SVM is subsequently used for fault classification and locating various fault conditions. Furthermore, we investigate the effectiveness of the SVM scheme using different samples of the cycles for fault classification and location. The results show that the fault classification and location on a distribution line can be determined rapidly and efficiently irrespective of the fault impedance and incipient angle with minimum estimation error. Lastly, the proposed scheme is tested on a grid-integrated system with renewable energy sources.

Predictive maintenance helps organizations to reduce equipment downtime, optimize maintenance schedules, and enhance operational efficiency. By leveraging machine learning algorithms to predict when equipment failure will likely occur, maintenance teams can proactively schedule maintenance activities and prevent unexpected breakdowns. Anomaly detection and fault classification are essential components of predictive maintenance. Anomaly detection involves analyzing sensor data collected from equipment to identify deviations from normal behavior. Fault classification, on the other hand, involves identifying the root cause of a fault or failure. A dataset of an industrial asset is used to evaluate the proposed study. Four distinct data-driven anomaly detection methodologies were employed after the pre-processing of the data, with the deep learning-based autoencoder producing the best results of all the techniques. Implementing machine learning-based fault categorization approaches revealed that Random Forest had the best results. Bayesian optimization and sequential model-based hyperparameter optimization technique is used for greater accuracy and optimized hyperparameters. Significant progress has been made in anomaly detection and fault classification using machine learning, but the degree of their explainability is significantly limited by the ``black-box" character of some machine learning techniques. Less emphasis has been placed on explainable artificial intelligence (XAI) approaches in the domain of maintenance. Therefore, the XAI tools have been used to acknowledge the extent of the variables to analyze the influence of respective features. A stability metric has been included to improve the explanation's overall quality. The findings of this article suggest that the utilization of eXplainable Artificial Intelligence (XAI) can offer significant contributions in terms of insights and solutions for addressing critical maintenance issues. As a result, decision-making processes can become more informed and effective.

This paper proposes a modified relevance vector machine with slow feature analysis fault classification for industrial processes. Traditional support vector machine classification does not work well when there are insufficient training samples. A relevance vector machine, which is a Bayesian learning-based probabilistic sparse model, is developed to determine the probabilistic prediction and sparse solutions for the fault category. This approach has the benefits of good generalization ability and robustness to small training samples. To maximize the dynamic separability between classes and reduce the computational complexity, slow feature analysis is used to extract the inner dynamic features and reduce the dimension. Experiments comparing the proposed method, relevance vector machine and support vector machine classification are performed using the Tennessee Eastman process. For all faults, relevance vector machine has a classification rate of 39%, while the proposed algorithm has an overall classification rate of 76.1%. This shows the efficiency and advantages of the proposed method.

International audience; This paper addresses the problem of Fault Classification and Prediction (FCP) without sufficient data in aircraft maintenance. An innovative approach based on Agent-Based Simulation System (ABSS) and the Fuzzy-Rough Nearest Neighbour (FRNN) method is proposed. To do so, a framework for integrating the FRNN approach into ABSS is firstly provided. The concept and architecture models of FRNN-ABSS are then used to describe the FRNN-ABSS system. Subsequently, random and sequence strategies are designed for the FCP of the engine. An algorithm for integrating the FRNN method into ABSS is also developed to automate FCP. Finally, the experiments analysing the impact of different strategies on maintenance costs and service level have been conducted. The results show that the approach proposed has achieved success for random and sequence strategies: 9.3% and 2.5% of maintenance costs have been saved; 4.17% and 12.5% of delayed flights have been changed into on-time flights.

This paper is devoted to develop interest of power system engineers in learning basic concepts of image processing and consequently using deep networks to solve problems of complex power system networks. To this end, we study fault classification in a power system through automation of equal area (EAC) criterion. By considering EAC graphs as images and using classical image processing techniques, we successfully distinguish between different transient conditions including sudden change of input power as well as short circuit at the sending end and middle points of a single and double circuit transmission lines. In addition to classification, some parameters are also determined from EAC images such as initial rotor angle, clearing angle, and maximum rotor angle. Further, the use of deep networks is introduced to perform the same task of fault classification and a comparison is drawn with multilayer perceptron neural networks. Developed algorithms are tested in MATLAB as well as Pytorch environments.

Background: Through the analysis of the relevant data of industrial equipment, faults diagnosis is helpful for system maintenance and reducing economic losses. Objective: The aim is to reduce the influence of irrelevant features and efficiently train the FIR-XgBoost model. Methods: An Extreme Gradient Boosting (XgBoost) approach based on feature importance ranking (FIR) is proposed in this article for fault classification of high- dimensional complex industrial systems. Gini index is applied to rank the importance of the features, and feature selection is implemented based on their position in the ranking. Results: The dataset from the PHM 2021 data challenge, which is related to the process of fuse thermal imaging, is used. The classification accuracy of FIR-XgBoost reaches 99.63%, outperforming other existing algorithms. A case study is presented to show that excellent fault classification can be achieved through ensemble learning and feature selection. Conclusion: Data-driven machine learning methods are proposed for solving high-dimensional fault classification problems on the dataset of the PHM2021 Data Challenge. An FIR-XgBoost method is proposed, the core of which is to retain important features and to reduce redundancy of sensor data. Consequently, feature selection based on FIR has better interpretability than other algorithms. Furthermore, the FIR- XgBoost algorithm retaining the 50 most important features achieves the best fault classification performance among the compared algorithms and can be implemented in specific industrial processes.

The objectives are to explore the effect of a random forest algorithm on the state prediction and fault classification of smart meters, so that the smart meters can run more stably. Based on the principle of the random forest algorithm and Light Gradient Boosting Machine (LightGBM) algorithm, its theoretical basis and application are deeply analyzed and improved. An improved fault classification and state prediction model of smart meters is designed based on a random forest-improved LightGBM algorithm. The built model algorithm is evaluated by utilizing public data sets. The results show that, by preprocessing the fault data set of smart meters, 8 fault feature types including fault type, working time, and fault month are obtained. When the improved LightGBM algorithm is trained based on random forest, the average accuracy of the algorithm is 67.65%, the average recall rate is 64.11%, and the average F1 value is 65.73%. Meanwhile, the difference between the algorithm and the random forest algorithm and the Correlation-based Feature Selection (CFS) algorithm is studied. Therefore, the prediction accuracy and fault classification of the constructed model algorithm for smart meters are higher than those of the other two algorithms. It indicates that the algorithm has a good application effect and high practical application value and can provide a scientific and useful reference for the follow-up research of smart meters.

The microgrid (MG) networks require adaptive and rapid fault classification mechanisms due to their insufficient kinetic energy reserve and dynamic response of power electronic converters of distributed generation (DG) systems. To achieve this requirement, this study explores the issues in standalone (SA) and grid-connected (GC) operating modes of MG and develops a near-real-time intelligent disturbance detection and protective solutions for their stable operation. In the proposed approach, an intelligent fault classification mechanism is developed using the advantages of wavelet transform and convolutional neural networks (CNNs). Initially, the voltage and current outcomes for each and every possible fault in the MG network are identified and the wavelet transforms are applied for preprocessing and image conversion. The converted images are identified as scalograms which are further trained with the CNNs. To assess the development of the proposed approach, the IEEE 13 bus system is considered for data gathering. To replicate the real-time behavior of the MG network, the additive white Gaussian noise (AWGN) and additive impulsive Gaussian noise (AIGN) are injected at various levels during the classifier development process. The trained classifier has an average training accuracy of 99.1% for SA MG and 97.7% for GC MG, and the average testing accuracies are 98.9% for SA MG and 97.1% for GC MG.

Fault diagnosis in photovoltaic (PV) arrays is essential in enhancing power output as well as the useful life span of a PV system. Severe faults such as Partial Shading (PS) and high impedance faults, low location mismatch, and the presence of Maximum Power Point Tracking (MPPT) make fault detection challenging in harsh environmental conditions. In this regard, there have been several attempts made by various researchers to identify PV array faults. However, most of the previous work has focused on fault detection and classification in only a few faulty scenarios. This paper presents a novel approach that utilizes deep two-dimensional (2-D) Convolutional Neural Networks (CNN) to extract features from 2-D scalograms generated from PV system data in order to effectively detect and classify PV system faults. An in-depth quantitative evaluation of the proposed approach is presented and compared with previous classification methods for PV array faults - both classical machine learning based and deep learning based. Unlike contemporary work, five different faulty cases (including faults in PS - on which no work has been done before in the machine learning domain) have been considered in our study, along with the incorporation of MPPT. We generate a consistent dataset over which to compare ours and previous approaches, to make for the first (to the best of our knowledge) comprehensive and meaningful comparative evaluation of fault diagnosis. It is observed that the proposed method involving fine-tuned pre-trained CNN outperforms existing techniques, achieving a high fault detection accuracy of 73.53%. Our study also highlights the importance of representative and discriminative features to classify faults (as opposed to the use of raw data), especially in the noisy scenario, where our method achieves the best performance of 70.45%. We believe that our work will serve to guide future research in PV system fault diagnosis.

The work describes the application of machine learning (ML) to the categorization and diagnosis of vehicle faults in the power transmission system. For each failure characteristic condition, a machine learning algorithm may be employed to categorize their separate diagnostic elements. The use of acoustic sensors can be used to create a real-time detection approach for vehicle engines and transmission systems. Previously, it was contacting drivers or obscuring services based on vehicle maintenance and driving safety degree under internet of vehicle (IoV) needs. The car's variable acoustic signals are captured and categorised utilising fuzzy logic controllers through the data acquisition device (DAQ) in this manner (FLC). Further, the results are optimized using Particle Swarm Optimization (PSO) technique. While previous systems used 15 fault conditions, we have just used 8 conditions to obtain results.

In this study, the hybrid Taguchi vortex search (HTVS) algorithm, which exhibits a rapid convergence rate and avoids local optima, is employed as a new training algorithm for feed-forward neural networks (FNNs) and its performance was analyzed by comparing it with the vortex search (VS) algorithm, the particle swarm optimization (PSO) algorithm, the gravitational search algorithm (GSA) and the hybrid PSOGSA algorithm. The HTVS-based FNN (FNNHTVS) algorithm was applied to three datasets (iris classification, wine recognition and seed classification) taken from the UCI database (the machine learning repository of the University of California at Irvine) and to the 3-bit parity problem. The obtained statistical results were recorded for comparison. Then, the proposed algorithm was used for fault classification on transmission lines. A dataset was created using 735 kV, 60 Hz, 100 km transmission lines for different fault types, fault locations, fault resistance values and fault inception angles. The FNNHTVS algorithm was applied to this dataset and its performance was tested in comparison with that of other classifiers. The results indicated that the performance of the FNNHTVS algorithm was at least as successful as that of the other comparison algorithms. It has been shown that the FNN model trained with HTVS can be used as a capable alternative algorithm for the solution of classification problems.

The induction motors are back bone of the modern industry and play very important role in manufacturing and transportation sectors. The induction motor faults are mainly classified into internal faults such as inter turn short circuits , broken rotors and external faults such as over load, over voltage faults and asymmetry in supply voltage. The identification of type of fault is very important for safe operation and for preventing risk of machine failures. In this work, a Bidirectional Long Short Term memory networks (Bi-LSTM)-based machine learning methodology is proposed for classification of external faults of Induction Motors. The line voltages of the three phases and the three line currents are considered as the inputs to the Bi-LSTM network for identifying types of fault. Line voltage and line current data sets are considered for six different types of fault conditions. The six different conditions of the three phase induction motor are normal output (NO), overload (OL), over voltage (OV), under voltage (UV), Voltage unbalance (VUB) and single phasing (SP). The BI-LSTM network is trained using Adam optimization algorithm. The classification results are obtained with Bi-LSTM network are compared with LSTM networks to show the advantage of the proposed approach.

Vessels typically house large sets of different, complex types of equipment; functional failures in them lead to operational stoppage or downgrade with impacts on performance, quality and/or cost. Preventive maintenance schedules are commonly employed, the optimization of which relates to the need of maintenance, the specific component where a problem is detected, the identified fault type, the severity, the expected remaining life within acceptable performance (confidence) limits, etc. Recent advances in sensors and in Machine Learning (ML) methods, have boosted both the fault diagnosis and prognosis, thus incenting companies to invest on the development of efficient Predictive Maintenance (PdM). In this work, we explore the PdM problem for a family of equipment, namely, compressors, through the application of ML techniques on large datasets obtained from on-board sensors. We first deal with the problem of identifying the most useful features in the frequency and time domains, that enable efficient classification and we demonstrate results on data pre-processing and feature extraction. We apply two different clustering and classification algorithms, namely, k-Nearest Neighbor (KNN) Support Vector Machines (SVM) on big datasets obtained from laboratory and industrial setups. We demonstrate that early failure prediction and fault classification is feasible and provides ample opportunities for the development of PdM tactics that reduce cost and minimize risk. Finally, we comment on the appropriateness of features and evaluate the classification accuracy for simple fault cases.

Modern manufacturing systems are often installed with sensor networks which generate high-dimensional data at high velocity. These data streams offer valuable information about the industrial system’s real-time performance. If a shift occurs in the manufacturing process, fault diagnosis based on the data streams becomes a fundamental task as it identifies the affected data streams and provides insights into the root cause. Existing fault diagnostic methods either ignore the correlation between different streams or fail to determine the shift directions. In this paper, we propose a directional fault classification procedure that incorporates the between-stream correlations. We suggest a three-state hidden Markov model that captures the correlation structure and enables inference about the shift direction. We show that our procedure is optimal in the sense that it minimizes the expected number of false discoveries while controlling the proportion of missed signals at a desired level. We also propose a deconvolution-expectation-maximization (DEM) algorithm for estimating the model parameters and establish the asymptotic optimality for the data-driven version of our procedure. Numerical comparisons with an existing approach and an application to a semiconductor production study show that the proposed procedure works well in practice.

This paper describes a comparison of three types of feature sets. The feature sets were intended to classify 13 faults in a centrifugal pump (CP) and 17 valve faults in a reciprocating compressor (RC). The first set comprised 14 non-linear entropy-based features, the second comprised 15 information-based entropy features, and the third comprised 12 statistical features. The classification was performed using random forest (RF) models and support vector machines (SVM). The experimental work showed that the combination of information-based features with non-linear entropy-based features provides a statistically significant accuracy higher than the accuracy provided by the Statistical Features set. Results for classifying the 13 conditions in the CP using non-linear entropy features showed accuracies of up to 99.50%. The same feature set provided a classification accuracy of 97.50% for the classification of the 17 conditions in the RC.

Rolling element bearings are crucial components of rotating machinery used in various industries, including aerospace, navigation, machine tools, etc. Therefore, it is essential to establish suitable techniques for condition monitoring and fault diagnosis of bearings to avoid breakdowns and damages during operation for overall industrial sustainability. Vibration-based condition monitoring has been the most employed technique in this perspective. Many researchers have investigated the vibration response of rolling element bearings having inner race defects, outer race defects, or rolling element defects using conventional techniques in past decades. However, Machine Learning (ML) has emerged as another way of bearing fault diagnosis. In this work, fault signatures of ball bearings are classified using a total of 6 (with 24 subcategories) ML models, and a comparative performance of these models is presented. The ML classifiers are trained with extracted time-domain and frequency-domain features using open-source Case Western Reserve University (CWRU) bearing data. Two datasets of different sample size and number of samples of vibration data corresponding to a healthy ball bearing, a defective bearing with inner race defect, a ball defect, and an outer race defect, running at a particular set of working conditions, are considered. The accuracy of ML models is compared to identify the best model for classifying the faults under consideration.

Short-circuit fault, also known as a lateral fault, is one of the common types of faults in power transmission lines. This paper proposes an accurate method for identifying short-circuit fault types for power transmission line systems. According to the three-phase A, B, and C values, short-circuit faults can be subdivided into ten classes. An MDL power system with controllable short-circuit fault types is designed and tested. The method of using wavelet packet to analyze short-circuit fault waveforms and using the fuzzy controller to analyze eigenvectors is also proposed. Wavelet packet analysis makes up for the defect that wavelet transform usually only decomposes low frequencies and retains high frequency. Therefore, more accurate frequency band information and eigenvectors are obtained through wavelet packet transform, and then, the fuzzy controller is used to identify the eigenvectors, which can effectively detect and discriminate short-circuit fault types. Accurate identification of short-circuit fault types can improve transmission lines’ overall operational efficiency and promote the power system’s development.

In the process of fault diagnosis for the core components of the integrated modular avionics power conversion module, selecting appropriate features can effectively improve the efficiency and classification accuracy of the model, and greatly reduce the computational complexity of the learning algorithm. This paper first designs a typical Sepic structure DC-DC converter model to simulate the typical fault types of the DC-DC converter; secondly, the corresponding original data is obtained through simulation; after data preprocessing, feature extraction and using multiple feature selection fusion algorithm, BP neural network method is used finally for fault diagnosis analysis of DC-DC converter. The simulation verifies the effectiveness of the above method.