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• Detection and classification of faults including high impedance faults in microgrid. • Fault detection and classification from current signals at the substation. • Faulty feeder and sub-feeder identification within half cycle of post fault current signal. • Wavelet based power spectral density of current signals utilized to detect and classify fault and also identify faulty feeder. • Performance of the proposed algorithm verified under dynamic and challenging situations. The recent progress in the area of microgrid protection has led to the application of data-driven and signal processing techniques for fault detection in microgrids. The deployment of inverter interfaced distributed generators makes the traditional protection schemes ineffective. In this paper a wavelet based power spectral density (PSD) has been utilized to detect fault, identify faulty phases and locate fault in the microgrid. Wavelet based PSD decouples the frequency information from the time information and it is the optimal technique to reveal the change of distribution of harmonics in non-stationary faulty signals as for fault analysis it can consider the faulty frequency band instead of one particular harmonic. The proposed technique can detect fault and identify faulty phases by analyzing current signals from the substation only. The normalized value of PSD of three phase current signals has been considered for fault detection and probabilistic distribution of PSD has been considered for faulty phase identification. The normalized value of PSD of the current signals collected from both ends of the main feeders and one end of the non-DG sub-feeder have been used to locate the faulty feeder and sub-feeder. Appropriate threshold values have been considered to detect fault and to locate the faulty feeder. To locate faults, the technique does not require synchronized signals and thus provides a promising and economical solution for inverter interfaced microgrid. It does not require huge amount of data set for training and testing as it is essentially a threshold based technique and also does not require collection of data at high sampling rate. The proposed scheme has been extensively tested for high impedance fault (HIF), unbalanced loading, load switching, variation of DG parameters, measurement uncertainty and presence of noise in the signal with signal to noise ratio up to 10 dB. This technique provides accurate results for fault resistance from 0 Ω to 300 Ω and up to 10% measurement error. The performance of the proposed technique has been verified for feeder length variation up to 20%. Half cycle of current signal only after fault is required and three levels of wavelet decomposition has been considered. The simulation results and comparative assessment demonstrate the efficacy of the proposed scheme. [ABSTRACT FROM AUTHOR]

• A kurtogram based deep sequential model is examined for bearing fault classification. • The deep learning sequential model has a good ability to analyze the sequential data. • Analysing the kurtogram as sequential data helps in improving the performance. • Conversion of raw signal to kurtogram helps to generate good feature representations. The condition monitoring of rotating machinery has been widely accepted by the industrial system for intelligent fault diagnosis to achieve sustainability, high performance and provide safety to workers. Therefore, in recent years, artificial intelligence (AI) and signal processing (SP) methods are operated collectively for fault diagnosis. The complex and hybrid input feature set are constructed using SP methods for AI-based fault diagnosis. Thus, over the years the numbers of features in the feature space are increasing to represent the various faults as well as fault severities, and also, different feature selection techniques are operated on feature space to determine the ideal features. Consequently, it is a challenging task to design the dominant feature set for distinguishing the type of defects. Also, the requirement of a number of features is changing due to various working conditions of rotating machinery. Therefore, a new intelligent diagnosis method for fault classification build on the kurtogram and sequence models (SM) of deep learning is proposed in this paper. The kurtogram is a comprehensive tool for providing well-defined information about defects by organizing frequency domain kurtosis values in order at each level. Thus the SM analyzes the kurtogram as sequential data for fault diagnosis and hence helps to eliminate the feature selection exercise for identifying the dominant features from feature space. The proposed method has examined using two vibration datasets of bearing. The result demonstrates that the proposed method has a promising performance and achieves decent fault classification accuracy in comparison with other methods. [ABSTRACT FROM AUTHOR]

• A new scheme is developed to identify the fault using reactive power measurements. • The reactive-power superiority compared with active measurements is investigated. • The optimal mother wavelet is selected based fault identification scheme. • The proposed scheme is immune to different fault parameters. • Testing the fault identification scheme at different operating conditions. The process of fault-identification in teed transmission lines (TTLs) has considerable attention in recent years. In this line, this paper proposes a novel scheme for fault detection, classification and faulted-phase/s identification on the basis of reactive-power components of TTLs. The current and voltage measurements are collected from a single-end measuring system of teed circuits. Based on the filtering of measured currents and voltages signals using improved recursive wavelet transform, the reactive-power measurements are computed. Thereafter, the Wavelet Multi-Resolution Analysis Technique (WMRAT) is employed for decomposition of reactive-power measurements. An improved recursive wavelet (IRW) transform that has good time-frequency characteristics is proceed to remove dc offsets and harmonics from the measured signals. The ATP/EMTP package and MATLAB program are used to simulate the faulted cases and verify of operating the proposed fault-identification scheme. Simulation outcomes show that the proposed scheme has the capability to detect and classify different fault types in TTLs. The presented scheme is designated to be insensitive to different fault resistances, different fault locations, and inception angles. In addition, the proposed scheme is also tested under different technical aspects such as different sampling rates, different locations of the single-end measuring system, loading change and source parameters variation. The superiority of reactive-power compared with active power measurements during faults occurrence is also investigated to show the notability of the proposed scheme. The obtained results corroborate that the proposed scheme can be correctly identified different fault types and locations along the line. [ABSTRACT FROM AUTHOR]

• Speed variation induces fault information imbalance in vibration signals. • Proposed a speed adaptive gate to address the information imbalance. • Empirically validated the effectiveness of the proposed speed adaptive gate. • Speed signals can facilitate deep learning-based fault classification. Rotating machines like wind turbines often work under varying speed conditions. Faults may occur in these machines as time goes on. It is important to detect faults early and classify the type and the severity of an occurred fault, so that timely maintenance could be scheduled. This paper attempts to improve the fault classification accuracy of existing deep learning models when they are used for the fault classification of rotating machinery that operates under varying speed conditions. To achieve this goal, we firstly investigate the effects of speed variation from the perspective of deep learning. We find that the fault information in vibration signals is imbalanced due to speed variation. The imbalance future deteriorates the fault classification accuracy of deep learning models. That is, the accuracy does not evenly distribute across speed but increases with speed. We then propose an auxiliary branch named speed adaptive gate (SAG) for existing deep learning models to address the speed induced fault information imbalance. The SAG takes the speed signal as the input and outputs speed adaptive gate values. The gate values control the information flow of deep learning models so that the effects of speed variation are mitigated. Case studies with two baseline models, i.e., a convolutional neural network (CNN) and a residual network (ResNet), over two experimental datasets, i.e., a planetary gearbox dataset and a fixed-shaft gearbox dataset, have validated the effectiveness of the proposed SAG and its superiority over existing approaches. [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]

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]

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]

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]

• 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]

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]

• Sensitivity analysis of different transfer function components for different faults. • Correlation coefficient drawbacks investigation on fault detection. • Proposing a new index for the classification of different faults. • Investigation of fault classification accuracy of transfer function components. • Detection and classification of fault type, location and extent of faults. Frequency response analysis is one of the best methods to diagnose mechanical defects in the power transformer winding. In this paper, sensitivity analysis of four components of the transfer function (TF), i.e., amplitude, phase, real and imaginary components of TF is investigated for different defects. The classification of type, location, and extent of different defects is carried out using support vector machine with cubic polynomial kernel. The accuracy of different indices and four different components of TF are investigated. Moreover, a new index based on fitting performance is defined. The results show that despite widespread use and popularity of amplitude of TF, the phase, imaginary, and real components of TF, have more sensitivity to the defects respectively and are more accurate for classification. Also, compared with the results of some high usage indices, the proposed index has the best classification accuracy. [ABSTRACT FROM AUTHOR]

• Novel fault classification algorithm (FCA) for GUPFC-compensated transmission lines. • FCA is based on the loci of active and reactive powers calculated using PMU signals. • Presenting faulty area and faulty circuit detection algorithms using new indices. • The proposed method is robust and independent of the operating modes of the GUPFC. • Easily applicable in regular fault locators available in the protection industry. The paper presents a fault area detection and classification algorithm in the phasor domain for double-circuit lines compensated by a generalized unified power flow controller (GUPFC). In the first step, by defining and using a voltage index, the algorithm determines the side of the GUPFC in which the fault has occurred. In the second step, by defining and employing a current index, the algorithm will be able to identify the circuit of the line on which the fault has happened. Finally, the fault is classified. In this step, the algorithm detects the faulty phase by analyzing the loci of three-phase active and reactive powers at the line terminal. The algorithm uses the current and voltage phasors of the line terminals measured by the phasor measurement units (PMUs) in one cycle before and during the fault. The GUPFC and the test system have been modeled in MATLAB/Simulink. The presented algorithm is tested and evaluated under different conditions including different fault types and fault resistances for all normal series and shunt fault types. The results obtained from simulations verify the accurate performance of the algorithm for various fault conditions. [ABSTRACT FROM AUTHOR]

• A dimensionality reduction algorithm based on local-global balanced orthogonal discriminant projection is proposed. • A novel fault diagnosis method of rotor based on LGBODP is presented. • The vibration signals of double-span rotor systems are used to verify the effectiveness of the LGBODP algorithm. • The feasibility of the rotor fault diagnosis method based on LGBODP dimensionality reduction is verified. The rotor is the most important part of the whole rotating machinery. Whether the rotor is normal directly determines the normal operation of the whole rotating machinery. Aiming at the problem of classification difficulty caused by multi-class and high-dimensional complex characteristics of rotor fault data, a fault data set reduction method based on Local-Global Balanced Orthogonal Discriminant Projection (LGBODP) is proposed. The algorithm comprehensively considers the intra-class local information, intra-class non-local information, inter-class local information and inter-class non-local information of the data, so as to avoid the loss of structure information in the dimension reduction process. By maximizing the inter-class distance and minimizing the intra-class distance, the intrinsic manifold structure information of the fault feature data set is effectively extracted while maintaining the global feature information. First of all, the mixed feature of the rotor vibration signal was extracted from multiple angles in time domain, frequency domain and time-frequency domain, and the high-dimensional feature set was constructed. The low-dimensional fault sensitive feature subsets are extracted by the proposed LGBODP algorithm. Then, the K-nearest neighbor (KNN) method is used as a fault feature classifier to recognize different fault types of rotors. The effectiveness of the proposed algorithm is verified by the vibration signal sets of two different types of double-span rotor systems. Application examples show that this method can be used to comprehensively extract the global and local discriminant information of vibration signals of rotors and effectively diagnosis the fault of rotors. [ABSTRACT FROM AUTHOR]

• In this study, motor faults were detected with sound signals. • Novel sound datasets were collected and comprehensively results were calculated. • A high accurate acoustical fault classification method was presented. • The proposed method achieved higher than 99% accuracies for the collected datasets. • This situation shown effectiveness of the proposed BP and NCA based method. Induction motors, which are widely used in industrial applications, are indispensable tools of the industry. Induction motors work in almost every part of the industry, such as production, packaging, and service. In this study, an acoustic-based method is proposed for the detection of the rotor and bearing faults of three-phase induction motors. In the first stage, two fault sound datasets were collected and these datasets are called near and far. For extracting features from these sounds, a multilevel feature generation method is presented and this method uses Discrete Wavelet Transform (DWT) and Local Binary Pattern (LBP) methods together. Neighborhood Component Analysis (NCA) method was used to select the most informative features. Selected features are utilized as the input of SVM (Support Vector Machine) and KNN (K Nearest Neighborhood) classification algorithms. 99.8% classification success was achieved as a result of the SVM algorithm and the KNN algorithm reached 99.9% classification accuracy. [ABSTRACT FROM AUTHOR]

• Various methods applied for series-compensated transmission lines (SCTL) are discussed. • An investigation of solutions to their drawbacks is presented. • A novel scheme based on the application of wavelet packet transform is proposed for an SCTL. • Simulation results validate the effectiveness of the proposed protection scheme. For series-compensated transmission lines (SCTLs), identification of the fault section with respect to the compensator as well as fault phase selection are important issues. In this paper, first, the various methods applied for SCTLs are classified and discussed according to specified performance indices selected by the operator. Further, an investigation of solutions to their drawbacks is presented. Second, a novel scheme based on the application of wavelet packet transform (WPT) is proposed for an SCTL. A db10 wavelet packet is used for decomposing the faulted phase current waveform to obtain the energy coefficients. Third, the ATP/EMTP package is used to test and validate the proposed scheme, in which several fault scenarios with different fault locations, inception angles, fault resistances, noise, and variation in voltage and source impedance are examined to show its robustness. The effectiveness of the proposed scheme is demonstrated compared to other methods in the literature. [ABSTRACT FROM AUTHOR]