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To address the issue of diagnosing hard and soft faults in active power factor correction (APFC) power supply, this study analyzes failure modes resulting from aging and malfunction of various sensitive components. The power fault waveform patterns are initially analyzed based on the circuit's THD, current ripple value, and RMS value. The inductor current signals in different fault modes are then utilized to extract and construct time–frequency fusion fault features of the APFC power supply. Finally, these feature quantities are downscaled and optimized using the RF algorithm. The SOA‐KELM model of the APFC converter is proposed, and the feature vectors under different fault modes are used to classify and diagnose faults, achieving hard and soft fault detection of the converter. The experiments show that the method achieves 100% accuracy for hard fault diagnosis and 96.36% accuracy for soft fault diagnosis of the converter, demonstrating high diagnostic accuracy. [ABSTRACT FROM AUTHOR]

This paper presents an efficient approach to estimate the failures of various components in an induction motor using motor current signature analysis. Conventional sensor-based fault detection methods lead to huge manpower and require greater number of sensors. To overcome these drawbacks, current signature base fault detection is proposed. An advanced spectral analysis, namely discrete wavelet transform (DWT), is used for frequency domain analysis. This paper also presents fault severity estimation using feature extraction-based evaluation of DWT coefficients. As the DWT gives many coefficients at higher level decomposition which is essential for high resolution, fault classification and severity index become challenging. To address this issue, artificial neural network (ANN) algorithm is used after DWT decomposition. The fault severity is predicted by proposed fault indexing parameter of various features like energy, standard deviation, skewness, variance, RMS values. Conventional algorithms like support vector machine, k‐nearest neighbour, local mean decomposition-singular value decomposition and extreme learning machine have given maximum of 98–99% accuracy, Whereas the proposed DWT-based ANN has given 100% accuracy with tanh function. Moreover, the testing loss with this function is also very less. Experimental results have affirmed the accuracy of proposed fault detection of various faults in induction motor of rating 3—Phase, 1. 5KW, 440 V and 50 Hz. [ABSTRACT FROM AUTHOR]

Machine learning and other data-driven methods have developed at a prolific rate for industrial applications due to the advent of industrial big data. However, industrial datasets may not be especially well-suited to supervised learning approaches that require extensive domain knowledge in the complete and accurate labeling of datasets. To address these challenges, a semi-supervised learning approach is proposed that makes use of partially labeled subsets. The proposed methodology is applied to high-dimensional in-process measurement data, utilizing a convolutional autoencoder (CAE) for unsupervised feature extraction. A multiclass extension for semi-supervised anomaly diagnosis is proposed that utilizes principal component analysis (PCA) as the basis for anomaly scoring, and the proposed approach intersects the results of targeted one-against-all phases on partially labeled sets to classify faults. Experiments in a case study on semiconductor manufacturing measurement data are performed to explore the relationship between latent features extracted and anomaly detection performance. The application of the proposed algorithm achieves a true positive detection rate of over 90% with false positive rate under 9% for both local and global anomaly types, with these results accomplished while reducing over 99% of the original input data dimensions. In addition, the approach also allows for positive samples to be identified that were previously undetected by human experts. These results are promising for the application of the proposed semi-supervised methodology in real industrial settings. [ABSTRACT FROM AUTHOR]

Rotor bars are one of the most failure-critical components in induction machines. We present an approach for developing a rotor bar fault identification classifier for induction machines. The developed machine learning-based models are based on simulated electrical current and vibration velocity data and measured vibration acceleration data. We introduce an approach that combines sequential model-based optimization and the nested cross-validation procedure to provide a reliable estimation of the classifiers' generalization performance. These methods have not been combined earlier in this context. Automation of selected parts of the modeling procedure is studied with the measured data. We compare the performance of logistic regression and CatBoost models using the fast Fourier-transformed signals or their extracted statistical features as the input data. We develop a technique to use domain knowledge to extract features from specific frequency ranges of the fast Fourier-transformed signals. While both approaches resulted in similar accuracy with simulated current and measured vibration acceleration data, the feature-based models were faster to develop and run. With measured vibration acceleration data, better accuracy was obtained with the raw fast Fourier-transformed signals. The results demonstrate that an accurate and fast broken rotor bar detection model can be developed with the presented approach. [ABSTRACT FROM AUTHOR]

The photovoltaic (PV) array is the most sensible element in PV plants, which is subject to different type of faults and defects. Thus, to keep these plants working efficiently they should be monitored and protected carefully. Some faults if they are not detected and isolated promptly they may lead to hazardous risks. The diagnosis of PV systems is widely addressed and recently machine learning (ML) and deep leaning (DL) methods drawn the attention of many researchers. Most applications of ML methods are based on the use of the I–V curves measurement, as enough information and features can be extracted from the curves, to detect and classify faults. These methods showed their capability to classify some faults, like line to line, degradation, disconnected PV modules, partial shading effect, and bypass diode faults. Another approach is based on the use of thermal or electroluminescence images of PV modules/arrays to detect and identify defects, such as hot spot, snails crack, and others. In this paper, different ML and ensemble learning (EL) methods are evaluated for fault diagnosis of PV arrays. The focus is mainly on the detection and classification of some complex faults that may affect the PV arrays, i.e., multiple faults, and faults with similar I–V curves, that are not evaluated before. The results showed the ability of the methods developed to detect faults with very good accuracy (classification rate = number of classified instances/total instances), within 99%, while the classification faults is done with an acceptable accuracy, within 81.73%. Through this study it is shown when really ML and EL methods should be used, and some recommendations, challenges and future directions in this topic are presented. [ABSTRACT FROM AUTHOR]

This article proposes the idea of fault classification-based control for the steady-state operation of unmanned surface vehicles (USVs). The idea of fault classification is achieved with the help of wavelet transforms and support vector machines, and the control is performed using a wavelet fuzzy controller. Initially, a brief idea of faults that affect the stable operation of USVs is identified. Furthermore, the surge and sway translational motion of USVs are realized with the help of a ball balancer setup. The fault data are measured in terms of plate angle, ball position, and motor operating voltage for developing the fault classifier. The proposed algorithm depicted improved classification accuracy when compared with conventional methods. To accommodate the operation of the system as per the operating state, a wavelet-based fuzzy controller is proposed. The proposed controller solves the problem of position tracking and balancing for ball and plate system with high precision, hence achieving the stable operation. [ABSTRACT FROM AUTHOR]

Fault classification is vital in smart manufacturing, and convolutional neural network (CNN) has been widely applied in fault classification. But the performance of CNN heavily depends on its learning rate. As the default setting on learning rate cannot guarantee its performance, the learning rate tuning process becomes essential. However, the traditional learning rate tuning methods either cost much time consumption or rely on the experts’ experiences, so it is a considerable barrier for the users. To overcome this drawback, this article proposes a CNN with automatic learning rate scheduler (AutoLR-CNN) for fault classification. First, the long short-term memory (LSTM) is used to extract the features of the past loss of CNN. Then, an agent based on deep deterministic policy gradient (DDPG) is trained to automatically control the learning rate for CNN online. Third, the double CNN structure is developed to enhance the stability of the proposed method. The proposed AutoLR-CNN is tested on two famous bearing data sets and a practical bearing data set on wind turbine. The results of AutoLR-CNN are superior to six commonly used baseline learning rate schedulers in Tensorflow. AutoLR-CNN is also compared with other reported machine learning and deep learning methods. The results show that AutoLR-CNN has achieved the state-of-the-art performance in fault classification. [ABSTRACT FROM AUTHOR]

Recently, precise and deterministic feature extraction is one of the current research topics for bearing fault diagnosis. For this aim, an experimental bearing test setup was created in this study. In this setup, vibration signals were obtained from the bearings on which artificial faults were generated in specific sizes. A new feature extraction method based on co-occurrence matrices for bearing vibration signals was proposed instead of the conventional feature extraction methods, as in the literature. The One (1) Dimensional–Local Binary Patterns (1D-LBP) method was first applied to bearing vibration signals, and a new signal whose values ranged between 0–255 was obtained. Then, co-occurrence matrices were obtained from these signals. The correlation, energy, homogeneity, and contrast features were extracted from these matrices. Different machine learning methods were employed with these features to carry out the classification process. Three different data sets were used to test the proposed approach. As a result of analysing the signals with the proposed model, the success rate is 87.50% for dataset1 (different speed), 96.5% for dataset2 (fault size (mm)) and 99.30% for dataset3 (fault type – inner ring, outer ring, ball) was found, respectively. [ABSTRACT FROM AUTHOR]

To improve power system reliability, a protection mechanism is highly needed. Early detection can be used to prevent failures in the power transmission line (TL). A classification system method is widely used to protect against false detection as well as assist the decision analysis. Each TL signal has a continuous pattern in which it can be detected and classified by the conventional methods, i.e., wavelet feature extraction and artificial neural network (ANN). However, the accuracy resulting from these mentioned models is relatively low. To overcome this issue, we propose a machine learning-based on Convolutional Neural Network (CNN) for the transmission line faults (TLFs) application. CNN is more suitable for pattern recognition compared to conventional ANN and ANN with Discrete Wavelet Transform (DWT) feature extraction. In this work, we first simulate our proposed model by using Simulink® and Matlab®. This simulation generates a fault signal dataset, which is divided into 45.738 data training and 4.752 data tests. Later, we design the number of machine learning classifiers. Each model classifier is trained by exposing it to the same dataset. The CNN design, with raw input, is determined as an optimal output model from the training process with 100% accuracy. [ABSTRACT FROM AUTHOR]

In this paper, machine learning technique is used to detect and classify all shunt faults in a UPFC compensated transmission line. A four-bus three-machine system with detailed modelling of UPFC has been used for fault simulation studies in MATLAB/Simulink. Instantaneous voltage and current signals obtained at local bus terminal are processed with DFT and statistical method for feature extraction. The input features of the ANN are minimised by using the statistical method. Generated features are used for training the ANN module. Trained ANN modules are used for testing different fault conditions in the time domain. Rigorous simulation studies have been performed with a wide variety of different possible fault situations. Simulation results bring out the superiority of the scheme. Moreover, the error introduced due to CT, CCVT and Dynamic behaviour of the UPFC has been considered for testing the trained ANNs by varying the different operating mode of UPFC, and different compensation levels, wherein all the cases, the performance is found reliable. [ABSTRACT FROM AUTHOR]

The paper presents a method using deep learning framework based on convolution neural network (CNN), for identification and localization of faults of transformer winding under impulse test. The results show that the proposed method outperforms the existing methods significantly. The present scheme eliminates the requirement of separate feature extraction and classification algorithms for the analysis of fault current patterns. A part of the proposed network performs feature learning and the other part classifies the features in a supervised manner. The method is computation intensive but capable of achieving very high degree of accuracy; on an average a margin of more than 7% compared to other published literature till date. [ABSTRACT FROM AUTHOR]

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]

Fault Detection and Isolation (FDI) in Heating, Ventilation, and Air Conditioning (HVAC) systems is an important approach to guarantee the human safety of these systems. Therefore, the implementation of a FDI framework is required to reduce the energy needs for buildings and improving indoor environment quality. The main goal of this paper is to merge the benefits of multiscale representation, Principal Component Analysis (PCA), and Machine Learning (ML) classifiers to improve the efficiency of the detection and isolation of Air Conditioning (AC) systems. First, the multivariate statistical features extraction and selection is achieved using the PCA method. Then, the multiscale representation is applied to separate feature from noise and approximately decorrelate autocorrelation between available measurements. Third, the extracted and selected features are introduced to several machine learning classifiers for fault classification purposes. The effectiveness and higher classification accuracy of the developed Multiscale PCA (MSPCA)-based ML technique is demonstrated using two examples: synthetic data and simulated data extracted from Air Conditioning systems. [ABSTRACT FROM AUTHOR]