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The presented paper introduces an accurate approach for detecting and classifying parametric or soft faults that affect analog integrated circuits. This technique is based on the use of machine learning algorithm to improve the accuracy and the performance of fault classification process. To achieve this, the real and imaginary frequency responses of output voltage and supply current of the circuits under test (CUT) are used to extract features for both normal and faulty cases. These features are then exploited to train machine learning classifiers, from which the selected one among its equivalents is the quadratic discriminant classifier since it allowed the highest average accuracy score. The faults to be investigated are parametric ones affecting resistors and capacitors values. The proposed approach is validated using three filters circuits that are Sallen-Key band-pass filter, four op-amp biquad high-pass filter, and a leapfrog filter circuit. Obtained results indicate a high classification average accuracy for all circuits that are undergone testing. The proposed approach has provided a highest classification accuracy level comparing to other research works. [ABSTRACT FROM AUTHOR]

Deep learning-based incipient fault diagnostic techniques have achieved surprisingly well in wind turbines. Due to component failures, wind turbines must undergo active maintenance, substantially influencing revenue and power generation. Unfortunately, there are consistently uneven data distributions between samples with faults and those without faults, resulting in incorrect fault classification. Wind turbine fault classification has a significant data imbalance problem, compromising learning attention for majority and minority classes. Machine learning methodologies based on Generative Adversarial Networks (GAN), over-sampling, and under-sampling techniques for generating synthetic data have been widely employed to address the imbalance data problem. However, the traditional synthetic minority oversampling technique (SMOTE) accomplishes oversampling using linear interpolation between close minority class samples, which could be confusing, subpar, and indistinguishable from the majority class. This study suggests combining over and under-sampling using adaptive SMOTE and edited nearest neighbors (ASMOTE-ENN) that incorporate over-sampling with adaptive SMOTE and under-sampling with ENN to improve the quality of the generated samples. With this resampling technique, noise in an imbalanced dataset is reduced on three levels by using an adaptive nearest neighbor selection algorithm to find the nearest neighbors that are visible. Then use SMOTE to create samples that precisely fall into the minority class, and later use the ENN technique to eliminate instances that contribute to noise afterwards. Resampling data created by combining over- and under-sampling approaches to match the data distribution over all classes is the foundation of the suggested method's efficacy. A hybrid ensemble method is used for effective classification, including boosting, bagging, and stacking techniques. The original unbalanced and balanced data using the ASMOTE-ENN algorithm were classified using the proposed hybrid ensemble method. The classification results show that the proposed strategy is more accurate than a few imbalanced fault diagnosis techniques. • To gain complete knowledge, characteristics of SCADA data are obtained using data analysis. • We present the adaptive SMOTE-ENN algorithm-based data resampling technique to deal with imbalanced data. • Create a hybrid ensemble classification technique to identify faulty and no-fault events effectively. • The results demonstrate that the suggested method outperforms the most popular ML techniques. [ABSTRACT FROM AUTHOR]

Residential air-conditioning systems are subject to soft faults, which can reduce their efficiency, capacity, and life expectancy, without being detected by the occupants. Early detection and diagnosis of faults could potentially be done by using machine learning (ML) on measurable features of the air-conditioner, such as refrigerant temperatures and pressures. This paper describes a novel approach to applying ML, which is to train a fault classifier using data from one system, and apply it to different systems. If this is effective, it can significantly reduce the cost of widescale application of ML fault classification. The support vector machine (SVM) algorithm was used. An additional question for practical application is whether the number of sensors required for input feature measurement can be reduced. This work also tests the feature reduction effect, in combination with applying an ML classifier trained on one system, to classify faults in two other systems. The results are disappointing, showing that fault classification is poor when the SVM classifier is trained on a different system than it is applied to. However, with additional work, this method has potential for improvement, possibly to a point where it can be a viable approach for field deployment. [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]

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]

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]

Abstract: This paper proposes a new approach based on combined Wavelet Transform-Extreme Learning Machine (WT-ELM) technique for fault section identification (whether the fault is before or after the series capacitor as observed from the relay point), classification and location in a series compensated transmission line. This method uses the samples of fault currents for half cycle duration from the inception of fault. The features of fault currents are extracted by first level decomposition of the current samples using discrete wavelet transform (DWT) and the extracted features are applied as inputs to ELMs for fault section identification, classification and location. The feasibility of the proposed method has been tested on a 400kV, 300km series compensated transmission line for all the ten types of faults using MATLAB simulink. On testing 28,800 fault cases with varying fault resistance, fault inception angle, fault distance, load angle, percentage compensation level and source impedance, the performance of the proposed method has been found to be quite promising. The results also indicate that the proposed method is robust to wide variation in system and operating conditions. [Copyright &y& Elsevier]

The development of airborne sensor monitoring and artificial intelligence technologies provides effective tools for precise prognostic and health management (PHM) of aircraft. This paper presents a PHM model which combines multiple deep learning algorithms for condition assessment, fault classification, sensor prediction, and remaining useful life (RUL) estimation of aircraft systems. A long short-term memory (LSTM) based recurrent network is used to predict multiple multivariate time series of sensors, and deep belief network (DBN) is applied to assess system condition and classify faults of aircraft systems. Then, the RUL can be estimated through the integration of condition assessment and sensor prediction. Finally, the proposed algorithm is validated experimentally using NASA's C-MAPSS dataset, and the results showed a lower error rate and deviation than traditional models. [ABSTRACT FROM AUTHOR]