Search

This study introduces a novel approach for fault classification in bearing components utilizing raw accelerometer data. By employing various neural network models, including deep learning architectures, we bypass the traditional preprocessing and feature-extraction stages, streamlining the classification process. Utilizing the Case Western Reserve University (CWRU) bearing dataset, our methodology demonstrates remarkable accuracy, particularly in deep learning networks such as the three variant convolutional neural networks (CNNs), achieving above 98% accuracy across various loading levels, establishing a new benchmark in fault-detection efficiency. Notably, data exploration through principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) provided valuable insights into feature relationships and patterns, aiding in effective fault detection. This research not only proves the efficacy of neural network classifiers in handling raw data but also opens avenues for more straightforward yet effective diagnostic methods in machinery health monitoring. These findings suggest significant potential for real-world applications, offering a faster yet reliable alternative to conventional fault-classification techniques. [ABSTRACT FROM AUTHOR]

Summary Node faults are often influenced by factors such as physical component failure, communication module errors, battery exhaustion, and environmental factors. Various researchers have contributed to addressing the problem of detecting, diagnosing, and classifying faults in sensor networks. However, computational complexity and fault occurrence probability increase with the size of the network. In this context, an enhanced optimized layered diagnosis framework (OLDF) is proposed for detecting, diagnosing, and classifying sensor node faults. OLDF utilizes a deep belief network (DBN) with chaos quantum‐behaved particle swarm optimization (CQ‐PSO) for efficient diagnosis and classification of various sensor faults in the network. The proposed OLDF consists of two layers. In the first layer, optimized DBN with CQ‐PSO algorithm is employed to determine the fault type of the sensor nodes. Then, the fault type of sensor nodes that are classified in the first layer is given as input into the second layer of the OLDF algorithm to determine the fault severity in the network. Experimentation has been carried out by using the NS3 simulator for the OLDF framework with performance metrics such as classification accuracy, diagnosis accuracy, false positives, fault alarm rate, and average energy consumption. The validity of the proposed framework has been verified through comparisons with existing works such as MFD, INSA, FDRFC, and HFD. From the comparative analysis carried out, it is apparent that the proposed framework achieves a high classification accuracy of 89.94% and diagnosis accuracy of 93.21%, with less false positive rate, and utilizes minimum energy when compared with its counterparts. [ABSTRACT FROM AUTHOR]

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 proposes a novel unsupervised classification method to solve the problem of mechanical fault diagnosis under the condition of unknown number of fault types. The proposed method combining the three data processing stage. First, the deep encoding neural networks is used to complete the abstract signal feature representation under the unsupervised conditions. Second, the feature dimensionality reduction technique based on manifold learning is used to complete the low-dimensional mapping of the feature space. Third, the spatial clustering based on density criterion is introduced to classify the different fault samples. This paper uses two fault signals dataset to complete the performance verification experiment. The experimental results show that the DMDUC method respectively achieves the classification accuracy of 99.7 % and 100 % on the two datasets. [ABSTRACT FROM AUTHOR]

This paper proposes a noise-robust and accurate bearing fault diagnosis model based on time-frequency multi-domain 1D convolutional neural networks (CNNs) with attention modules. The proposed model, referred to as the TF-MDA model, is designed for an accurate bearing fault classification model based on vibration sensor signals that can be implemented at industry sites under a high-noise environment. Previous 1D CNN-based bearing diagnosis models are mostly based on either time domain vibration signals or frequency domain spectral signals. In contrast, our model has parallel 1D CNN modules that simultaneously extract features from both the time and frequency domains. These multi-domain features are then fused to capture comprehensive information on bearing fault signals. Additionally, physics-informed preprocessings are incorporated into the frequency-spectral signals to further improve the classification accuracy. Furthermore, a channel and spatial attention module is added to effectively enhance the noise-robustness by focusing more on the fault characteristic features. Experiments were conducted using public bearing datasets, and the results indicated that the proposed model outperformed similar diagnosis models on a range of noise levels ranging from −6 to 6 dB signal-to-noise ratio (SNR). [ABSTRACT FROM AUTHOR]

During power supply swings, current and voltage waveforms are modulated with additional frequency elements which lead to maloperation of relays. This maloperation may cause cascade tripping of transmission lines, which results in the failure of electrical grid networks. Power Swing Blockers (PSBs) are employed to block and prevent maloperation of distance relays during power swing. Along with power swing, the presence of TCSC compensation in transmission line deteriorates the input signals to relays which make fault classification a complex task. During the PSB blocking period, modulated signals distorted with non-linearities of MOV operation complicate the fault diagnosis functioning. This challenge motivates to develop an algorithm for fault classification during power swings. To address aforementioned challenges, a Wigner Ville energy-based fault classification technique is proposed in this work. A modified 9 bus WSCC system with TCSC compensation is used for testing and validation of the proposed scheme. The scheme is tested for different cases like symmetrical, close-in fault, asymmetrical high resistance fault, and CT saturation, etc. The scheme is also validated against transient conditions like load and capacitor switching. The performance of proposed scheme is compared with existing schemes in literature. The above scheme is also implemented and validated on an Intel Cyclone V SOC FPGA. The results show that the proposed scheme classifies faults accurately during the simulation as well as on hardware platform. [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]

The occurrence of multiple faults is a practical problem in the bearings of rotating machines, and early diagnosis of such issues in an intelligent manner is vital in the era of industry 4.0. The present work investigated various combinations of bearing faults, including dual and multiple fault conditions. Two prevalent fault diagnosis methods were employed: vibration monitoring using time-frequency scalograms extracted through Continuous Wavelet Transform (CWT) and a non-invasive Infrared Thermography (IRT). A 2-D Convolutional Neural Network (CNN) was used to classify various combinations of fault conditions through automated feature extraction. The proposed methodology was validated at two constant speed conditions of 19 Hz and 29 Hz and continuously accelerated and decelerated speed conditions in the range of 19 Hz - 29 Hz. Adequate accuracy was achieved in both dual and multiple fault conditions in the case of vibration-based fault diagnosis, with a range of 99.39 % to 99.97 %. Meanwhile, in the case of proposed IRT-based fault diagnosis, 100 % classification accuracy was achieved for dual and multiple faults in all conditions. These results signify the robustness and reliability of the proposed methodology for dual and multiple fault diagnosis in bearings at constant and varying speed conditions. [ABSTRACT FROM AUTHOR]

A wind turbine (WT) is an electromechanical system that often operates under a wide range of production conditions. These electrical systems are nowadays expanding rapidly, and they have considerable importance due to their efficiency as renewable energy sources. This led to proposing an innovative and efficient solution with intelligent systems to maintain and ensure the safe and stable operation of these dynamic systems. Maintenance tasks are based on the development of high-performance diagnostic tools, which consist in detecting and locating correctly and upstream the various failures affecting this wind machine. Where, the condition monitoring and supervision systems must rely on reliable fault diagnosis techniques in order to: avoid breakdowns and unscheduled shutdowns, improve their operation, and increase their energetic performances. In order to ensure adequate maintenance actions for the wind system, the purpose of this article is to propose and develop a robust and intelligent fault diagnosis structure. In this work, Kalman filters (KF) as state estimators are used to observe the output states of the sub-systems in order to generate the appropriate residuals evaluated by predetermined thresholds. Adaptive and hybrid network-based fuzzy inference systems (ANFIS) have been employed for the evaluation and classification stages of the detected faults to minimize the degradation of the wind turbine. All possible faults of wind turbine systems, sensors, and actuators are tested and investigated in all parts: pitch angle systems, drive, and generator with converter. The developed fault detection and identification structure are tested on a horizontal WT benchmark model using different scenarios and faults. The simulation results show the ability of the proposed and developed diagnostic methodology to detect the faults occurring efficiently and correctly in the machine. Thus, by using this robust diagnostic strategy, the condition monitoring system can maintain and ensure stable and safe operation to generate sufficient electrical power. [Display omitted] [ABSTRACT FROM AUTHOR]

In this study, a novel technique for identifying and categorizing flaws in small-scale photovoltaic systems is presented. First, a supervised machine learning (neural network) was developed for the fault detection process based on the estimated output power. Second, an extra tree supervised algorithm was used for extracting important features from a current-voltage (I–V) curve. Third, a multi-stacking-based ensemble learning algorithm was developed to effectively classify faults in solar panels. In this work, single faults and multiple faults are investigated. The benefit of the stacking strategy is that it can combine the strengths of several machine learning-based algorithms that are known to deliver good results on classification tasks, producing results that are more precise and efficient than those produced by a single algorithm. The approach was tested using an experimental dataset and the findings show that it could accurately diagnose faults (a detection rate of around 98.56% and a classification rate of around 96.21%). A comparison study with different ensemble learning algorithms (AdaBoost, CatBoost, and XGBoost) was conducted to evaluate the effectiveness of the suggested method. [ABSTRACT FROM AUTHOR]

Bearings are critical components of motors. However, they can cause several issues. Proper and timely detection of faults in the bearings can play a decisive role in reducing damage to the entire system, thereby reducing economic losses. In this study, a hybrid fuzzy V-structure fuzzy fault estimator was used for fault diagnosis and crack size identification in the bearing using vibration signals. The estimator was designed based on the combination of a fuzzy algorithm and a V-structure approach to reduce the oscillation and improve the unknown condition's estimation and prediction in using the V-structure method. The V-structure surface is developed by the proposed fuzzy algorithm, which reduces the vibrations and improves the stability. In addition, the parallel fuzzy method is used to improve the robustness and stability of the V-structure algorithm. For data modeling, the proposed combination of an external autoregression error, a Laguerre filter, and a support vector regression algorithm was employed. Finally, the support vector machine algorithm was used for data classification and crack size detection. The effectiveness of the proposed approach was evaluated by leveraging the vibration signals provided in the Case Western Reserve University bearing dataset. The dataset consists of four conditions: normal, ball failure, inner fault, and outer fault. The results showed that the average accuracy of fault classification and crack size identification using the hybrid fuzzy V-structure fuzzy fault estimation algorithm was 98.75% and 98%, respectively. [ABSTRACT FROM AUTHOR]

Bearings are generally used as bearings or turning elements. Bearings are subjected to high loads and rapid speeds. Furthermore, metal-to-metal contact within the bearing makes it sensitive. In today's machines, bearing failures disrupt the operation of the system or completely stop the system. Bearing failures that can occur can cause enormous damage to the entire system. Therefore, it is necessary to anticipate bearing failures and to carry out a regular diagnostic examination. Various systems have been developed for fault diagnosis. In recent years, deep transfer learning (DTL) methods are often preferred in current bearing diagnosis models, as they provide time savings and high success rates. Deep transfer learning models also improve diagnosis accuracy under certain conditions by greatly reducing human intervention. Diagnosis at the right time is very important for the sustainability and efficiency of industrial production. A technique based on continuous wavelet transform (CWT) and two dimensional (2D) convolutional neural networks (CNN) is presented in this paper to detect fault size from vibration data of various bearing failure types. Time-frequency (TF) color scalogram images for bearing vibration signals were obtained using the CWT method. Using AlexNet, GoogleNet, Resnet, VGG16, and VGG19 deep transfer learning methods with scalogram images, fault size prediction from vibration signals was performed. Five different transfer deep learning models were used for three different data sets. It was observed that the success rates obtained varied between 96.67% and 100%. [ABSTRACT FROM AUTHOR]

Large rotating machinery is widely applied in many industrial applications and shaft orbit classification plays an important role in fault diagnosis of large rotating machinery. Therefore, we propose a novel raw data feature-based automatic and deterministic shaft orbit classification framework for large rotating machinery. The main idea of the proposed framework is that it directly uses the most discriminative geometric features of each shaft orbit type for classification. Different from existing supervised learning shaft orbit classification methods, it does not need any labeled data and it is a deterministic, quite simple, light, easy to implement. Extensive experiments of synchronous integrated period sampling and fixed sampling at different rotating speeds conducted on real and simulated datasets have shown the superiority of proposed framework. This study provides a new deterministic interpretable and explainable approach to support predictive maintenance of large rotating machinery in various industrial fields even when no labeled data is available. [Display omitted] • ADSOC is a raw data feature-based classification framework so that it does not need any labeled data. • ADSOC is invariant to shaft orbit translation, scale and rotation. • ADSOC model and results are easy to interpret and explain. • ADSOC is independent from both shaft orbit starting point and rotating direction, and the shaft orbit does not need to be closed. • ADSOC is a deterministic framework and it is quite simple, light and easy to implement. [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]

Induction motors are important equipment in modern industry. However, the occurrence of fatigue failure following an extended period of operation invariably results in a catastrophic failure. As a result, monitoring and diagnosing induction motors is critical to avoiding unplanned shutdowns caused by premature failures. This article aims to develop an effective method for motor fault detection using time–frequency contents of vibration signals and an attention-based convolutional neural network model. First, the vibration signals are collected and labeled into five different categories: normal condition, outer ring fault, inner ring fault, misalignment condition, and broken rotor bar. Then, using the Morlet function, continuous wavelet transform (CWT) converts the vibratory time-series signals to the scalogram feature images. The time–frequency feature images are created after downsampling and converting the measured vibration signals to the frequency domain. These images are then resized and fed into the proposed convolutional attention neural network (CANN) to identify various induction motor failures. The experimental results demonstrate that the suggested model can provide an excellent diagnosis accuracy of 99.43%, significantly better than the state-of-the-art deep learning approaches for fault diagnosis. Moreover, the developed model’s robustness is validated against adversarial attacks based on the fast gradient sign method (FGSM) by including white Gaussian noise. [ABSTRACT FROM AUTHOR]

Rolling element bearings (REBs) are vital parts of rotating machinery across various industries. For preventing breakdowns and damages during operation, it is crucial to establish appropriate techniques for condition monitoring and fault diagnostics of these bearings. The development of machine learning (ML) brings a new way of diagnosing the fault of rolling element bearings. In the current work, ML models, namely, Support Vector Machine (SVM) and K-Nearest Neighbor (KNN), are used to classify the faults associated with different ball bearing elements. Using open-source Case Western Reserve University (CWRU) bearing data, machine learning classifiers are trained with extracted time-domain and frequency-domain features. The results show that frequency-domain features are more convincing for the training of ML models, and the KNN classifier has a high level of accuracy compared to SVM. [ABSTRACT FROM AUTHOR]

Summary: Fault diagnosis is vital for detecting the type of fault and rapid restoration of the electric power supply. The existing machine learning‐based classification method for detecting fault shows better performance. But these methods require manual pre‐processing steps and feature engineering, which makes them unreliable and time‐consuming for the complex and multi‐bus system. In this paper, a fully automated approach based on a deep learning framework has been proposed for the multi‐classification of faults in a grid network. The deep learning framework provides a better incentive to predict the type of fault directly using raw data as input. For this intended task of fault diagnosis, a fully convolutional network is designed and implemented to provide better performance with less trainable parameters. The proposed scheme is tested on the large number of simulated data generated from an IEEE 30 bus system. The performance for fault classification is validated through 10‐fold cross‐validation and numerous performance metrics like accuracy, recall, precision, specificity, and F1 score are evaluated. The performance of the different models with parameter variation is compared to obtain better performance with optimal trainable parameters. It has also been compared with some existing methods and shows promising result for fault classification in the large or multi‐bus system. [ABSTRACT FROM AUTHOR]

In machinery fault detection and identification (FDI), decomposing vibration signals into corresponding intrinsic mode functions (IMFs) reduces the intricacy in extracting weak fault features at the early failure state. However, selecting a suitable IMF for fault information extraction is a challenging task. Analyzing the non-Gaussian IMFs allows extracting effective fault-related information rather than the entire signal or other IMFs because the vibration signals are random in nature. In this study, we present an IMF selection method based on the maximum kurtosis value of each IMF. A kurtosis computation method named autogram is used. It considers the autocovariance function to characterize the 2nd order cyclostationary. We deploy the AdaBoost algorithm with a decision tree classifier to gain a better performance compared with other tree-based classifiers. The proposed FDI framework can effectively detect and classify multiple fault features at the incipient failure stage. [ABSTRACT FROM AUTHOR]

This study aims at developing a novel method for condition monitoring technique for detection and classification of developing faults and increase the working life of continuous variable transmission (CVT) using Daubechies Wavelet 06 (DB-06). The vibration data is collected for 4 different types of faults and healthy condition. Using a magnetic accelerometer and signal analyser, vibration data is collected from the system in the time-domain which is then used as input for a MATLAB code producing the plot of the frequency-domain signal. Maximum frequency is determined to diagnose the faults which are induced over three different belts. Collected data for large scale automotive system (CVT) is used to train the network and then it is tested based on random data points. Faults were classified using ANN with a classification rate of 90.8 %. [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]

The simple and robust construction, less weight, wide operating speed range, and higher fault tolerance capability of switched reluctance (SR) motor make it a viable contender for the conventional dc and ac machines. The faults at the rotor, winding, stator converters, and sensors lead to overcurrent, increase torque ripples, and sudden breakdown of the system. Thus, it is an urgent requirement to recognize and classify the faults that exist in switched reluctance motors, thereby the reliability, robustness, and widespread utilization of SR motors can be increased. The phases of the SR Motor are excited by using an asymmetric bridgeless resonance converter. This paper proposes an automatic diagnosis and classification of faults using radial basis function neural network (RBFNN). A mathematical model of the SR motor is established to determine the state of the art of fault condition of SR motors. The speed data of SR motor are utilized by RBFNN to generate fault information. Gabor filter is used for preprocessing the input data, and segmentation is achieved using high accurate DCT-DOST transformation. A gray-level co-occurrence matrix optimized with a genetic algorithm is used to extract the features in the speed signal of the motor. A test setup was developed in MATLAB to measure the performances of the RBFNN classifier in real-time. The effectiveness of the simulated fault classification model is verified by comparing the results of the conventional PI controller with several optimized algorithm-based tuned PI controllers. [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]

The fault classification of medium-voltage transmission lines consisting of wind farms connected to the high-voltage (HV) transmission networks is carried out using the power-spectrum-based hyperbolic S-transform (HST) powerful technique for analysing non-linear and non-stationary fault signals through the non-intrusive monitoring systems. The HST technique extracts the useful features in the time-frequency domain from measuring fault current waveforms of the HV utility side to discriminate the fault types. Parseval's theorem is applied to each HST coefficient to quantify the energy distribution of various fault types for reducing the size of inputs for recognition algorithms. Next, multiclass support vector machines achieve identification. The results have proved that the proposed classification technique is independent of fault resistance, source impedance, and fault inception angles. Extensive simulations are conducted using the electromagnetic transients program to show that the recognition accuracy of the fault classification for all types is up to 96.84%. [ABSTRACT FROM AUTHOR]

Reliability is an important performance for permanent magnet synchronous machine (PMSM) exploited to industrial applications and electric‐energy vehicles. Both air‐eccentricity and inter‐turn short circuit (ITSC) could induce the machine running in unbalanced status. Once not restored timely, the fault will lead to the whole drive system and its application damage. To enhance the reliability for PMSM, an effective fault diagnosis and classification method is necessary in advance. For this purpose, an improved exponential discriminant analysis (IEDA) algorithm is proposed and optimized. Further, the distortion characteristics of phase current signals with air‐eccentricity and ITSC are extracted and trained by the IEDA, which are applied for diagnosing whether the motor healthy or faulty and distinguishing fault types. Besides, the IEDA can not only identify fault type accurately but also estimate the fault degree precisely. Finally, compared to other common learning methods, several simulations and practical tests accompanying with diverse operating status are implemented, and their results can prove the proposed method has more accuracy and robustness than other methods. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Fault diagnosis of bearings using classification techniques plays an important role in industrial applications, and, hence, has received increasing attention. Recently, significant efforts have been made to develop various methods for bearing fault classification and the application of Case Western Reserve University (CWRU) data for validation has become a standard reference to test the fault classification algorithms. However, a systematic research for evaluating bearing fault classification performance using the CWRU data is still lacking. This paper aims to provide a comprehensive benchmark analysis of the CWRU data using various entropy and classification methods. The main contribution of this paper is applying entropy-based fault classification methods to establish a benchmark analysis of entire CWRU datasets, aiming to provide a proper assessment of any new classification methods. Recommendations are provided for the selection of the CWRU data to aid in testing new fault classification algorithms, which will enable the researches to develop and evaluate various diagnostic algorithms. In the end, the comparison results and discussion are reported as a useful baseline for future research. [ABSTRACT FROM AUTHOR]

In order to reduce the instability of manual extraction and screening features in pipeline fault diagnosis, a fault diagnosis method based on deep confidence network to reconstruct feature parameters and model is proposed. Under laboratory conditions, the acoustic emission signals of the pipeline under normal and different fault conditions are collected, the characteristic parameters are extracted, the characteristic parameters are reconstructed by the deep confidence network and the classification model is established, and the number of model nodes is adjusted according to the characteristics of the collected sample data, and the parameter optimization model is obtained after the final diagnosis. The studies have shown that: under the same conditions, using the classification model established after the reconstruction of the depth of belief networks characteristic parameters have better stability and higher accuracy. [ABSTRACT FROM AUTHOR]

Rotating machinery is widely applied in industrial fields. However, it generally operates under tough working conditions, which leads to the weak fault features and renders fault diagnosis more difficult. In this case, an emerging method called sparse representation classification (SRC) is proposed to enhance the fault features and identify the fault status. However, the typical SRC theory fails to consider the locality of the test sample and training sample, and the training set generally contains much redundant information, which may reduce the fault recognition accuracy. Moreover, the time-shift deviation of vibration signal cannot be avoided effectively using a typical SRC model. To overcome the above-mentioned problems, a novel SRC model, i.e., weighted SRC based on dictionary learning (DL-WSRC), is proposed. For the training set, different fault signals are learned based on the improved K-singular value decomposition (K-SVD) algorithm, which can not only adaptively update the whole training set but also reduce redundant information so as to enhance the sample fault features. For the test sample, DL-WSRC selects an accurate time-domain parameter using the K-means clustering algorithm and computes the weighted coefficients according to the parameter distance between the test sample and the training samples. Then, it sparsely represents the test sample by solving a weighted $l_{0}$ -norm problem. The goal of weighting is to pay more attention to the locality of the sample so as to improve the recognition accuracy. Finally, according to the results of sparse representation, the fault status can be identified through the correlation analysis, which can effectively solve the time-shift deviation problem. The effectiveness of the proposed method is validated by the experiments of rotating machinery, and the results indicate that the proposed method realizes fault classification with a high accuracy. [ABSTRACT FROM AUTHOR]

In modern diagnostic approaches, the key step consists in generating the features related to fault type and severity. In fact, the generated features should be able to help the classifier to determine the health condition of the monitored system based on the measured signal. In this paper, in order to make an effective diagnosis about the rolling-element bearing failure, novel generated features that can maintain the physical meaning of the extracted vibration signal, while identifying its relationship to rolling bearing damage, are proposed using a wrapper model. For this purpose, based only on the Most Impulsive Frequency Bands (MIFBs) of the measured vibration signals for many bearing conditions, 33 feature parameters are proposed. Using a wrapper scheme, these parameters can be reduced until a set of them are found improving the efficiency of the diagnostic approach. The effectiveness of the proposed predictive features is analyzed by comparing it with some related works using many testing data for several bearing conditions. The experimental results reveal that the proposed procedure has obtained a high level of accuracy of 99.83%. [ABSTRACT FROM AUTHOR]

Condition monitoring of piston pumps has great significance to ensure the reliability and security of hydraulic systems. However, the complex working conditions of the integrated electromechanical systems make the fault mechanism unclear which is difficult for fault diagnosis by feature matching techniques. In this paper, a novel minimum entropy deconvolution (MED)-based convolutional neural network (CNN) is presented to classify faults in axial piston pumps. Firstly, the collected raw signals are preprocessed using the MED technique. Then, the filtered signals are used to construct training samples and testing samples. Finally, the constructed samples are fed into the CNN to classify the multi-faults of axial piston pumps. With the convolution and subsampling operations, the present model can automatically obtain data features via iterative learning processes, which is suitable for the unknown fault mechanism problems. The learned features are visualized by t-distributed stochastic neighbor embedding technique. A benchmark data simulation of mechanical transmission systems and an experimental data investigation of an axial piston pump are performed to manifest the superiority of the present method by comparing with the traditional CNN. [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]

Today's modern industry has accepted condition monitoring based on intelligent fault diagnosis of rotating machinery systems to provide precision and sustainability. The conventional signal processing methods are less productive due to the involvement of various noises from different sources in the vibration signal, and therefore, recently fault diagnosis approaches use the artificial intelligent techniques along with the signal processing methods. Thus, motivated by the kurtogram and convolutional neural network (CNN), a novel efficient method for the fault classification of rotating machines is proposed in this paper. Kurtogram is a measure of dispersion for time–frequency energy density which provides additional frequency contents information and as an effect represents a pattern of each fault uniquely. Thus, it inspires to utilize the kurtogram as an input feature vector, and it helps in reducing the task of identifying dominant features to represent the different faults. Hence, this 2D distinct feature vector presented to CNN for fault classification. The various levels of kurtogram are examined by tuning different hyperparameters of CNN to achieve a good feature set for decent performance. The experimental results demonstrate that the proposed method effectively classifies the bearing faults under different operating conditions in comparison to other methods. [ABSTRACT FROM AUTHOR]

In this study, a novel intelligent relaying scheme is proposed for fault classification and faulted phase selection using magnitude of differential power (MODP). The DP is the multiplication of difference between the magnitude of the real and estimated voltage and current phasors. The MODP for each phase is used as an input data to the data mining model known as decision tree (DT). The DT is used to generate thresholds to detect and classify the faults, since determination of robust thresholds is an important challenge in protective relay engineering. The proposed method has been tested for different operating modes of single‐circuit transmission line in Simulink/MATLAB software. Simulation results indicate that the proposed method is able to detect and classify the faults with acceptable accuracy, in less than half cycle. [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]

Highlights • Shaded PV cells degrade the performance of PV systems. • I-V curves contain useful information for health monitoring. • Data driven method for fault diagnosis can deal with non-controlled solar irradiance. • Principal component analysis has proven its efficacy under real working conditions. Abstract Health monitoring and diagnosis of photovoltaic (PV) systems is becoming crucial to maximise the power production, increase the reliability and life service of PV power plants. Operating under faulty conditions, in particular under shading, PV plants have remarkable shape of current-voltage (I-V) characteristics in comparison to reference condition (healthy operation). Based on real electrical measurements (I-V), the present work aims to provide a very simple, robust and low cost Fault Detection and Classification (FDC) method for PV shading faults. At first, we extract the features for different experimental tests under healthy and shading conditions to build the database. The features are then analysed using Principal Component Analysis (PCA). The accuracy of the data classification into the PCA space is evaluated using the confusion matrix as a metric of class separability. The results using experimental data of a 250 Wp PV module are very promising with a successful classification rate higher than 97% with four different configurations. The method is also cost effective as it uses only electrical measurements that are already available. No additional sensors are required. [ABSTRACT FROM AUTHOR]

This study deals with the faults' detection and classification in AC transmission lines based on power spectral density (PSD), introducing PSD in time and frequency for analysing transient information under faulted conditions. The discrete wavelet transform is used for scaling current signals in time–frequency at different decomposition levels by approximation and detail coefficients. Then, a wavelet‐covariance matrix is shaped with the aim to obtain its PSD, being this the key to detect and classifying faults. Results show that the proposed method attains the detection in a short time and the classification is accomplished via the Hellinger distance, whose straightforward implementation is carried out in this study. The classification process is compared adopting different classifiers to cope with a set of signals in a time frame. Finally, the proposal is extensively assessed using real and simulated signals stemming from multiple fault cases of radial and interconnected power grids. [ABSTRACT FROM AUTHOR]

In order to simplify the process of analogue circuit fault diagnosis under the premise of improving the fault diagnosis rate of analogue circuit, and to deeply mine the fault characteristics of the output signal, a fault diagnosis method based on convolutional neural network (CNN) is proposed. The output signals in different fault states are directly input into CNN for fault feature extraction and fault classification. By optimising the CNN model and its parameters, the 100% fault diagnosis rate of Sallen‐Key circuit can be achieved. The experimental results indicate that the CNN‐based analogue circuit fault diagnosis method simplifies the fault diagnosis process and improves the fault diagnosis rate. [ABSTRACT FROM AUTHOR]

Bearings are one of the most omnipresent and vulnerable components in rotary machinery such as motors, generators, gearboxes, or wind turbines. The consequences of a bearing fault range from production losses to critical safety issues. To mitigate these consequences condition based maintenance is gaining momentum. This is based on a variety of fault diagnosis techniques where fuzzy clustering plays an important role as it can be used in fault detection, classification, and prognosis. A variety of clustering algorithms have been proposed and applied in this context. However, when the extensive literature on this topic is investigated, it is not clear which clustering algorithm is the most suitable, if any. In an attempt to bridge this gap, in this study four representative fuzzy clustering algorithms are compared under the same experimental realistic conditions: fuzzy c-means (FCM), the Gustafson-Kessel algorithm, FN-DBSCAN, and FCMFP. The study considers only real-world bearing vibration data coming from both a benchmark data set (CWRU) and from a lab setup where interference between bearing faults can be studied. The comparison takes into account the quality of the generated partitions measured by the external quality (Rand and Adjusted Rand) indexes. The conclusions of the study are grounded in statistical tests of hypotheses. [ABSTRACT FROM AUTHOR]

Vibration signal of gearbox systems carries the important dynamic information for fault diagnosis. However, vibration signals always show non stationary behavior and overwhelmed by a large amount of noise make this task challenging in many cases. Thus, a new fault diagnosis method combining the Hilbert empirical wavelet transform (HEWT), the singular value decomposition (SVD) and Elman neural network is proposed in this paper. Vibration signals of normal gear, gear with tooth root crack, gear with chipped tooth in width, gear with chipped tooth in length, gear with missing tooth and gear with general surface wear are collected in different speed and load conditions. HEWT, a new self-adaptive time-frequency analysis, was applied to the vibration signals to obtain the instantaneous amplitude matrices. Singular value vectors, as the fault feature vectors were then acquired by applying the SVD. Last, the Elman neural network was used for automatic gearbox fault identification and classification. Through experimental results, it was concluded that the proposed method can accurately extract and classify the gear fault features under variable conditions. Moreover, the performance of the proposed HEWT-SVD method has an advantage over that of Hilbert-Huang transform (HHT)-SVD, local mean decomposition (LMD)-SVD or wavelet packet transform (WPT)-PCA for feature extraction. [ABSTRACT FROM AUTHOR]

Faults frequently occur in transmission lines and become a major issue in power system engineering. It is an unavoidable incident and leads to many problems such as failure of equipment, instability in power flow and economical losses. Protection relay is a device installed in transmission lines to perform detection, classification and estimation of fault. The task must be performed accurately and fast to isolate the faulted phase and protect the system from the harmful effects of the fault. Fault classification involves the process of identifying the type of fault in the transmission lines. The presence of ground fault in three-phase faults has caused difficulty in identifying the fault types. This is due to the input signals that only contain three-phase currents/voltages and influenced by noise. Recent studies show that ANNs are powerful tools for fault classification. However, most studies utilized single ANN to classify all the faults, even though not all fault classes are equally difficult. This paper proposes a robust fault classification scheme using wavelet transform (WT) and 2-Tier multilayer perceptron (MLP) network. The first-Tier MLP network (MLP 1) is used to classify the three-phase fault (A, B and C), and the second-Tier MLP network (MLP 2) is used to identify the presence of ground fault. A new WT-based feature that properly describe the presence of ground fault called class-dependent feature (CDF) is formulated. The CDF is determined from the correlation between the output of MLP 1, wavelet mean and energy features. The CDF is fed into the MLP 2 and used to determine the presence of ground fault. Comparison performance with different MLP network structures indicated that the proposed method showed good classification accuracy. The average accuracy of CDF and 2-Tier MLP network for three different datasets, ideal (no noise), 20 and 30 dB, shows the highest with 99.36% as compared to other structures. [ABSTRACT FROM AUTHOR]

In this study, a novel algorithm for detecting and classifying faults in transmission lines is proposed. The algorithm is based on mathematical morphology and initial current travelling waves. A new morphological edge detection (MED) filter to extract the transient features from the original fault signal is designed. This MED filter can fast and accurately detect the arrival time and polarity of travelling waves in all conditions. The appropriate criteria of fault classification and faulted‐phase selection are introduced based on polarity of initial current travelling waves. The simulations based on the electromagnetic transients program and MATLAB have been done to evaluate the validity of the proposed algorithm. [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]

Condition monitoring and fault diagnosis are important for maintaining the system performance and guaranteeing the operational safety. The traditional data‐driven approaches mostly incorporate well‐defined features and methodologies such as supervised artificial intelligence algorithms. Prior knowledge of possible features and a large quantity of labelled condition data are needed. Besides, many traditional approaches require rebuilding or a retraining of the original model to diagnosis new conditions. The present study proposes an intelligent fault diagnosis approach that uses a deep neural network (DNN) based on stacked denoising autoencoder. Representative features are learned by applying the denoising autoencoder to the unlabelled data in an unsupervised manner. A DNN is then constructed and fine‐tuned with just a few items of labelled data. The trained DNN achieves high performance in fault classification. Furthermore, new conditions can be correctly classified by simply fine‐tuning the trained DNN model using a small amount of labelled data under the new conditions. The effectiveness of the proposed approach is evaluated using a case study of fault diagnosis of a bearing unit. The results indicate that the proposed method can extract representative features from massive unlabelled data on the system condition and achieve high performance in fault diagnosis. [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]

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]

This paper presents a complete protection scheme for detecting, classifying and locating the fault in HVDC transmission lines using support vector machines (SVM). SVM has been used for protective relaying application in HVAC transmission line, however very limited works have been reported for HVDC transmission line. In this work, a ±500 kV HVDC transmission system is developed in PSCAD/EMTDC and the measurement signals obtained are analyzed in MATLAB. The rectifier side AC RMS voltage, DC voltage and current on both the poles are continuously monitored, and given as input to the SVM binary classifier in order to detect the presence of fault in the line. Once a fault is detected, the SVM multi‐class classification module predicts the type of fault and the SVM regression algorithm predicts the location of fault. The feature vector used in the classification and location modules is the standard deviation of the signals over half cycle before and after the occurrence of fault. The method proposed is simple as it requires single‐end data and a direct standard deviation of one cycle data gives very accurate results. The detection and classification modules are 100% accurate whereas the fault location module has a mean error of 0.03%. [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]

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]

• 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]