Motor Fault Diagnosis Research Articles

Back Propagation Neural Network-Based Fault Diagnosis and Fault Tolerant Control of Distributed Drive Electric Vehicles Based on Sliding Mode Control-Based Direct Yaw Moment Control

Distributed-drive vehicles utilize independent drive motors on the four-wheel hubs. The working conditions of the wheel-hub motors are so harsh that the motors are prone to failing under different driving conditions. This study addresses the impact of drive motor faults on vehicle performance, particularly on slippery roads where sudden faults can lead to accidents. A fault-tolerant control system integrating motor fault diagnosis and a direct yaw moment control (DYC) based fault-tolerant controller are proposed to ensure the stability of the vehicle during various motor faults. Due to the difficulty of identifying the parameters of the popular permanent magnet synchronous wheel hub motors (PMSMs), the system employs a model-free backpropagation neural network (BPNN)-based fault detector. Turn-to-turn short circuits, open-phase faults, and diamagnetic faults are considered in this research. The fault detector is trained offline and utilizes rotor speed and phase currents for online fault detection. The system assigns the torque outputs from both healthy and faulted motors based on fault categories using sliding mode control (SMC)-based DYC. Simulations with four-wheel electric vehicle models demonstrate the accuracy of the fault detector and the effectiveness of the fault-tolerant controller. The proposed system is prospective and has potential for the development of distributed electric vehicles.

A Novel Electric Motor Fault Diagnosis by Using a Convolutional Neural Network, Normalized Thermal Images and Few-Shot Learning

Motors constitute one critical part of industrial production and everyday life. The effective, timely and convenient diagnosis of motor faults is constantly required to ensure continuous and reliable operations. Infrared imaging technology, a non-invasive industrial fault diagnosis method, is usually applied to detect the equipment status in extreme environments. However, conventional Infrared thermal images inevitably show a large amount of noise interference, which affects the analysis results. In addition, each motor may only possess a small amount of fault data in practice, as collecting an infinite amount of motor data to train the diagnostic system is impossible. To overcome these problems, a novel automatic fault diagnosis system is proposed in this study. Data features are enhanced by a normalization module based on color bars first, as the same color in various infrared thermal images represent different temperatures. Then, the few-shot learning method is used to diagnose the faults of unseen electric motors. In the few-shot learning method, the minimum dataset size required to expand system universality is fifteen pieces, effectively solving the universality problem of artificial-to-natural data migration. The method saves a large amount of training data resources and the experimental training data collection. The accuracy of the fault diagnosis system achieved 98.9% on similar motor datasets and 91.8% on the dataset of motors that varied a lot from the training motor, which proves the high reliability and universality of the system.

Harnessing attention mechanisms in a comprehensive deep learning approach for induction motor fault diagnosis using raw electrical signals

Induction motors are widely used in various industrial applications due to their simplicity, robustness, and high efficiency. In recent years, deep learning approaches have shown great potential for detecting and diagnosing faults in induction motors. However, it was observed that the convolutional neural network (CNN) might miss the crucial long-range temporal dependencies of the significant features. At the same time, the recursive neural network (RNN) may face issues such as vanishing gradient and less computational efficiency if the data is too long with complex patterns. This paper proposes a novel deep-learning model that combines a one-dimensional CNN and an RNN into two independent pipelines. The spatial, temporal, long-term, and short-term features can be extracted from the raw input signal. A multi-head mechanism, a state-of-the-art deep learning technique in natural language processing (NLP), enables the model to focus more on relevant features and prevent overfitting. Specifically, our method simplifies the conventional fault diagnosis workflow while maintaining competitive accuracy compared to several state-of-the-art methods. By leveraging the ability to detect faults directly from raw, unprocessed electrical signals, our approach offers a practical, cost-effective solution for real-time fault detection in industrial applications. This innovation has the potential to significantly reduce downtime and the associated operational costs, thereby providing immediate practical value in various industrial settings.

Stator Turn Fault Diagnosis and Severity Assessment in Converter-Fed Induction Motor Using Flat Diagnosis Structure Based on Deep Learning Approach

This work aims to explore the hybrid deep learning techniques for early interturn fault diagnosis and fault severity assessment in variable frequency induction motor drive. However, the existing literature mostly focuses on identifying the fault patterns, whereas the assessment of fault deterioration before it reaches the extreme degradation state has been less investigated. In this paper, a hybrid structure combining two-dimensional convolution neural network (2DCNN) and long-short term memory/gated recurrent unit (LSTM/GRU) has been modelled as a relatively lightweight flat diagnosis structure (FDS) for early fault identification and its severity assessment simultaneously in converter-fed induction motor. Initially, the hierarchical layers of 2DCNN extract the effective features automatically for fault detection and its severity identification simultaneously. Then, these features are presented as suitable inputs to LSTM/GRU for classification. A comparative evaluation of the proposed hybrid method with the standalone structure, namely 2DCNN, LSTM and SVM proves the advantages of the proposed scheme, and its applicability for both fault diagnosis and fault severity assessment in industrial drive systems, under variable frequencies and load variations.

Bearing fault diagnosis of rail vehicles traction motor based on adaptive periodic segment matrix and singular value decomposition

The early fault characteristics of rolling bearings are relatively weak and accompanied by large amounts of noise, resulting in the collected bearing vibration signal having an extremely low signal-to-noise ratio, hence creating challenges in diagnosing faults in rail vehicle bearings. This paper proposes a method based on an adaptive period segment matrix and singular value decomposition to extract fault information from bearing fault impulse signals, in response to the problem of unstable period recognition in existing rolling bearing fault diagnosis methods due to strong background noise. Firstly, a periodic segment estimation method based on the average envelope spectrum is proposed. By calculating the envelope spectrum of the vibration signal, a method is proposed for assessing period length and period constraint F in segments. Then this method is applied to pinpoint the fault impact peak and the position of the vibration signal. Utilizing the fault impact peak and the calculated period constraint F, a strict rank-1 adaptive period segment matrix (APSM) is introduced. Finally, singular value decomposition is performed on this matrix, the bearing impulse vibration signal can be reconstructed through the first maximum singular value. The simulation and experimental analysis results of rolling bearings signals demonstrate the performance of the proposed method, even under severe background noise conditions(−20 dB), and the calculated envelope spectrum signal-to-noise ratio further supports the adaptive periodic segment matrix has an excellent ability to identify and extract periodic components, thus validating its usefulness as a fault diagnosis approach for rolling bearings.

Analysis of Inter-Turn Short-Circuit Faults in Brushless DC Motors Based on Magnetic Leakage Flux and Back Propagation Neural Network

Inter-turn short-circuit (ITSC) faults are the most common fault types of brushless DC (BLDC) motors used in industry. Fault diagnosis of BLDC motors is of critical importance due to their wide spread applications. Existing diagnosis methods are based on voltage and current analysis which is useful but difficult to identify early faults and fault locations, as these suffer from external disturbances. This paper proposes a new offline method for fault detection based on magnetic leakage flux (MLF) and backpropagation neural network (BPNN) for improving the level of fault diagnosis. The ITSC and MLF are modeled and analyzed theoretically. Then they are verified by finite element analysis (FEM) and experimental tests. Hall sensors are used to form an array to collect MLF signals at different positions outside the test motor. The frequency-domain characteristic matrix of MLF signals is analyzed by BPNN models. The experimental results show that the proposed method can effectively detect ITSCs, and estimate the fault degree and the location of the fault. The method is a promising technology as it is non-intrusive and accurate.

Fault diagnosis of brushless DC motor based on Stack Sparse Autoencoder

Because of their simple structure, long service life, high efficiency, etc., brushless DC (BLDC) motors have been widely applied in many fields. In some applications, high requirements for BLDC continuous use of motors, so often to BLDC running state monitoring of motors, realizes the early fault diagnosis, to improve the reliability, safety, and prolonged use. To solve this problem, a BLDC fault diagnosis method based on Fast Fourier Transform (FFT), Stacked Sparse Auto-encoder (SSAE), and soft classifier was proposed. In the laboratory, a BLDC model R-3525 was used as the experimental equipment, and the data of 9 kinds of fault conditions such as bearing damage, cage damage, inner and outer ring damage, and the normal condition of the motor were collected. The collected data are transformed into 28 × 28 two-dimensional data sets by FFT. The feature expressions of various faults are adaptively learned from many data, and the intelligent diagnosis of the motor is realized by the feature function expression. The experimental results demonstrate that, in comparison to the single neural network approach, the stacked method can significantly enhance the precision of fault classification and achieve fault diagnosis for brushless DC motors. This method carries valuable implications for other varieties of BLDC motors.

A Residual Multihead Self-Attention Network Using Multimodal Shallow Feature Fusion for Motor Fault Diagnosis

A Residual Multihead Self-Attention Network Using Multimodal Shallow Feature Fusion for Motor Fault Diagnosis

Algorithm for diagnosing and warning of vibration caused by loosened screws on motor base using wavelet packet and neural network

Motor base screw loosening is a common problem in motor operation, which, if not dealt with in time, may lead to motor failure and damage. However, few studies have focused on the diagnosis and warning of this problem. Based on wavelet packet and neural network analysis, this paper presents a new algorithm for monitoring, diagnosing, and warning vibration caused by loose screws in the motor base. The vibration signal generated by the base screw loosening is monitored and sampled with sensors, and the wavelet packet is used to decompose, reconstruct, and reduce the noise of the vibration signal to enhance the time–frequency characteristics of the signal. After analyzing the fault data by wavelet, the feature vector characterizing the fault is extracted, and then, the vector and the corresponding fault type are used as the input and output of the neural network, respectively, and the non-mapping relationship between them is built to complete the diagnosis and early warning of the fault. Finally, the method is used to compare the motor base screw loosening operation and normal operation. The experiments show that the new algorithm based on wavelet packet and neural network can complete the health diagnosis and early warning of motor in the early stage of motor base screw loosening, reduce the loss caused by subsequent faults, and provide a new reference scheme for the motor fault diagnosis field.

Research on Three Common Fault Diagnosis Methods for AC Asynchronous Motors Based on Deep Learning

<p>In response to the problem that traditional fault diagnosis methods mainly rely on manual search, this paper proposes an improved convolutional neural network based three item asynchronous motor fault diagnosis method. Taking the motor rotor bar fault as the research object, in the early stage of the fault, the characteristic signal is easily mixed with the motor fundamental frequency signal. Therefore, first, the current characteristics of the motor rotor bar fault are analyzed, and then the motor vibration signal is converted into a time-frequency map using wavelet analysis method. Then, based on the superpixel segmentation method, the image is generated into a superpixel block. Finally, the image information is input into an improved neural network, The improved neural network can adaptively extract fault features. The experimental results show that the method described in this article can improve the diagnostic ability for rotor bar breaking faults, and has a higher fault recognition accuracy compared to traditional methods.</p> <p> </p>

Artificial Intelligence to detection fault on three phase squirrel cage induction motors subjected to broken bar fault

Induction motors are widely used in various industrial applications due to their robustness, reliability, and low cost. However, they are also prone to various types of faults, such as broken rotor bars, bearing defects, stator winding faults, and eccentricity. These faults can cause performance degradation, energy loss, and even catastrophic failures if not detected and diagnosed in time. Therefore, condition monitoring and fault diagnosis of induction motors are essential for ensuring their safe and efficient operation. In this paper, we propose a novel fault diagnosis method for induction motors based on artificial intelligence, peak variation response (PVR), park vector approach (PVA), and standard deviation (SD). The proposed method consists of four steps: · Data acquisition and preprocessing, · Feature extraction using pvr and pva, · Feature selection using sd, and · Fault classification using artificial neural networks. The PVR and PVA are used to extract the amplitude and phase information of the stator current signals under different load conditions and fault types. The SD is used to select the most relevant features for fault diagnosis. The ANNs are used to classify the faults based on the selected features. The proposed method is validated by experimental results on a 1.5 kW three-phase induction motor with various simulated faults. The results show that the proposed method can effectively diagnose different types of faults with high accuracy and robustness.

Artificial Intelligence to detection fault on three phase squirrel cage induction motors subjected to stator winding fault

Induction motors are widely used in various industrial applications due to their robustness, reliability, and low cost. However, they are also prone to various types of faults, such as broken rotor bars, bearing defects, stator winding faults, and eccentricity. These faults can cause performance degradation, energy loss, and even catastrophic failures if not detected and diagnosed in time. Therefore, condition monitoring and fault diagnosis of induction motors are essential for ensuring their safe and efficient operation. In this paper, we propose a novel fault diagnosis method for induction motors based on artificial intelligence, peak variation response (PVR), park vector approach (PVA), and standard deviation (SD). The proposed method consists of four steps: · Data acquisition and preprocessing, · Feature extraction using pvr and pva, · Feature selection using sd, and · Fault classification using artificial neural networks. The PVR and PVA are used to extract the amplitude and phase information of the stator current signals under different load conditions and fault types. The SD is used to select the most relevant features for fault diagnosis. The ANNs are used to classify the faults based on the selected features. The proposed method is validated by experimental results on a 1.5 kW three-phase induction motor with various simulated faults. The results show that the proposed method can effectively diagnose different types of faults with high accuracy and robustness.

Stator-rotor fault diagnosis of induction motor based on time-frequency domain feature extraction

Since the induction motor operates in a complex environment, making the stator and rotor of the motor susceptible to damage, which would have significant impact on the whole system, efficient diagnostic methods are necessary to minimize the risk of failure. However, traditional fault diagnosis methods have limited applicability and accuracy in diagnosing various types of stator and rotor faults. To address this issue, this paper proposes a stator-rotor fault diagnosis model based on time-frequency domain feature extraction and Extreme Learning Machine (ELM) optimized with Golden Jackal Optimization (GJO) to achieve highprecision diagnosis of motor faults. The proposed method first establishes a platform for acquiring induction motor stator-rotor fault data. Next, wavelet threshold denoising is used to pre-process the fault current signal data, followed by feature extraction to perform time-frequency domain eigenvalue analysis. By comparison, the impulse factor is finally adopted as the feature vector of the diagnostic model. Finally, an induction motor fault diagnosis model is constructed by using the GJO to optimize the ELM. The resulting simulations are carried out by comparing with neural networks, and the results show that the proposed GJO-ELM model has the highest diagnostic accuracy of 94.5%. This finding indicates that the proposed method outperforms traditional methods in feature learning and classification of induction motor fault diagnosis, and has certain engineering application value.

Self-driven continual learning for class-added motor fault diagnosis based on unseen fault detector and propensity distillation

Continual learning is a high-potential technique that enables intelligent motor fault diagnosis models to extend new diagnosable fault classes without costly training from scratch. However, existing continual learning methods have the following limitations. (1) They manually detect new faults, which is labor-intensive, untimely, and more importantly, may lead to mistaken diagnosis results. (2) They adopt the traditional knowledge distillation to align the absolute responses of old and new models, which alleviates catastrophic forgetting but restricts flexible learning from incremental datasets. To overcome the above limitations, this paper proposes a novel self-driven continual learning framework for class-added motor fault diagnosis, which can spontaneously detect unseen faults and perform more flexible continual learning from incremental datasets. For the automatic detection of unseen faults, after collecting online samples, adversarial training with exemplars of each seen class is conducted to measure the class separability. The truth fault classes that are unseen for diagnosis models can be clearly distinguished from all seen classes, and correspondingly missed diagnosis or misdiagnosing can be avoided effectively and incremental samples with new fault types can be collected quickly. For the flexible continual learning strategy, a more flexible knowledge distillation is proposed to preserve the prediction propensity rather than the absolute response. This strategy not only keeps the recognition performance of old classes but also loosens unnecessary constraints and increases the diagnosis model plasticity to learn new knowledge from incremental datasets, thus improving the accuracy of motor fault diagnosis during continual learning. The effectiveness of the proposed method is verified by conducting fault simulation experiments of three-phase motors and its superiority is also demonstrated by comparing it with some state-of-the-art diagnosis methods.

ANN-based fault diagnosis of induction motor under stator inter-turn short-circuits and unbalanced supply voltage

Induction motors (IMs) are extensively used in industrial sector. This kind of machine is subjected to several stresses that could interrupt their normal operation. An excessive stress can generate some symptoms before the IM fall in failure situation. Therefore, incipient detection of these symptoms permits the shutdown of IMs in order to avoid total destruction. Fault detection is then the main objective of diagnosis systems. Stator inter-turn short-circuits (SITSC) constitutes an important amount of cause of IM breakdown. However; unbalance supply voltage (USV) is one of the advantageous factors that affect IMs operation. Thus, in order to avoid false alarm induced by USV, the diagnosis system must make difference between USV and SITSC faults. This paper presents an efficient approach to estimate SITSC percentage and detect USV occurrence using Artificial Intelligent (AI) tool. Artificial neuronal network (ANN) plays the key-role of the proposed diagnosis system. A fault Classifier of SITSC and USV is carried out using multi-layer perceptron neuronal network (MLP-NN). The training, testing and validation phases of MLP-NN need the dataset creation. The required data is obtained from both simulated mathematical model of IM and laboratory test-bed. The reached results show the sensitivity and the well-functioning of the proposed diagnosis system.

Fault Diagnosis of Three-Phase Induction Motors Using Convolutional Neural Networks

Fault Diagnosis of Three-Phase Induction Motors Using Convolutional Neural Networks

An Improved Direct Torque Control with an Advanced Broken-Bar Fault Diagnosis for Induction Motor Drives

This paper presents an advanced strategy combining fuzzy logic and artificial neural networks (ANNs) for direct torque control (DTC) and broken-bar fault diagnosis in induction motors. More specifically, a fuzzy-based controller is used to simultaneously minimize the stator flux and the electromagnetic torque ripples. A neural switching table is then proposed to achieve the interface inverter control. Besides, a closed-loop broken-bar fault detection strategy based on the Hilbert technique (HT) with the discrete wavelet transform (DWT) and ANNs is proposed. The fault detection is performed by analyzing the induction motor’s stator current by using the combined techniques HT-DWT. The effect of a broken-bar fault on the machine varies according to the number and position of the broken bars. The neural detector was used in order to identify the number of broken bars through only one current measurement. The effectiveness of the developed control has been verified using MATLAB/Simulink and real-time simulation in OPAL-RT 4510. Obtained results show improved performances in terms of torque ripple minimization and stator current quality, evaluated, respectively, at 43.75% and 41.26% as well as a rigorous motor health monitoring.

Induction Motor Fault Diagnosis with Local Ternary Pattern and AI Approaches

Induction Motor Fault Diagnosis with Local Ternary Pattern and AI Approaches

Research on Three-Phase Asynchronous Motor Fault Diagnosis Based on Multiscale Weibull Dispersion Entropy.

Three-phase asynchronous motors have a wide range of applications in the machinery industry and fault diagnosis aids in the healthy operation of a motor. In order to improve the accuracy and generalization of fault diagnosis in three-phase asynchronous motors, this paper proposes a three-phase asynchronous motor fault diagnosis method based on the combination of multiscale Weibull dispersive entropy (WB-MDE) and particle swarm optimization-support vector machine (PSO-SVM). Firstly, the Weibull distribution (WB) is used to linearize and smooth the vibration signals to obtain sharper information about the motor state. Secondly, the quantitative features of the regularity and orderliness of a given sequence are extracted using multiscale dispersion entropy (MDE). Then, a support vector machine (SVM) is used to construct a classifier, the parameters are optimized via the particle swarm optimization (PSO) algorithm, and the extracted feature vectors are fed into the optimized SVM model for classification and recognition. Finally, the accuracy and generalization of the model proposed in this paper are tested by adding raw data with Gaussian white noise with different signal-to-noise ratios and the CHIST-ERA SOON public dataset. This paper builds a three-phase asynchronous motor vibration signal experimental platform, through a piezoelectric acceleration sensor to discern the four states of the motor data, to verify the effectiveness of the proposed method. The accuracy of the collected data using the WB-MDE method proposed in this paper for feature extraction and the extracted features using the optimization of the PSO-SVM method for fault classification and identification is 100%. Additionally, the proposed model is tested for noise resistance and generalization. Finally, the superiority of the present method is verified through experiments as well as noise immunity and generalization tests.

Optimizing and Analyzing Performance of Motor Fault Diagnosis Algorithms for Autonomous Vehicles via Cross-Domain Data Fusion

Electric motors play a pivotal role in the functioning of autonomous vehicles, necessitating accurate fault diagnosis to ensure vehicle safety and reliability. In this paper, a novel motor fault diagnosis approach grounded in vibration signals to enhance fault detection performance is presented. The method involves capturing vibration signals from the motor across various operational states and frequencies using vibration sensors. Subsequently, the signals undergo transformation into frequency domain representations through fast Fourier transform. This includes normalizing and concatenating the amplitude frequency and phase frequency signals into comprehensive frequency domain information. Leveraging Gramian image-encoding attributes, cross-domain fusion of time-domain and frequency-domain data is achieved. Finally, the fused Gram angle field map is fed into the ConvMixer deep learning model, augmented by the ECA mechanism to facilitate precise motor fault identification. Experimental outcomes underscore the efficacy of cross-domain data fusion, showcasing improved pattern recognition and recognition rates for the models compared to traditional time-domain methods. Additionally, a comparative analysis of various deep learning models highlights the superior performance of the ECA-ConvMixer model. This study makes significant contributions by introducing a cross-domain data fusion method, merging time-domain and frequency-domain information to enhance motor vibration signal analysis. Additionally, the incorporation of the ECA-ConvMixer deep learning model, equipped with attention mechanisms, effectively captures critical features, thus serving as a robust tool for motor fault diagnosis. These innovations not only enhance diagnostic accuracy but also have broad applications in areas like autonomous vehicles and industry, leading to reduced maintenance expenses and enhanced equipment reliability.