A Light Weight Multisensory Fusion Model for Induction Motor Fault Diagnosis

Wang, Jinjiang; Fu, Peilun; Ji, Shuaihang; Li, Yilin; Gao, Robert X.

doi:10.1109/tmech.2022.3169143

Cited by 29 publications

(6 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In response to the computational challenges hindering the application of deep learning models in fault diagnosis, Wang et al introduced a lightweight multisensory fusion model. This model, designed for induction motor data fusion and diagnosis, outperforms other neural networks by accurately predicting fault modes in a shorter time frame [8].…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Electric vehicle motor fault diagnosis using improved wavelet packet decomposition and particle swarm optimization algorithm

Zheng,

Wang

2024

Archives of Electrical Engineering

View full text Add to dashboard Cite

This study addresses the issue of diagnosing faults in electric vehicle motors and presents a method utilizing Improved Wavelet Packet Decomposition (IWPD) combined with particle swarm optimization (PSO). Initially, the analysis focuses on common demagnetization faults, inter turn short circuit faults, and eccentricity faults of permanent magnet synchronous motors. The proposed approach involves the application of IWPD for extracting signal feature vectors, incorporating the energy spectrum scale, and extracting the feature vectors of the signal using the energy spectrum scale. Subsequently, a binary particle swarm optimization algorithm is employed to formulate strategies for updating particle velocity and position. Further optimization of the binary particle swarm algorithm using chaos theory and the simulated annealing algorithm results in the development of a motor fault diagnosis model based on the enhanced particle swarm optimization algorithm. The results demonstrate that the chaotic simulated annealing algorithm achieves the highest accuracy and recall rates, at 0.96 and 0.92, respectively. The model exhibits the highest fault accuracy rates on both the test and training sets, exceeding 98.2%, with a minimal loss function of 0.0035. Following extraction of fault signal feature vectors, the optimal fitness reaches 97.4%. In summary, the model constructed in this study demonstrates effective application in detecting faults in electric vehicle motors, holding significant implications for the advancement of the electric vehicle industry.

show abstract

Section: Related Workmentioning

confidence: 99%

“…The energy spectrum scale is utilized for extracting the feature vector of the signal, and the method is to analyze the existing differences, as illustrated in Eq. (8).…”

Section: Wpd and Feature Vector Extractingmentioning

confidence: 99%

Electric vehicle motor fault diagnosis using improved wavelet packet decomposition and particle swarm optimization algorithm

Zheng,

Wang

2024

Archives of Electrical Engineering

View full text Add to dashboard Cite

show abstract

“…Further, these diagnostic techniques are less effective in dealing with dynamic and real-time work situations (Wang et al, 2022). So, there is a constant need for a diagnostic system that can diagnose induction motor faults under varying working conditions.…”

Section: Introductionmentioning

confidence: 99%

Nature‐inspired artificial bee colony‐based hyperparameter optimization of CNN for anomaly detection in induction motor

et al. 2023

View full text Add to dashboard Cite

The Induction Motor (IM) is one of the most frequently used prime movers in most industrial and transportation systems. The motor's stable and safe operation directly influences the secure and reliable operation of such prime movers. Developing an intelligent fault diagnosis system for such motors is very significant. This paper presents an intelligent fault diagnosis method based on the improved functionality of a Convolutional Neural Network (CNN) through its hyperparameter optimization using a nature‐inspired Artificial Bee Colony Optimization (ABCO) algorithm. The proposed diagnostic method introduces and analyses various possible mechanical and electrical faults in the IM. The validation of the proposed method is presented with three different modalities, including vibration, acoustic, and infrared thermography, with their comparative performance analysis. Vibration and acoustic‐based detection are done with time‐frequency scalograms using Constant Q Transform (CQT), which provides enhanced time resolution for lower and higher frequencies. The obtained result indicates that the infrared thermography‐based anomaly detection outperforms the vibration and acoustic‐based diagnosis with 100% classification accuracy. The results signify the potential to diagnose different mechanical and electrical faults in IM with substantial reliability and robustness.

show abstract

“…This method improved the diagnostic accuracy, however, the high computational cost limited the application of deep learning model in fault diagnosis. Also, a lightweight multi-sensor fusion model for induction motor data fusion and diagnosis was proposed in the literature [15], introducing reverse residual blocks and network architecture search techniques. Compared with other popular neural networks, this method could accurately determine the fault category with a shorter prediction time.…”

Section: Introductionmentioning

confidence: 99%

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

Yi,

Long,

Wang

et al. 2023

Metrology and Measurement Systems

View full text Add to dashboard Cite

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.

show abstract

A Light Weight Multisensory Fusion Model for Induction Motor Fault Diagnosis

Cited by 29 publications

References 25 publications

Electric vehicle motor fault diagnosis using improved wavelet packet decomposition and particle swarm optimization algorithm

Electric vehicle motor fault diagnosis using improved wavelet packet decomposition and particle swarm optimization algorithm

Nature‐inspired artificial bee colony‐based hyperparameter optimization of CNN for anomaly detection in induction motor

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

Contact Info

Product

Resources

About