Fault Diagnosis of Induction Motors Using Recurrence Quantification Analysis and LSTM with Weighted BN

The advent of deep learning (DL) has transformed diagnosis and prognosis techniques in industry. It has allowed tremendous progress in industrial diagnostics, has been playing a pivotal role in maintaining and sustaining Industry 4.0, and is also paving the way for industry 5.0. It has become prevalent in the condition monitoring of industrial subsystems, a prime example being motors. Motors in various applications start deteriorating due to various reasons. Thus, the monitoring of their condition is of prime importance for sustaining the operation and maintaining efficiency. This paper presents a state-of-the-art review of DL-based condition monitoring for motors in terms of input data and feature processing techniques. Particularly, it reviews the application of various input features for the effectiveness of DL models in motor condition monitoring in the sense of what problems are targeted using these feature processing techniques and how they are addressed. Furthermore, it discusses and reviews advances in DL models, DL-based diagnostic methods for motors, hybrid fault diagnostic techniques, points out important open challenges to these models, and signposts the prospective future directions for DL models. This review will assist researchers in identifying research gaps related to feature processing, so that they may effectively contribute toward the implementation of DL models as applied to motor condition monitoring.

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“…Xiao et al [107] have employed LSTM with weighted batch normalization (BN) for detecting different faults in the induction motor.…”

Section: Recurrent Neural Network (Rnn)mentioning

confidence: 99%

The Importance of Feature Processing in Deep-Learning-Based Condition Monitoring of Motors

Soother

Daudpoto

Harris

et al. 2021

Mathematical Problems in Engineering

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“…recurrence plots (RPs) and recurrence quantification analysis (RQA). The proposed method is applied to the analysis of short-time series and has been successfully used in the diagnostics of mechanical systems [24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Effect of Radial Clearance on Ball Bearing’s Dynamics Using a 2-DOF Model

Ambrożkiewicz¹,

Litak²,

Georgiadis³

et al. 2021

Int. j. simul. model.

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In the present work, a method for the analysis of short time intervals in ball bearings is proposed. We study the effect of the internal clearance on the dynamics of ball bearings using recurrence plots and the recurrence quantification approach. In the proposed method, we focused on the analysis of dynamic states generated from the 2-DOF mathematical model, to which a function changing the damping coefficient in the clearance domain was added. Chosen recurrence methods showed the specific dynamic responses by different values of the radial clearance. The effect of self-consistency concerning the damping effect and vibration development is confirmed. The proposed method can be useful for the prediction of variable radial clearance in time by analysis of the acceleration response of ball bearing during its operation.

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“…The prediction model trained on one dataset can be used to predict the relevant target dataset by fine-tuning the TL strategy. In addition, adaptive batch normalization (AdaBN) [22], [23] algorithm can also be used to improve the domain adaptability of the prediction model. Initially, batch normalization (BN) was used to help stochastic gradient descent (SGD) optimization by adjusting the distribution of each layer of the output in the network [24].…”

Section: Introductionmentioning

confidence: 99%

A Bayesian Optimization AdaBN-DCNN Method With Self-Optimized Structure and Hyperparameters for Domain Adaptation Remaining Useful Life Prediction

2020

IEEE Access

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The prediction of remaining useful life (RUL) of mechanical equipment provides a timely understanding of the equipment degradation and is critical for predictive maintenance of the equipment. In recent years, the applications of deep learning (DL) methods to predict equipment RUL have attracted much attention. There are two major challenges when applying the DL methods for RUL prediction: (1) It is difficult to select the prediction model structure and hyperparameters such as network depth, learning rate, batch size, and etc. (2) The developed prediction model is domain dependent, i.e., it can only give good prediction performance in one data domain (one particular type of working conditions and fault modes). In order to meet the challenges, a novel RUL prediction method developed using a deep convolutional neural network (DCNN) combined with Bayesian optimization and adaptive batch normalization (AdaBN) is presented in this paper. The proposed RUL prediction model is validated by the turbofan engine degradation simulation dataset provided by NASA. The prediction results show that the proposed prediction model provides better prediction results than model structures obtained by random search and grid search. The results also show that the domain adaptation capability of the prediction model has been improved. INDEX TERMS Remaining useful life prediction, Bayesian optimization, adaptive batch normalization, domain adaptation.

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Fault Diagnosis of Induction Motors Using Recurrence Quantification Analysis and LSTM with Weighted BN

Cited by 37 publications

References 27 publications

The Importance of Feature Processing in Deep-Learning-Based Condition Monitoring of Motors

The Importance of Feature Processing in Deep-Learning-Based Condition Monitoring of Motors

Effect of Radial Clearance on Ball Bearing’s Dynamics Using a 2-DOF Model

A Bayesian Optimization AdaBN-DCNN Method With Self-Optimized Structure and Hyperparameters for Domain Adaptation Remaining Useful Life Prediction

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