Abstract
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.
Highlights
Condition monitoring is described as a continuous process of diagnosis that allows prevention of unintended failure of a system. e basic principle of condition monitoring is to indicate the occurrence of deterioration by taking physical measurements at regular intervals
Compared to machine learning (ML) models, deep learning (DL) models can achieve superior performance and their classification accuracies have been tending to 100%. ese benefits of DL models have attracted the attention of researchers and they have been extensively applying these models in their domains
Sun et al [57] have employed sparse autoencoders (SAE) with DNN for unsupervised feature extraction. e model was fed with vibration data to classify different faults of the induction motor. ey employed the “dropout” regularization method to avoid overfitting during the training process. e SAE remained inactive during the testing process. e results confirmed that the approach provided better performance with 97.6% accuracy compared to conventional models such as support vector machine (SVM) and linear regression (LR), which achieved 96.4% and 92.7% accuracy, respectively
Summary
Condition monitoring is described as a continuous process of diagnosis that allows prevention of unintended failure of a system. e basic principle of condition monitoring is to indicate the occurrence of deterioration by taking physical measurements at regular intervals. DL-based diagnosis and prognosis methods have outperformed conventional machine learning algorithms owing to their generalized nature and many other advantages such as end-to-end implementation, model upgradability, and representation learning using raw data. It does not require human knowledge or intervention in feature designing.
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