Stacked pruning sparse denoising autoencoder based intelligent fault diagnosis of rolling bearings

Zhu, Haiping; Cheng, Jiaxin; Zhang, Cong; Wu, Jun; Shao, Xinyu

doi:10.1016/j.asoc.2019.106060

Cited by 104 publications

(41 citation statements)

References 22 publications

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“…The autoencoder was used together with an optimized transfer learning algorithm. Similarly, Zhu et al [16] proposed a novel stacked pruning sparse denoising autoencoder for intelligent fault diagnosis of rolling bearings. The method comprised of a fully connected autoencoder network, connecting the optimal features extracted from previous layers to subsequent layers.…”

Section: Related Workmentioning

confidence: 99%

Artificial neural network technique for improving prediction of credit card default: A stacked sparse autoencoder approach

Ebiaredoh-Mienye

Esenogho

Swart

2021

IJECE

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Presently, the use of a credit card has become an integral part of contemporary banking and financial system. Predicting potential credit card defaulters or debtors is a crucial business opportunity for financial institutions. For now, some machine learning methods have been applied to achieve this task. However, with the dynamic and imbalanced nature of credit card default data, it is challenging for classical machine learning algorithms to proffer robust models with optimal performance. Research has shown that the performance of machine learning algorithms can be significantly improved when provided with optimal features. In this paper, we propose an unsupervised feature learning method to improve the performance of various classifiers using a stacked sparse autoencoder (SSAE). The SSAE was optimized to achieve improved performance. The proposed SSAE learned excellent feature representations that were used to train the classifiers. The performance of the proposed approach is compared with an instance where the classifiers were trained using the raw data. Also, a comparison is made with previous scholarly works, and the proposed approach showed superior performance over other methods.

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Section: Related Workmentioning

confidence: 99%

Artificial neural network technique for improving prediction of credit card default: A stacked sparse autoencoder approach

Ebiaredoh-Mienye

Esenogho

Swart

2021

IJECE

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“…Zhu et al [69] have presented a novel DL model called stacked pruning denoising autoencoder (SPDAE) for rolling bearing fault diagnosis. e model reduces information loss by introducing new channels to interconnect the layers.…”

Section: Autoencoders (Ae)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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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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“…On account of the limitations of obtaining running data under all conditions, a new method was developed based on sparse stacked denoising autoencoder (SDAE) and transfer learning and applied to rolling bearing fault diagnosis [33]. Similarly, Zhu et al proposed a new SDAE model with a pruning method for the promotion of training effectiveness [34].…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network in Intelligent Fault Diagnosis Toward Rotatory Machinery

2020

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Rotating machinery is of vital importance in the field of engineering, including aviation and navigation. Its failure will lead to severe loss to personnel safety and the stability of the equipment system. It is a long way to investigate the relevant fault diagnosis method, especially the intelligent fault diagnosis method on the basis of deep learning. In consideration of the limitations of traditional fault diagnosis approaches based on shallow layer network structure, the methods based on deep neural network (DNN) are worthy of thorough exploration. As a common DNN with special structure, deep convolutional neural network is of great concern in intelligent fault diagnosis due to its advantages in processing nonlinear problems. This review will play an emphasis on convolutional neural network (CNN). The basic structure and principle are introduced. The applications of CNN-based fault diagnosis method in rotating machinery are summarized and analyzed. Furthermore, the diagnosis performance and potential mechanism from different CNN methods are discussed. In the end, this review is highlighted on the challenges and the potential key points in research on novel intelligent fault diagnosis strategies. The corresponding analysis and discussion will provide some references and lay the foundation for the investigation in related fields. INDEX TERMS Deep learning, convolutional neural network, intelligent fault diagnosis, rotating machinery.

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Stacked pruning sparse denoising autoencoder based intelligent fault diagnosis of rolling bearings

Cited by 104 publications

References 22 publications

Artificial neural network technique for improving prediction of credit card default: A stacked sparse autoencoder approach

Artificial neural network technique for improving prediction of credit card default: A stacked sparse autoencoder approach

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

Convolutional Neural Network in Intelligent Fault Diagnosis Toward Rotatory Machinery

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