Research and Application of Regularized Sparse Filtering Model for Intelligent Fault Diagnosis Under Large Speed Fluctuation

Han, Baokun; Zhang, Guowei; Wang, Jinrui; Wang, Xiaoyu; Jia, Sixiang; He, Jingtao

doi:10.1109/access.2020.2975531

Cited by 4 publications

(2 citation statements)

References 24 publications

(24 reference statements)

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“…L1/2-SF: The L1/2-SF (L1/2 regularized sparse filtering) approach [29] is widely used as an excellent method for bearing and gear fault diagnosis. This method follows the traditional unsupervised machine learning model, thus providing a benchmark for the proposed method.…”

Section: Compared Methods Descriptionmentioning

confidence: 99%

A New Universal Domain Adaptive Method for Diagnosing Unknown Bearing Faults

Yan

Liu

Wang

et al. 2021

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The domain adaptation problem in transfer learning has received extensive attention in recent years. The existing transfer model for solving domain alignment always assumes that the label space is completely shared between domains. However, this assumption is untrue in the actual industry and limits the application scope of the transfer model. Therefore, a universal domain method is proposed, which not only effectively reduces the problem of network failure caused by unknown fault types in the target domain but also breaks the premise of sharing the label space. The proposed framework takes into account the discrepancy of the fault features shown by different fault types and forms the feature center for fault diagnosis by extracting the features of samples of each fault type. Three optimization functions are added to solve the negative transfer problem when the model solves samples of unknown fault types. This study verifies the performance advantages of the framework for variable speed through experiments of multiple datasets. It can be seen from the experimental results that the proposed method has better fault diagnosis performance than related transfer methods for solving unknown mechanical faults.

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Section: Compared Methods Descriptionmentioning

confidence: 99%

A New Universal Domain Adaptive Method for Diagnosing Unknown Bearing Faults

Yan

Liu

Wang

et al. 2021

Entropy

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“…An et al [12] built a three-dimensional numerical simulation model by using advantage finite element method (FEM) for accurate diagnosis under rotating-speed fluctuations. Han et al [13] set up a regularized sparse filtering model for intelligent fault diagnosis under large speed fluctuation. Some researchers [14,15,16] effectively used sequential tracking to eliminate the effects of speed fluctuations.…”

Section: Introductionmentioning

confidence: 99%

A Novel Data Augmentation Method for Intelligent Fault Diagnosis Under Speed Fluctuation Condition

et al. 2020

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The problem of insufficient datasets has long been a hot topic in the field of prognosis and health management of rotary machines. Generative adversarial network (GAN) and other data augmentation algorithms can solve the problem of insufficient samples. However, the premise of the above method is the signal collected at a constant speed rather than at large speed fluctuation. To deal with data augmentation under large speed fluctuation, this paper proposes an effective deep learning method, namely, domain adaptive efficient sub-pixel network (DAESPN). The core idea of DAESPN is to enhance the resolution of the original sample for data augmentation. The DAESPN framework is implemented as follows: after the data passes through the fully connected neural network, the multi-feature maps of the four channels are outputted. A group of high resolution (HR) features is obtained through the sub-pixel fully connected layer. In addition, maximum mean discrepancy (MMD) and mean square error (MSE) are used to construct the loss function of the model. Experimental results of gearbox and bearing datasets show that the DAESPN model has strong feasibility to carry out data augmentation for fault diagnosis of rotating machines under speed fluctuation condition. In addition, the feature learning process of DAESPN is visually displayed and analyzed.

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