Adaptive deep feature learning network with Nesterov momentum and its application to rotating machinery fault diagnosis

Tang, Shenghao; Shen, Changqing; Wang, Dong; Li, Shuang; Huang, Weiguo; Zhu, Zhongkui

doi:10.1016/j.neucom.2018.04.048

Cited by 88 publications

(42 citation statements)

References 29 publications

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“…To overcome this difficulty, unsupervised feature learning methods can be employed to learn latent features from massive unlabeled data. In recent years, several unsupervised feature learning methods have been applied in machinery fault diagnosis, for example, restricted Boltzmann machine (RBM) [13,14], deep belief network (DBN) [15,16], auto-encoder (AE), stacked AE (SAE) [17], deep AE (DAE) [18][19][20], sparse DAE [21][22][23], convolutional AE (CAE) [24], and other AE variants [25,26]. For example, Yang et al [13] employed an energy-based model, stacked RBM, to capture the system-wide patterns and applied it in wind turbine condition monitoring.…”

Section: Introductionmentioning

confidence: 99%

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Unsupervised Mechanical Fault Feature Learning Based on Consistency Inference-Constrained Sparse Filtering

et al. 2020

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In machinery fault diagnosis, a large amount of monitoring data is often unlabeled, while the number of labeled data is limited. Therefore, learning effective features from massive unlabeled data is a challenging issue for machinery fault diagnosis. In this paper, a simple unsupervised feature learning method, consistency inference-constrained sparse filtering (CICSF), is proposed to learn mechanical fault features with enhanced clustering performance for fault diagnosis. Firstly, inspired by the data augmentation strategy, consistency inference of latent representations for time series (CILRTS) is derived, which infers that training data instances segmented from the same time series should possess consistent latent feature representations. Then, CILRTS is integrated into sparse filtering (SF) as an additional constraint in the latent feature space. The developed CICSF method can optimize the inter-class sparsity and intra-class similarity of the feature distribution simultaneously. Thus, it can learn more effective features from massive unlabeled data. Finally, based on CICSF, a semi-supervised machinery fault diagnosis method is developed. After unsupervised feature learning by CICSF, a softmax regression classifier is trained with limited labeled data to realize machinery fault diagnosis. Experimental results on bearing and gearbox datasets verify the effectiveness of the proposed method. Moreover, comparisons with standard SF and several auto-encoder (AE) variants validate its superiority in unsupervised feature learning and fault diagnosis using limited labeled data. INDEX TERMS Unsupervised feature learning, machinery fault diagnosis, consistent inference of latent representations for time series, sparse filtering, auto-encoder

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Section: Introductionmentioning

confidence: 99%

“…Chen et al [14] proposed an unsupervised feature learning method based on convolutional RBM model for bearing fault diagnosis. Tang et al [15] proposed an adaptive learning rate DBN and applied it in rotating machinery fault diagnosis. Jia et al [17] proposed SAE-constructed deep neural networks (DNNs) for machinery fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Mechanical Fault Feature Learning Based on Consistency Inference-Constrained Sparse Filtering

et al. 2020

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“…Recently, deep neural networks (DNNs) with more powerful fitting abilities have been developed and widely applied to prognostics and health management. In [10][11][12][13][14][15], time-domain and frequency-domain features are extracted in data processing, and then an FDD model is applied for motor status classification. In [16][17][18], vibration image generation with signal analysis was utilized for feature extractions.…”

Section: Introductionmentioning

confidence: 99%

Generative Oversampling Method for Imbalanced Data on Bearing Fault Detection and Diagnosis

Suh

Lee

et al. 2019

Applied Sciences

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In this study, we developed a novel data-driven fault detection and diagnosis (FDD) method for bearing faults in induction motors where the fault condition data are imbalanced. First, we propose a bearing fault detector based on convolutional neural networks (CNN), in which the vibration signals from a test bench are used as inputs after an image transformation procedure. Experimental results demonstrate that the proposed classifier for FDD performs well (accuracy of 88% to 99%) even when the volume of normal and fault condition data is imbalanced (imbalance ratio varies from 20:1 to 200:1). Additionally, our generative model reduces the level of data imbalance by oversampling. The results improve the accuracy of FDD (by up to 99%) when a severe imbalance ratio (200:1) is assumed.

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“…Recently, the method based on deep learning has drawn the attention of researchers [24]. Some studies in this area include the deep learning framework using the improved logistic Sigmoid and transfer learning [25,26], the convolutional neural network-based hidden Markov model [27], the adaptive learning rate deep belief network combined with Nesterov momentum [28], the combination of sparse autoencoder and deep belief network [29].…”

Section: Introductionmentioning

confidence: 99%

Related Entropy Theories Application in Condition Monitoring of Rotating Machineries

Liu

Zhi

Zhang

et al. 2019

Entropy

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Rotating machinery plays an important role in various kinds of industrial engineering. How to assess their conditions is a key problem for operating safety and condition-based maintenance. The potential anomaly, fault and failure information can be obtained by analyzing the collected condition monitoring data of the previously deployed sensors in rotating machinery. Among the available methods of analyzing sensors data, entropy and its variants can provide quantitative information contained in these sensing data. For implementing fault detection, diagnosis, and prognostics, this information can be utilized for feature extraction and selecting appropriate training data for machine learning methods. This article aims to review the related entropy theories which have been applied for condition monitoring of rotating machinery. This review consists of typical entropy theories presentation, application, summary, and discussion.

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Adaptive deep feature learning network with Nesterov momentum and its application to rotating machinery fault diagnosis

Cited by 88 publications

References 29 publications

Unsupervised Mechanical Fault Feature Learning Based on Consistency Inference-Constrained Sparse Filtering

Unsupervised Mechanical Fault Feature Learning Based on Consistency Inference-Constrained Sparse Filtering

Generative Oversampling Method for Imbalanced Data on Bearing Fault Detection and Diagnosis

Related Entropy Theories Application in Condition Monitoring of Rotating Machineries

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