A renewable fusion fault diagnosis network for the variable speed conditions under unbalanced samples

Xu, Kun; Li, Shunming; Jiang, Xingxing; An, Zenghui; Wang, Jinrui; Yu, Tianyi

doi:10.1016/j.neucom.2019.08.099

Cited by 39 publications

(11 citation statements)

References 23 publications

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“…Finally, the migration network is used to complete the migration diagnosis task. To further deal with the problems under the condition of variable speeds, they also proposed a renewable fusion fault diagnosis network (RFFDN) [38]. The model was applied to solve the problem in the case of sampling data missing at variable speeds.…”

Section: A Brief Review Of the Fault Diagnosis Based On Deep Learning Methods Under The Variable Speed Conditionmentioning

confidence: 99%

A Two-Stage, Intelligent Bearing-Fault-Diagnosis Method Using Order-Tracking and a One-Dimensional Convolutional Neural Network with Variable Speeds

Peng

et al. 2021

Sensors

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When performing fault diagnosis tasks on bearings, the change of any bearing’s rotation speed will cause the frequency spectrum of bearing fault characteristics to be blurred. This makes it difficult to extract stable fault features based on manual or intelligent methods, resulting in a decrease in diagnostic accuracy. In this paper, a two-stage, intelligent fault diagnosis method (order-tracking one-dimensional convolutional neural network, OT-1DCNN) is proposed to deal with the problem of fault diagnosis under variable speed conditions. Firstly, the order tracking algorithm is used to resample the monitoring data obtained under different rotation speeds. Then, the one-dimensional convolutional neural network is adopted to extract features of the fault data. Finally, the fault type of collected data can be obtained by fully connected networks based on the features extracted. In the time domain, while the proposed algorithm only relies on the fault data collected under one speed as the training dataset, it is capable of doing fault diagnosis under different speed conditions. In the condition with the largest difference in speed with each dataset, the accuracy of the proposed method is higher than the baseline methods by 0.54% and 11.00%—on CWRU dataset and our own dataset respectively. The results show that the proposed method performs well in dealing with the fault diagnosis under the condition of variable speeds.

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Section: A Brief Review Of the Fault Diagnosis Based On Deep Learning Methods Under The Variable Speed Conditionmentioning

confidence: 99%

A Two-Stage, Intelligent Bearing-Fault-Diagnosis Method Using Order-Tracking and a One-Dimensional Convolutional Neural Network with Variable Speeds

Peng

et al. 2021

Sensors

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“…Lee et al [25] proposed a multi-objective instance weightingbased transfer learning network to solve the problem that the discrepancy between and within domains is large and successfully applied it to fault diagnosis. The deep domain adaptation model can get rid of the inherent disadvantages of the domain adaptation network with the help of a deep learning network and also maintain the advantages of the domain adaptation model, which can effectively solve the problem of data sample distribution discrepancy [26].…”

Section: Introductionmentioning

confidence: 99%

Domain Adaptation Network with Double Adversarial Mechanism for Intelligent Fault Diagnosis

et al. 2021

Applied Sciences

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Due to the mechanical equipment working under variable speed and load for a long time, the distribution of samples is different (domain shift). The general intelligent fault diagnosis method has a good diagnostic effect only on samples with the same sample distribution, but cannot correctly predict the faults of samples with domain shift in a real situation. To settle this problem, a new intelligent fault diagnosis method, domain adaptation network with double adversarial mechanism (DAN-DAM), is proposed. The DAN-DAM model is mainly composed of a feature extractor, two label classifiers and a domain discriminator. The feature extractor and the two label classifiers form the first adversarial mechanism to achieve class-level alignment. Moreover, the discrepancy between the two classifiers is measured by Wasserstein distance. Meanwhile, the feature extractor and the domain discriminator form the second adversarial mechanism to realize domain-level alignment. In addition, maximum mean discrepancy (MMD) is used to reduce the distance between the extracted features of two domains. The DAN-DAM model is verified by multiple transfer experiments on some datasets. According to the transfer experiment results, the DAN-DAM model has a good diagnosis effect for the domain shift samples. Moreover, the diagnostic accuracy is generally higher than other mainstream diagnostic methods.

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“…Reference [9] proposed a novel fault diagnosis method based on local-global deep neural network algorithm. A deep learning model named renewable fusion fault diagnosis network is proposed for updating automatically as the collected fault data increases in [10]. Nowadays, various fault diagnosis methods have enriched fields of fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Tensor Transfer Learning for Intelligence Fault Diagnosis of Bearing with Semisupervised Partial Label Learning

et al. 2021

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A new tensor transfer approach is proposed for rotating machinery intelligent fault diagnosis with semisupervised partial label learning in this paper. Firstly, the vibration signals are constructed as a three-way tensor via trial, condition, and channel. Secondly, for adapting the source and target domains tensor representations directly, without vectorization, the domain adaptation (DA) approach named tensor-aligned invariant subspace learning (TAISL) is first proposed for tensor representation when testing and training data are drawn from different distribution. Then, semisupervised partial label learning (SSPLL) is first introduced for tackling a problem that it is hard to label a large number of instances and there exists much data left to be unlabeled. Ultimately, the proposed method is used to identify faults. The effectiveness and feasibility of the proposed method has been thoroughly validated by transfer fault experiments. The experimental results show that the presented technique can achieve better performance.

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A renewable fusion fault diagnosis network for the variable speed conditions under unbalanced samples

Cited by 39 publications

References 23 publications

A Two-Stage, Intelligent Bearing-Fault-Diagnosis Method Using Order-Tracking and a One-Dimensional Convolutional Neural Network with Variable Speeds

A Two-Stage, Intelligent Bearing-Fault-Diagnosis Method Using Order-Tracking and a One-Dimensional Convolutional Neural Network with Variable Speeds

Domain Adaptation Network with Double Adversarial Mechanism for Intelligent Fault Diagnosis

Tensor Transfer Learning for Intelligence Fault Diagnosis of Bearing with Semisupervised Partial Label Learning

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