A deep convolutional neural networks model for intelligent fault diagnosis of a gearbox under different operational conditions

Qiu, Guangqi; Gu, Yujiong; Cai, Qi

doi:10.1016/j.measurement.2019.05.057

Cited by 71 publications

(27 citation statements)

References 40 publications

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“…In order to maintain mechanical safety and stability, it is crucial and challengeable to perform the research of its fault diagnosis methods [92], [93]. Furthermore, some investigations on DL-based fault diagnosis toward gear and gearbox have achieved successful results [94], [95]. It will play an emphasis on CNN-based intelligent diagnosis as well as combined technologies.…”

Section: B Cnn-based Fault Diagnosis For Gear and Gearboxmentioning

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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Section: B Cnn-based Fault Diagnosis For Gear and Gearboxmentioning

confidence: 99%

Convolutional Neural Network in Intelligent Fault Diagnosis Toward Rotatory Machinery

2020

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“…To verify the effectiveness of the proposed gearbox evaluation model, we performed a gearbox vibration experiment [35]. Figure 2 shows the gearbox vibration experimental rig, including a driven motor, a gearbox, a brake, a control device, a driven shaft support, and some corresponding electronic units.…”

Section: A the Gearbox Vibration Experimentsmentioning

confidence: 99%

A Fuzzy Performance Evaluation Model for a Gearbox System Using Hidden Markov Model

Huang

et al. 2020

IEEE Access

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In order to track and grasp the operation situations of the gearboxes, the vertical vibration signals of three different gear fault states, normal, worn and broken teeth, are collected via a gearbox vibration experiment. An online diagnosis and performance evaluation model with hidden Markov model (HMM) and fuzzy comprehensive evaluation is proposed. To address the limitation of maximum membership principle in the case of equal membership or the membership is very close to each other, a closeness evaluation strategy is proposed by defining the likelihood ratio of HMM as a similarity and selecting an combined membership function of the semi-trapezoidal and intermediate-ridge distribution. Results show that the online diagnosis has achieved a good performance with the similarity strategy. Compared with the evaluation strategy of the maximum membership principle, the proposed gearbox performance model with the closeness evaluation strategy is more accurately distinguished from the evaluation results of the broken teeth state and the worn state, especially for the case of the equal membership.

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“…In recent years, with the achievement in deep learning and computation ability, the data-driven methods accompanied with the 2D visualization technique [22][23][24] for feature selection are widely developed and show excellent performance in fault diagnosis. Deep Convolutional Neural Network is one of the most used deep learning structures that has been gaining more and more attention, particularly in the field of image processing [25], pattern analysis [26], and fault diagnosis [27][28][29]. For the DCNN-based fault diagnosis, data are primarily processed and transformed into 2D samples as the input of classifier, and the final features for decision-making are obtained by iterations of self-study and not dependent on manual selection.…”

Section: Introductionmentioning

confidence: 99%

An Improved Scheme for Vibration-Based Rolling Bearing Fault Diagnosis Using Feature Integration and AdaBoost Tree-Based Ensemble Classifier

2020

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Bearings are key components in modern power machines. Effective diagnosis of bearing faults is crucial for normal operation. Recently, the deep convolutional neural network (DCNN) with 2D visualization technology has shown great potential in bearing fault diagnosis. Traditional DCNN-based fault diagnosis mostly adopts a single learner with one input and is time-consuming in sample and network construction to obtain a satisfied performance. In this paper, a scheme combining diverse DCNN learners and an AdaBoost tree-based ensemble classifier is proposed to improve the diagnosis performance and reduce the requirement of sample and network construction simultaneously. In this scheme, multiple types of samples can be constructed independently and employed for diagnosis simultaneously; next, the same number of DCNN learners are built for underlying features extraction and the obtained results are integrated and finally fed into the ensemble classifier for fault diagnosis. An illustration based on the Case Western Reserve University datasets is given, which proves the superiority of the proposed scheme in both accuracy and robustness. Herein, we present a universal scheme to improve the diagnosis performance, and give an example for practical application, where the signal preprocessing and image sample construction methods can also be applied in other vibration-based analysis.

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A deep convolutional neural networks model for intelligent fault diagnosis of a gearbox under different operational conditions

Cited by 71 publications

References 40 publications

Convolutional Neural Network in Intelligent Fault Diagnosis Toward Rotatory Machinery

Convolutional Neural Network in Intelligent Fault Diagnosis Toward Rotatory Machinery

A Fuzzy Performance Evaluation Model for a Gearbox System Using Hidden Markov Model

An Improved Scheme for Vibration-Based Rolling Bearing Fault Diagnosis Using Feature Integration and AdaBoost Tree-Based Ensemble Classifier

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