Fault diagnosis method study in roller bearing based on wavelet transform and stacked auto-encoder

Tan, Jiyuan; Lu, Weining; Juneng, An; Xueqian, Wan

doi:10.1109/ccdc.2015.7162738

Cited by 61 publications

(35 citation statements)

References 4 publications

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“…The corresponding framework proposed by Jia et al is shown in Figure 6. Tan et al used digital wavelet frame and nonlinear soft threshold method to process the vibration signal and built a SAE on the preprocessed signal for roller bearing fault diagnosis [52]. Zhu et al proposed a SAEbased DNN for hydraulic pump fault diagnosis with input as frequency domain features after Fourier transform [53].…”

Section: Health Monitoringmentioning

confidence: 99%

Deep learning and its applications to machine health monitoring

Zhao

Yan

Chen

et al. 2019

Mechanical Systems and Signal Processing

1,884

813

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Section: Health Monitoringmentioning

confidence: 99%

Deep learning and its applications to machine health monitoring

Zhao

Yan

Chen

et al. 2019

Mechanical Systems and Signal Processing

1,884

813

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“…The basis for the operation of diagnostic systems based on neural networks (NNs), applied also for induction motor (IM) fault detection and classification, are analytical methods. Therefore, the input information for such systems is the result of extracting symptoms from chosen diagnostic signals using analytical methods, for example: FFT [24,25], WT [26], HHT [27], etc.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network-Based Stator Current Data-Driven Incipient Stator Fault Diagnosis of Inverter-Fed Induction Motor

et al. 2020

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In this paper, the idea of using a convolutional neural network (CNN) for the detection and classification of induction motor stator winding faults is presented. The diagnosis inference of the stator inter-turn short-circuits is based on raw stator current data. It offers the possibility of using the diagnostic signal direct processing, which could replace well known analytical methods. Tests were carried out for various levels of stator failures. In order to assess the sensitivity of the applied CNN-based detector to motor operating conditions, the tests were carried out for variable load torques and for different values of supply voltage frequency. Experimental tests were conducted on a specially designed setup with the 3 kW induction motor of special construction, which allowed for the physical modelling of inter-turn short-circuits in each of the three phases of the machine. The on-line tests prove the possibility of using CNN in the real-time diagnostic system with the high accuracy of incipient stator winding fault detection and classification. The impact of the developed CNN structure and training method parameters on the fault diagnosis accuracy has also been tested.

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“…This makes the convolutional neural network gradually stand out in the era of big data. In 2015, Xueqian Wang from Tsinghua University constructed a deep neural network using a stacked sparse self-encoder to diagnose the faults of rolling bearings (Junbo et al, 2015). In 2016, Janssen from the University of Ghent in Belgium used convolutional neural network for the first time to diagnose the faults of bearings and gears in the gear box (Janssens et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Research on fault diagnosis of time-domain vibration signal based on convolutional neural networks

Wei

Wang

et al. 2019

Systems Science & Control Engineering

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In order to maintain the safe operation of various types of equipment, the health status of main components should be monitored in real-time, and the demand for intelligent fault diagnosis algorithm has increased sharply. However, the traditional intelligent diagnosis algorithm is based on the manual method for signal feature extraction, which has high requirements for expert experience and poor generality. A convolutional neural network, with big data as its engine, is the most effective pattern classification algorithm at present. In this paper, the convolutional neural network is applied to time-domain vibration signal fault diagnosis, taking the bearing as an example, and an intelligent diagnosis method of bearing based on convolution neural network is proposed. The proposed method does not need manual feature extraction, and can automatically complete feature extraction and automatic fault recognition. The convolutional neural network has three convolutional layers. We use data enhancement techniques for the input raw signal and convert the one-dimensional original time-domain vibration signal into a two-dimensional signal. The model shows good results on CWRU dataset and the recognition accuracy of the algorithm in the CWRU bearing database is more than 96%.

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Fault diagnosis method study in roller bearing based on wavelet transform and stacked auto-encoder

Cited by 61 publications

References 4 publications

Deep learning and its applications to machine health monitoring

Deep learning and its applications to machine health monitoring

Convolutional Neural Network-Based Stator Current Data-Driven Incipient Stator Fault Diagnosis of Inverter-Fed Induction Motor

Research on fault diagnosis of time-domain vibration signal based on convolutional neural networks

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