Data-Driven Fault Diagnosis Method Based on Compressed Sensing and Improved Multiscale Network

Hu, Zhongxu; Wang, Yan; Ge, Ming‐Feng; Liu, Jie

doi:10.1109/tie.2019.2912763

Cited by 146 publications

(50 citation statements)

References 29 publications

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“…On the whole, the proposed CNN-LSTM model achieved minimum values on three indicators, which illustrates the effectiveness of the proposed model. Conv1D (32,8) Conv1D (32,8) LSTM(32) Figure 11. The predicted results of ELM model.…”

Section: Comparison Of Svr and Elm Predictionmentioning

confidence: 99%

Degradation Tendency Prediction for Pumped Storage Unit Based on Integrated Degradation Index Construction and Hybrid CNN-LSTM Model

Zhou

Shan

Liu

et al. 2020

Sensors

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Accurate degradation tendency prediction (DTP) is vital for the secure operation of a pumped storage unit (PSU). However, the existing techniques and methodologies for DTP still face challenges, such as a lack of appropriate degradation indicators, insufficient accuracy, and poor capability to track the data fluctuation. In this paper, a hybrid model is proposed for the degradation tendency prediction of a PSU, which combines the integrated degradation index (IDI) construction and convolutional neural network-long short-term memory (CNN-LSTM). Firstly, the health model of a PSU is constructed with Gaussian process regression (GPR) and the condition parameters of active power, working head, and guide vane opening. Subsequently, for comprehensively quantifying the degradation level of PSU, an IDI is developed using entropy weight (EW) theory. Finally, combining the local feature extraction of the CNN with the time series representation of LSTM, the CNN-LSTM model is constructed to realize DTP. To validate the effectiveness of the proposed model, the monitoring data collected from a PSU in China is taken as case studies. The root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) obtained by the proposed model are 1.1588, 0.8994, 0.0918, and 0.9713, which can meet the engineering application requirements. The experimental results show that the proposed model outperforms other comparison models.

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Section: Comparison Of Svr and Elm Predictionmentioning

confidence: 99%

Degradation Tendency Prediction for Pumped Storage Unit Based on Integrated Degradation Index Construction and Hybrid CNN-LSTM Model

Zhou

Shan

Liu

et al. 2020

Sensors

Self Cite

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show abstract

“…Their values are 27.5mm and 47.5mm, respectively. More detailed information about the experimental platform can be referred to [39], [40].…”

Section: A Case 1: Fault Diagnosis Of Gears 1) Experimental Setup Anmentioning

confidence: 99%

Fault Diagnosis of Rotating Machinery Based on Combination of Deep Belief Network and One-dimensional Convolutional Neural Network

Zou

Jiang

et al. 2019

IEEE Access

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The traditional intelligent diagnosis methods of rotating machinery generally require feature extraction of the raw signals in advance. However, it is a very time-consuming and laborious process for extracting the sensitive feature information to improve classification performance. Deep learning method, as a novel machine learning approach, can simultaneously achieve feature extraction and pattern classification. With the characteristics of Deep Belief Network (DBN) and one-dimensional Convolutional Neural Network (1D-CNN) (e.g. learning complex nonlinear, sparse connection and weight sharing), a precise diagnosis method based on the combination of DBN and 1D-CNN is proposed. Firstly, the DBN composed of three pre-trained restricted Boltzmann machines (RBMs) is constructed to achieve feature extraction and dimensionality reduction of the high-dimensional raw data. Secondly, the low-dimensional features extracted by DBN are fed into 1D-CNN for further extracting the abstract features. Finally, Soft-max classifier is employed to identify different operating conditions of rotating machinery. The superiority of the proposed method is validated by comparison with several state-of-the art fault diagnosis methods on two experimental cases. Meanwhile, the proposed method is tested in different background noises and on the imbalanced datasets. The results show that it has higher efficiency and accuracy than the state-of-the art fault diagnosis methods. INDEX TERMS Deep belief network (DBN), one-dimensional convolutional neural network (1D-CNN), rotating machinery, feature extraction, intelligent fault diagnosis.

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“…The historical data generated in product manufacturing processes have been used to build models of the relationship between manufacturing process data and the product quality. The advantages of product quality diagnosis and prediction models have been demonstrated for the applications of various industrial processes [7][8][9], such as design [10], logistics [11], and fault detection [12]. The decision-making basis has changed from relying on experts to data-driven processes in many fields of manufacturing by introducing artificial intelligence technology.…”

Section: Introductionmentioning

confidence: 99%

A Real-Time Quality Control System Based on Manufacturing Process Data

Duan

Yan

2020

IEEE Access

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Data-Driven Fault Diagnosis Method Based on Compressed Sensing and Improved Multiscale Network

Cited by 146 publications

References 29 publications

Degradation Tendency Prediction for Pumped Storage Unit Based on Integrated Degradation Index Construction and Hybrid CNN-LSTM Model

Degradation Tendency Prediction for Pumped Storage Unit Based on Integrated Degradation Index Construction and Hybrid CNN-LSTM Model

Fault Diagnosis of Rotating Machinery Based on Combination of Deep Belief Network and One-dimensional Convolutional Neural Network

A Real-Time Quality Control System Based on Manufacturing Process Data

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