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Machinery Fault Diagnosis Based on Deep Learning for Time Series Analysis and Knowledge Graphs

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Abstract

With the rapid development of modern industrial production, mechanical equipment plays an increasingly important role, and its fault diagnosis is therefore critical. Traditional fault diagnosis generally needs a manual selection of features and a large amount of labeled data for training. In addition, diagnosis results are often too isolated and cannot provide a complete fault diagnosis process. In this paper, we propose a fault diagnosis approach for mechanical equipment, which combines one-dimensional CNN (Convolutional Neural Network), GRU (Gated Recurrent Unit), attention mechanism, and KG (knowledge graph). We first construct an ATT-1D CNN-GRU model by the serial merger of the one-dimensional CNN and GRU. The CNN model is used for feature extraction, and GRU and attention mechanism are applied for more accurate feature extraction. To prevent overfitting, we add the BN (Batch Normalization) before each pooling layer input. Then, we train the ATT-1D CNN-GRU model according to the existing fault data and obtain a precise prediction of the fault diagnosis results. Furthermore, we match the prediction results by searching through the knowledge graph and obtaining more relevant information about this fault. We verify our attention-based 1D CNN-GRU model over rolling bearing datasets under different loads. The results show that the accuracy of our proposed model is up to 99%.

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Notes

  1. http://www.ebusiness-unibw.org/tools/rdf2rdfa/

  2. http://csegroups.case.edu/bearingdatacenter/pages/welcome-case-western-reserve-university-bearing-data-center-website

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Acknowledgements

The authors wish to thank the anonymous referees for their valuable comments and suggestions, which greatly improved the technical content and the presentation of the paper. The work was supported in part by the National Natural Science Foundation of China (U1931207).

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Authors and Affiliations

  1. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China

    Haiying Liu, Daiyi Li, Li Yan & Zongmin Ma

  2. Department of Computer Science, University of Massachusetts Lowell, Lowell, MA, 01854, USA

    Ruizhe Ma

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Liu, H., Ma, R., Li, D. et al. Machinery Fault Diagnosis Based on Deep Learning for Time Series Analysis and Knowledge Graphs. J Sign Process Syst 93, 1433–1455 (2021). https://doi.org/10.1007/s11265-021-01718-3

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