Abstract
It is important for monitoring and predicting equipment failures. The existing fault prediction method has poor efficiency and accuracy on processing imbalanced data. This paper proposes a feature pattern-based LSTM method (called FLSTM, Feature based Long Short Term Memory) to analyze failures through processing imbalanced data. The method constructs a time-series feature matrix as the input to the LSTM model. In addition, we propose a failure prediction system based on Hadoop environment. The experimental results show that the FLSTM can improve failure prediction with imbalanced big data and the failure prediction system performs well.
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Acknowledgement
“This research is supported by the National Key R&D Program (2018YFE0116700), the Shandong Provincial Natural Science Foundation (ZR2019MF049, Parallel Data Driven Fault Prediction under Online-Offline Combined Cloud Computing Environment), the supporting project from China Petroleum Group (2018D-5010-16) for Big Data Industry Development Pilot Demonstration Project from Ministry of Industry and Information Technology, the National Major Science and Technology Project (2017ZX05013-002), the China Petroleum Group Science and Technology Research Institute Co., Ltd. Innovation Project (Grant No. 2017ycq02) and the Fundamental Research Funds for the Central Universities (2015020031).”
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Xu, L. et al. (2019). FLSTM: Feature Pattern-Based LSTM for Imbalanced Big Data Analysis. In: Ning, H. (eds) Cyberspace Data and Intelligence, and Cyber-Living, Syndrome, and Health. CyberDI CyberLife 2019 2019. Communications in Computer and Information Science, vol 1137. Springer, Singapore. https://doi.org/10.1007/978-981-15-1922-2_8
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DOI: https://doi.org/10.1007/978-981-15-1922-2_8
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