Abstract
This study proposes a classification model of equipment fault diagnosis based on integrated incremental learning mechanism on the basis of characteristics of industrial equipment status data. The model first proposes a dynamic weight combination classification model based on long short-term memory (LSTM) and support vector machine (SVM). It solved the problem of fault feature extraction and classification in high noise equipment state data. Then, in this model, integrated incremental learning mechanism and unbalanced data processing technology were introduced to solve problems of massive unbalanced new data feature extraction and classification and sample category imbalance under equipment status data. Finally, an equipment fault diagnosis classification model based on integrated incremental dynamic weight combination is formed. Experiments prove that the model can effectively overcome the problems of excessive data volume, unbalanced, high noise, and inability to correlate data samples in the process of equipment fault diagnosis.
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Acknowledgment
This work is supported by Tianjin Science and Technology Project under Grant No. 18YFCZZC00060 and No. 18ZXZNGX00100, and Hebei Provincial Natural Science Foundation Project under Grant No. F2019202062.
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Ji, H., Liu, X., Tan, A., Wang, Z., Yu, B. (2020). Research on Equipment Fault Diagnosis Classification Model Based on Integrated Incremental Dynamic Weight Combination. In: Qin, P., Wang, H., Sun, G., Lu, Z. (eds) Data Science. ICPCSEE 2020. Communications in Computer and Information Science, vol 1258. Springer, Singapore. https://doi.org/10.1007/978-981-15-7984-4_36
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DOI: https://doi.org/10.1007/978-981-15-7984-4_36
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