Abstract
Data-based diagnostics and condition prediction research is actively underway in the heavy equipment industry, including construction machinery. However, it is practically difficult to obtain anomalous data and predict maintenance in most industrial equipment. Also, several problems occur with missing sensors and abnormal conditions of equipment in various forms. In this paper, a realistic industrial problem is dealt with an anomaly detection approach based on prediction of multi-sensor data collected under normal conditions. We propose a multiple feature extraction model to discriminate at least four different anomaly types, and a prediction model based on normal state to diagnose abnormalities according to the difference between the predicted and the actual values. The proposed method uses a hierarchical model that predicts missing sensor information and predicts global sensor information. Extensive experiments have shown that the proposed model improves robustness and detection accuracy. Our model generates missing sensor data with about 90% accuracy and detects anomalies with about 85% accuracy, even if part of the sensor is missing or the device has been changed.
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Acknowledgement
This work was partly supported by an IITP grant funded by the Korean government (MSIT) (No. 2020-0-01361, AI Graduate School Program (Yonsei University)) and a grant funded by Doosan Infracore, Inc. (Seoul, Korea).
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Jang, Gb., Cho, SB. (2022). Anomaly Detection for Health Monitoring of Heavy Equipment Using Hierarchical Prediction with Correlative Feature Learning. In: Sanjurjo González, H., Pastor López, I., GarcÃa Bringas, P., Quintián, H., Corchado, E. (eds) 16th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2021). SOCO 2021. Advances in Intelligent Systems and Computing, vol 1401. Springer, Cham. https://doi.org/10.1007/978-3-030-87869-6_57
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DOI: https://doi.org/10.1007/978-3-030-87869-6_57
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