Abstract
With the development of information technology, industry data is increasingly generated during the manufacturing process. Companies often want to utilize the data they collected for more than the initial purposes. In this paper, we report a case study with an industrial equipment manufacturer to analyze the operation data and the failure records of the equipment. We first tried to map the working condition of the equipment according to the daily recorded sensor data. However, we found the collected sensor data is not strongly correlated with the failure data to capture the phenomenon of the recorded failure categories. Thus, we proposed a data driven-based method for anomaly identification of such low correlation data. Our idea is to apply a deep neural network to learn the behavior of collected records to calculate the severity degree of each record. The severity degree of each record indicates the difference of performance between each record and all other records. Based on the value of severity degree, we identified a few anomalous records, which have very different sensor data with other records. By analyzing the sensor data of the anomalous records, we observed some unique combinations of sensor values that can potentially be used as indicators for failure prediction. From the observations, we derived hypotheses for future validation.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Due to Non-Discloser Agreement, we are not allowed to give detailed information of the equipment and the company in the paper.
References
Lee, J., Bagheri, B., Kao, H.-A.: A cyber-physical systems architecture for industry 4.0-based manufacturing systems. Manuf. Lett. 3, 18–23 (2015)
Lee, C.K.M., Zhang, S.Z., Ng, K.K.H.: Development of an industrial Internet of things suite for smart factory towards re-industrialization. Adv. Manuf. 5(4), 335–343 (2017)
Bodrow, W.: Impact of Industry 4.0 in service oriented firm. Adv. Manuf. 5(4), 394–400 (2017)
Li, Z., Wang, Y., Wang, K.-S.: Intelligent predictive maintenance for fault diagnosis and prognosis in machine centers: industry 4.0 scenario. Adv. Manuf. 5(4), 377–387 (2017). https://doi.org/10.1007/s40436-017-0203-8
Khan, A., Turowski, K.: A survey of current challenges in manufacturing industry and preparation for industry 4.0. Paper presented at the Intelligent Information Technologies for Industry, Cham (2016)
Li, Z., Wang, Y., Wang, K.: A deep learning driven method for fault classification and degradation assessment in mechanical equipment. Comput. Ind. 104, 1–10 (2019)
Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. 41(3), 15 (2009)
Acknowledgment
The work described in this article has been conducted as part of the research project CIRCit (Circular Economy Integration in the Nordic Industry for Enhanced Sustainability and Competitiveness), which is part of the Nordic Green Growth Research and Innovation Programme (grant numbers: 83144), and funded by NordForsk, Nordic Energy Research, and Nordic Innovation.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Li, Z., Li, J. (2020). Knowledge Discovery and Anomaly Identification for Low Correlation Industry Data. In: Wang, Y., Martinsen, K., Yu, T., Wang, K. (eds) Advanced Manufacturing and Automation IX. IWAMA 2019. Lecture Notes in Electrical Engineering, vol 634. Springer, Singapore. https://doi.org/10.1007/978-981-15-2341-0_24
Download citation
DOI: https://doi.org/10.1007/978-981-15-2341-0_24
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-2340-3
Online ISBN: 978-981-15-2341-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)