Abstract
To ensure availability of industrial machines and reducing breakdown times, a machine monitoring can be an essential help. Unexpected machine downtimes are typically accompanied by high costs. Machine builders as well as component suppliers can use their detailed knowledge about their products to counteract this. One possibility to face the challenge is to offer a product-service system with machine monitoring services to their customers. An implementation approach for such a machine monitoring service is presented in this article. In contrast to previous research, we focus on the integration and interaction of machine learning tools and human domain experts, e.g. for an early anomaly detection and fault classification. First, Long Short-Term Memory Neural Networks are trained and applied to identify unusual behavior in operation time series data of a machine. We describe first results of the implementation of this anomaly detection. Second, domain experts are confronted with related monitoring data, e.g. temperature, vibration, video, audio etc., from different sources to assess and classify anomaly types. With an increasing knowledge base, a classifier module automatically suggests possible causes for an anomaly automatically in advance to support machine operators in the anomaly identification process. Feedback loops ensure continuous learning of the anomaly detector and classifier modules. Hence, we combine the knowledge of machine builders/component suppliers with application specific experience of the customers in the business value stream network.
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Olivotti, D., Passlick, J., Axjonow, A., Eilers, D., Breitner, M.H. (2018). Combining Machine Learning and Domain Experience: A Hybrid-Learning Monitor Approach for Industrial Machines. In: Satzger, G., Patrício, L., Zaki, M., Kühl, N., Hottum, P. (eds) Exploring Service Science. IESS 2018. Lecture Notes in Business Information Processing, vol 331. Springer, Cham. https://doi.org/10.1007/978-3-030-00713-3_20
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