A Fine-grained Semi-supervised Anomaly Detection Framework for Predictive Maintenance of Industrial Assets

Abstract

Reliable operation of industrial assets is of high priority for businesses where productivity determines the ability to deliver safety-critical products of high quality in a timely manner. The aerospace industry leads the demand for predictive maintenance (PdM). In the manufacturing space, unscheduled down time causes production delay and increases operational costs while introducing potential risks in product quality and on-time delivery. In field application of these products, unexpected breakdown of critical components can result in safety-critical events. Failure events are, therefore, extremely rare in industrial settings. Diverse operating conditions in the manufacturing environment and field applications contribute to the heterogeneous nature of data collected from these assets. This work presents an anomaly detection framework for PdM of industrial assets to address the practical challenges of scarce failure data sources and heterogeneous data across assets. We introduce a fine-grained modeling approach that efficiently accounts for individual asset differences in a semi-supervised fashion which requires only normal operation data for model training. The framework is demonstrated with an industrial 4.0 use case. Vibration sensor data from pumps in one of our manufacturing facilities is ingested to enable PdM with 2 weeks lead time using the proposed framework. This transforms unexpected breakdown time to scheduled maintenance, thereby reducing cost of delays and operation interruptions. The systematic implementation of the framework in the case study covers the practical production aspects including data quality evaluation, model training, optimization and daily serving of predictions. Furthermore, implementation challenges and recommendations are discussed based on the end-to-end solution implementation experiences.