Abstract
Reliable fault detection and diagnostics are crucial in order to ensure efficient operations in industrial assets. Data-driven solutions have shown great potential in various fields but pose many challenges in Prognostics and Health Management (PHM) applications: Changing external in-service factors and operating conditions cause variations in the condition monitoring (CM) data resulting in false alarms. Furthermore, novel types of faults can also cause variations in CM data. Since faults occur rarely in complex safety critical systems, a training dataset typically does not cover all possible fault types. To enable the detection of novel fault types, the models need to be sensitive to novel variations. Simultaneously, to decrease the false alarm rate, invariance to variations in CM data caused by changing operating conditions is required. We propose contrastive learning for the task of fault detection and diagnostics in the context of changing operating conditions and novel fault types. In particular, we evaluate how a feature representation trained by the triplet loss is suited to fault detection and diagnostics under the aforementioned conditions. We showcase that classification and clustering based on the learned feature representations are (1) invariant to changing operating conditions while also being (2) suited to the detection of novel fault types. Our evaluation is conducted on the bearing benchmark dataset provided by the Case Western Reserve University (CWRU).
Highlights
IntroductionPublisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations
We evaluate whether fault detection and diagnostics based on the learned feature representation is, on the one hand, invariant to variations in the condition monitoring (CM) data caused by novel operating conditions and, on the other hand, sensitive to variations caused by novel fault types
Contrastive learning has been evaluated in the context of Prognostics and Health Management (PHM) applications
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Modern industrial processes are increasingly subject to oversight by condition monitoring (CM) devices. The recorded data opens up the possibility of data-driven maintenance models [1]. Data-driven solutions are especially interesting with regard to complex assets for which model-based approaches are limited or do not exist. Recent successes in deep learning have demonstrated the potential of data-driven solutions [2,3]. For the task of fault detection and diagnostics, particular challenges arise when applying deep learning to CM data from an industrial asset
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