Fault detection and diagnostics in the context of sparse multimodal data and expert knowledge assistance: Application to hydrogenerators

Deep learning-based methods for industrial fault detection and diagnostics (FDD) depend strictly on good quality and sufficient quantity of condition monitoring data. However, in real-world industrial settings, data collection is usually limited, leading to sparse and insufficient data to train a data-driven model. Therefore, this work proposes a new methodology to address this issue by leveraging multimodal data anddomain knowledge to develop a data-driven solution. Particularly for large, complex machinery, unimodal sensors may not fully capture the health state information. In such cases, multimodal data may provide complementary insights into the machine degradation. However, challenges mentioned above need to be addressed before these data can be useful. The multimodal learning method presented within the methodology can benefit from useful information from different data modalities and from domain expert knowledge, even when these data are of low volume. The performance of the proposed methodology is investigated through a real industrial case study involving energy production systems. The obtained results demonstrate the potential of the proposed methodology in augmenting the FDD accuracy and tackling the sparse data challenge. •Innovative methodology for fault detection and diagnostics with sparse data.•Multimodal data learning approach with cross-modal attention.•Expert knowledge assisted feature extraction even under incomplete knowledge.•Demonstration of the methodology on an industrial case study of hydrogenerators.•Robust performance in a real-world case study even when high quality data is missing.