Attention-Based Multisignal Representation of High-Resolution Time Series: A Fault Detection Method for Industrial Machinery

Data-driven fault detection in prognostic and health management research poses significant challenges, particularly with few measurements to model complex machinery health conditions. Approaches requiring data in one or many faulty conditions are impractical for actual use cases where only healthy condition data exist. Most of the existing methods for this scenario rely on feature-based representations, separate one-class classifiers, and frequency-converted input. These methods struggle to detect anomalies in high-resolution time series that contain broader frequency information and complex time series shapes. Therefore, to address this issue, we propose the multisignal-based representation of complex time series for fault detection in industrial machinery. It includes a learning rule for the proposal that considers the purity and frequency diversity of the representation with a procedure to compute them efficiently, while the one-class decision function is part of the learned model. In detecting single-and multicomponent faults in reciprocating machinery, our method successfully solves the task, outperforming other classical and state-of-the-art approaches.