A novel condition monitoring approach using hybrid lightweighted adaptive models for complex machinery

Condition monitoring of complex industrial systems is critical for ensuring operational reliability. Data-driven methods using artificial intelligence have advanced anomaly detection (AD) and fault diagnosis (FD), but existing approaches often treat them separately, focus on known faults, and struggle with previously unseen or rare conditions in multi-modal scenarios. This study proposes a novel condition monitoring framework that integrates AD and FD within a distributed architecture. Lightweight models—including kernel principal component analysis, support vector machines, and one-dimensional convolutional neural networks—enable efficient and scalable processing. A multilevel information fusion strategy ensures consistent detection and diagnosis while facilitating the isolation of previously unknown faults. Module test results demonstrate the effectiveness and robustness of the proposed feature extraction and adaptive modeling approaches. The overall test results for previously unknown faults vary across channels and modules. For samples with misalignment and inner blade wear, channel-level detection accuracy ranges from 0.007 to 0.989, with unknown recognition rates up to 0.933 and diagnosis probabilities from 0.508 to 0.933. For strong misalignment and fan-end inner race faults, nearly all channels achieve 100 % detection accuracy, with some diagnosis probabilities above 0.9, while unknown recognition remains minimal (mostly below 0.05). Importantly, the proposed framework integrates detection and diagnostic outputs across channels, effectively mapping previously unseen faults to similar known categories or to an unknown category. Overall, the proposed framework offers a referenced solution for condition monitoring of industrial systems like pumps, turbines, and compressors, and lays the foundation for future improvements incorporating domain knowledge and model-driven interpretability.