Improving Accuracy and Interpretability of CNN-Based Fault Diagnosis through an Attention Mechanism

Abstract

This study aims to enhance the accuracy and interpretability of fault diagnosis. To address this objective, we present a novel attention-based CNN method that leverages image-like data generated from multivariate time series using a sliding window processing technique. By representing time series data in an image-like format, the spatiotemporal dependencies inherent in the raw data are effectively captured, which allows CNNs to extract more comprehensive fault features, consequently enhancing the accuracy of fault diagnosis. Moreover, the proposed method incorporates a form of prior knowledge concerning category-attribute correlations into CNNs through the utilization of an attention mechanism. Under the guidance of thisprior knowledge, the proposed method enables the extraction of accurate and predictive features. Importantly, these extracted features are anticipated to retain the interpretability of the prior knowledge. The effectiveness of the proposed method is verified on the Tennessee Eastman chemical process dataset. The results show that proposed method achieved a fault diagnosis accuracy of 98.46%, which is significantly higher than similar existing methods. Furthermore, the robustness of the proposed method is analyzed by sensitivity analysis on hyperparameters, and the interpretability is revealed by visually analyzing its feature extraction process.

Data-Driven Process Monitoring and Fault Diagnosis: A Comprehensive Survey

“…There are works involving applications of process monitoring using several network architectures, such as recurrent networks [351][352][353][354][355][356][357][358][359][360][361][362][363][364][365][366][367], convolutional networks [299,349,[368][369][370][371][372][373][374][375][376][377][378][379][380][381][382], autoencoders [282,365,379,, generative adversarial networks [406][407][408][409][410][411][412][413][414][415][416], Transformers [417]…”