Multitask learning enhanced fault diagnosis based on multimodal feature with adaptive weight fusion

Jian Cui; Dailin Zhang

doi:10.1117/12.3038384

5 August 2024 Multitask learning enhanced fault diagnosis based on multimodal feature with adaptive weight fusion

Jian Cui, Dailin Zhang

Proceedings Volume 13226, Third International Conference on Advanced Manufacturing Technology and Manufacturing Systems (ICAMTMS 2024); 132262Q (2024) https://doi.org/10.1117/12.3038384
Event: 3rd International Conference on Advanced Manufacturing Technology and Manufacturing Systems (ICAMTMS 2024), 2024, Changsha, China

Abstract

Fault diagnosis has gained increasing interests to assure the security and stability of industrial systems. Simultaneously collecting multi-sensor signals is crucial for comprehensively characterizing the system's health status. Nonetheless, creating effective fusion mechanisms for multi-sensor signals to enhance diagnostic performance remains a challenge. Consequently, this study proposes an innovative fault diagnosis method enhanced by multi-task learning, incorporating multimodal feature learning and attention fusion across vibration and current signals. Initially, we introduce an attention fusion module designed to selectively extract crucial features from multimodal data obtained from vibration and current signals. Furthermore, a multitask learning module is developed to collectively optimize multiple classification tasks using fused features and two distinct unimodal features. The performance of our proposed method is evaluated on two datasets. Experimental results illustrate that our method can extract more significant features and achieve superior classification performance compared to unimodal methods which lacks attention fusion and multi-task learning modules.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jian Cui and Dailin Zhang "Multitask learning enhanced fault diagnosis based on multimodal feature with adaptive weight fusion", Proc. SPIE 13226, Third International Conference on Advanced Manufacturing Technology and Manufacturing Systems (ICAMTMS 2024), 132262Q (5 August 2024); https://doi.org/10.1117/12.3038384

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KEYWORDS

Machine learning

Vibration

Feature fusion

Feature extraction

Signal attenuation

Image fusion

Wavelets

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