Adversarial Multimodal Contrastive Learning for Robust Industrial Fault Diagnosis

Fault diagnosis (FD) techniques leveraging self-supervised contrastive learning (SSCL) have demonstrated significant potential in industrial scenarios due to their reduced dependence on manually annotated data. However, the existing SSCL algorithms primarily focus on establishing complex similarity relationships among unimodal augmented views. These unimodal SSCL approaches are particularly vulnerable to learning shallow, domain-dependent spurious features in the training data rather than more intrinsic and essential features. Consequently, such spurious features may cause the algorithm failure when encountering distribution shift issues resulting from environmental perturbations or changes in working conditions. To address this challenge, we propose adversarial multimodal contrastive learning (AMMCL), a novel approach designed to extract robust and generalizable multimodal representations from time series and their corresponding spectrograms. AMMCL utilizes intermodal contrastive learning and adversarial training strategy to align modal-invariant features from both elementwise and setwise perspectives. These essential features are beneficial for intradomain and cross-domain FD tasks. Furthermore, a slice segmentation processing (SSP) method based on dominant frequency is employed to enhance model’s ability to recognize varying patterns within time series. AMMCL is first evaluated on intradomain and cross-domain FD tasks using the Gearbox and XJTU-SY datasets, where it outperforms nine existing FD algorithms in terms of performance. Additionally, AMMCL is compared with ten other valve stiction detection algorithms on International Stiction Database (ISDB) dataset, successfully identifying the most loop states (23 out of 26). Finally, the trained AMMCL model on the ISDB dataset is implemented in actual industrial valve detection, demonstrating the feasibility and practicality of AMMCL in real industrial scenarios.