Class-relevant feature density estimator for open set fault classification of industrial equipment using vibration signals

• Published in: Measurement : journal of the International Measurement Confederation 2024-01, Vol.224, p.113806, Article 113806
• Main Authors: Mei, Jie, Liu, Wei, Zhu, Ming, Qi, Yongka, Xu, Wenbo, Xu, Hui
• Format: Article
• Language: English
• Subjects: Class conditional variational auto-encoder; Class-relevant feature density estimator; Fault diagnosis; Open set recognition; Vibration signal
• ISSN: 0263-2241; 1873-412X
• DOI: 10.1016/j.measurement.2023.113806

Recently, the application of vibration signals to the fault diagnosis of industrial equipment has attracted increased attention. Open set recognition allows deep networks to detect unknown faults while maintaining a high-classification accuracy for known faults, which is necessary for practical industrial applications. Existing distance- and probability-based open set recognition methods show significant potential in handling this challenging task. In this study, we combine a deep backbone network and probabilistic model to construct a class-relevant feature density estimator (CRFDE) that integrates the advantages of distance- and probability-based methods. Specifically, a loss function is developed to enable the backbone network to extract the class-relevant features and probabilistic model to estimate them, where the employed probabilistic model is improved from variational auto-encoder (VAE) to class-conditional VAE (CCVAE) that can estimate the class-conditional data densities. The output class-relevant feature densities of CCVAE are treated as special distances to determine the class belongingness of the input samples. We validated the proposed CRFDE on two public motor bearing datasets and one laboratory gas-insulated switchgear vibration signal dataset. The results revealed that the proposed CRFDE achieved higher F1 scores than the existing methods. •The open set classification task in fault diagnosis is addressed.•A class-relevant feature density estimator that combines a backbone and novel probabilistic CCVAE is proposed.•A novel loss function is developed that jointly enables the backbone network to extract the class-relevant features and CCVAE to estimate them.•Experimental results demonstrate the superior performance of the proposed method.