Counterfactual Inference for Generalized Zero-Shot Compound-Fault Diagnosis

• Published in: IEEE transactions on instrumentation and measurement Vol. 74; pp. 1 - 11
• Main Authors: Xu, Juan, Kong, Hui, Ding, Xu, Yuan, Xiaohui
• Format: Journal Article
• Language: English
• Published: New York IEEE 2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Compounds; Counterfactual inference; Data models; Fault diagnosis; Faults; generalized zero-shot learning (GZSL); generative adversarial network; Generative adversarial networks; Inference; Neural networks; Predictive models; rolling bearing; Semantics; Training; Zero shot learning

Learning a model heavily depends on the training examples, which are sometimes difficult to obtain if not impossible. This a typically true for fault diagnosis in machinery, particularly for compound faults. The counterfactual inference reveals the causal components inherent in the fault data in an...