From deterministic to Bayesian: Adapting pre-trained models for human-computer collaborative fault diagnosis via post-hoc uncertainty

Existing fault diagnosis research focuses on improving accuracy, implying that decisions are made by the model alone. This can lead to models providing untrustworthy predictions without the user’s knowledge. Moreover, there are dual pitfalls of black-box effects and imperfect accountability mechanisms. Human-computer collaborative paradigm promises to address these issues by including humans in the decision-making loop, leveraging the strengths of both parties to provide safer decisions. To establish such a paradigm, a support is required and predictive uncertainty is a suitable candidate, which is often captured by constructing Bayesian neural network based on variational inference or deep ensemble. However, these ante-hoc uncertainty methods, which require prior adjustment of the model structure and training the model from scratch, suffer from many limitations: (1) Modification of model structure for uncertainty estimation may sacrifice accuracy or task-specific requirements. (2) These methods multiply the number of parameters and lead to a significant increase in training cost. (3) Retraining a model when a pre-trained model is available can be a waste of resources. Therefore, we propose a post-hoc uncertainty method based on Laplace approximation that quickly and easily switches any pre-trained model from deterministic to Bayesian mode, avoiding heavy computational burden and loss of predictive performance. The proposed method is validated by conducting calibration and OOD detection tasks in both in-domain and cross-domain scenarios, and the experimental results show that the proposed method has comparable or even better uncertainty estimation quality than ante-hoc methods.