  Fault diagnosis is essential in industrial processes for monitoring the
conditions of important machines. With the ever-increasing complexity of
working conditions and demand for safety during production and operation,
different diagnosis methods are required, and more importantly, an integrated
fault diagnosis system that can cope with multiple tasks is highly desired.
However, the diagnosis subtasks are often studied separately, and the currently
available methods still need improvement for such a generalized system. To
address this issue, we propose the Generalized Out-of-distribution Fault
Diagnosis (GOOFD) framework to integrate diagnosis subtasks, such as fault
detection, fault classification, and novel fault diagnosis. Additionally, a
unified fault diagnosis method based on internal contrastive learning is put
forward to underpin the proposed generalized framework. The method extracts
features utilizing the internal contrastive learning technique and then
recognizes the outliers based on the Mahalanobis distance. Experiments are
conducted on a simulated benchmark dataset as well as two practical process
datasets to evaluate the proposed framework. As demonstrated in the
experiments, the proposed method achieves better performance compared with
several existing techniques and thus verifies the effectiveness of the proposed
framework.
