Open Set Recognition Methods for Fault Diagnosis: A Review

Open set fault diagnosis (OSFD) refers to the task of identifying faults in a system where the set of possible faults is not predetermined and may include novel, unseen faults. This can be a challenging problem in the field of machine learning and artificial intelligence, as traditional techniques for fault diagnosis are typically designed to work within a fixed set of predefined categories. In this review article, we present an overview of recent developments in OSFD methods. We begin by discussing the motivations of OSFD, and then introduce a range of different OSFD approaches. These approaches include methods based on deep neural network-based, mapping-based, and adversarial-based. We also discuss the performance of these methods on a variety of benchmark datasets and real-world applications, and highlight the strengths and limitations of each approach. Finally, we conclude by discussing future directions for research in OSFD and the potential impact of these methods on practical applications. Overall, OSFD represents a promising and active area of research, with significant potential for improving the reliability and efficiency of systems in a wide range of industries.