Fault diagnosis methods for advanced diagnostics and prognostics testbed (ADAPT): A review

摘要

Nowadays in industrial processes, whether producers or users think highly of performance reliability and robustness of equipments. Therefore, the FDI (Fault detection and isolation) and maintenance techniques have become hot topics for health management of industrial units, as a safety guarantee indeed. As a consequence, researchers have made great efforts to develop, verify and refine diverse diagnosis techniques, meanwhile compare and screening them in order to apply them properly in practice. And then, NASA Ames has built the Advanced Diagnostics and Prognostics Testbed, a real-world system as a general platform for verification and validation (V&V) of diagnosis techniques. Until now, many researchers have developed effective diagnosis algorithms specially applied to this system. In this paper, we introduce the ADAPT and the diagnosis competition around the system, and we review a variety of diagnosis methods divided mainly in three types, model-based, optimization-based and artificial intelligence-based methods, while elaborating the first type in detail by two sorts of model: physical and graphic, of which the second attracts more and more attention of scientists in actual research. Finally, we make a comparison among them based on simplified metrics of qualification, which plays an important role in choosing appropriate methods for diagnosing a special problem.