To avoid severe damages and unexpected shutdowns, fault diagnosis and health assessment of rotating machinery have received considerable attention in recent years. On the other hand, as a great amount of data become acquirable and accessible in industry, data-driven tools have become an emerging research area, acting as a complement to the model-based (or physics-based) fault diagnosis and health assessment methods. In this chapter, based on the kernel density estimation (KDE) and the Kullback-Leibler divergence (KLID), a new data-driven fault diagnosis approach and a new health assessment approach are introduced. By utilizing the KDE, the statistical distribution of selected features can be readily estimated without assuming any parametric family of distributions, whereas the KLID is able to quantify the discrepancy between two probability distributions of selected features. An integrated Kullback-Leibler divergence, which aggregates the KLID of all the selected features, is introduced to discriminate various fault types or health status of rotating machinery. The effectiveness of the proposed approaches is demonstrated through three case studies of fault diagnosis and health assessment of rotating machinery.
Part of the book: Fault Diagnosis and Detection