Abstract

Rolling bearings based rotating machinery are widely used in various industrial applications. The failure of rolling bearings, as one of the most critical components, would lead to disastrous consequences to the machinery. Therefore, it’s paramount to deliver an effective intelligent fault diagnosis method for rolling bearings to ensure the machinery’s stability and reliability. With this aim, this article proposes a novel approach that features are extracted via an improved complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and the faults are identified based on a semi-supervised clustering algorithm, that is, clustering approach of fast search and discovery of density peaks (CFSFDP). The proposed method provides two main contributions: (1) highly representative important features may be derived from common high-dimensional features, and (2) the intelligent semi-supervised classifier can identify faults type adaptively without large amount of type-labelled data unlike other supervised classifiers. Benchmarking studies were carried out to indicate that the proposed methodology for the fault diagnostic is superior to other common-used approaches.