Dual-stage manifold preserving mixed supervised learning for bogie fault diagnosis under variable conditions

Bogie fault diagnosis for bogie is crucial to the safety of rail systems. However, since bogies work under normal states most of the time, the sporadic faulty samples are often submerged in massive normal samples, which are difficult to be distinguished and labeled. Therefore, the labeled training data are always insufficient or even lack of some certain fault states (novel faults), which brings great challenges to fault diagnosis, especially under variable working conditions. Therefore, this paper proposes a new framework named dual-stage manifold preserving mixed supervised learning (d-MMSL) to simultaneously absorb from labeled and unlabeled data effectively. Firstly, manifold similarity (MSLP) is presented to cluster unlabeled samples according to one-off calculation of the manifold similarity. In MSLP, the Best-versus-Second-Best differences and uncertain values are utilized to measure manifold distance and maintain the inherent structure of data. Secondly, Local manifold regularization - broad learning system (LMR-BLS) is presented to o deal with the problem of linear and nonlinear function transformation using simple incremental structure, which could further separate fuzzy sets from MSLP and distinguish the operation conditions of known states accurately. The proposed framework has been verified by a classical dataset and actual vibration data collected from bogies, which achieves a F1-score of 0.99. It is proven that this framework outperforms traditional methods in accuracy and efficiency.