Graph Entropy-Based Early Change Detection in Dynamical Bearing Degradation Process

Efficient information extraction enhances condition monitoring and fault diagnosis for bearings. The graph model (GM) has been proven to be a practical approach to extracting signal information within the temporal dynamic of frequencies. This article proposes an early fault detection method based on graph entropy (GE) for the dynamical bearing degradation process, considering the structure differences between graph dynamic changes. First, the complete GM (CGM) is constructed by a short-time spectrum generated from the original signal. In the fault detection phase, the GE, highly correlated with the health condition, is extracted from the GM to check any change in the machine state. Subsequently, the adaptive threshold of short-term month-over-month is used to judge the final decision making in an automated way. Finally, the validation experiment on the XJTU-SY data set and FEMTO-ST data set, as well as compared with the state of the art demonstrates its excellent detection performance. The proposed method extracts an effective 1-D index, which affords an excellent detection ability on early fault occurring in noisy environments, indicating a good potential for identification in the practical dynamic operation of engineering applications.