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 paper 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 graph model (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 dataset and FEMTO-ST dataset, as well as compared with the state-of-the-art demonstrates its excellent detection performance. The proposed method extracts an effective one-dimensional 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.