Bearing early fault identification and condition monitoring based on dynamic difference index

Bearing condition monitoring is essential for early fault detection and early warning of large equipment, and signal processing techniques are frequently used to analyse nonlinear and nonstationary sequences. Cross-correlation integral is implemented in bearing condition monitoring because it can analyse the non-stationarity of time series in dynamic systems. This paper proposes a dynamic difference index (DDI) due to the difficulty of determining the threshold in the cross-correlation integral and the roughness and operation caused by sequence similarity of 0 or 1. It is a measure of the similarity between the fuzzy autocorrelation integral of a portion of a time series and the cross-correlation between that portion and other portions of the same time series, and it is used to determine the stationarity of the time series. When bearings begin to degrade or develop structural defects, the DDI changes dramatically. The XJTU-SY dataset and IMS Bearing Data were utilised for algorithm validation. First, the algorithm’s efficacy was demonstrated by optimising the effects of various thresholds, distance measures, and time window sizes on DDI and computational efficiency. Secondly, the comparison with common methods and state of the art shows the superiority of the algorithm in detecting early bearing faults, and reveals its appropriate use in practical engineering applications.