Initial fault time estimation of rolling element bearing by backtracking strategy, improved VMD and infogram

Rolling bearing failure is widely regarded as a failure form of industrial machines. Owing to the poor operating circumstance with the stochastic contact between rolling elements, the performance of the bearing will deteriorate over time and cause a cascade breakdown in the mechanical system. Early fault detection has been found to be an effective strategy to avoid economic loss. Therefore, an integration method for fault diagnosis that combines backtracking strategy, improved variational mode decomposition (VMD), and infogram is proposed to tackle the challenge of the early feature extraction from the heavy noisy non-stationary signal. The backtracking strategy is adopted to track the data sample points earlier than the fault threshold determined based on the kurtosis index. The optimum parameters α and K of VMD are acquired through the particle swarm optimization (PSO) algorithm. In this way, the more accurate intrinsic mode functions (IMFs) can be gained by the improved VMD. The optimum IMFs are acquired according to the maximum values of kurtosis and correlation coefficients, and these IMFs can be reconstructed into the noise reduction signal. Since envelope analysis requires the selection of the appropriate central frequency and bandwidth, infogram is utilized to select the values of them. A simulated case is applied to demonstrate the validation of the proposed method. And to further illustrate its practicality, it is employed to perform early fault diagnosis for an experimental case. According to the diagnosis results, the proposed method has conspicuous superiority over the other existing technologies for estimating incipient fault time of the bearing.