A novel hybrid method based on KELM with SAPSO for fault diagnosis of rolling bearing under variable operating conditions

It's a great challenge to detect fault types from the measured vibration signals automatically and efficiently under variable running condition. A novel hybrid method based on simulated annealing particle swarm optimization (SAPSO) and improved kernel-based extreme learning machine (IKELM) is proposed for diagnosing rolling bearing faults under variable conditions in the paper. Multi-domain fault features from time domain, frequency domain and time-frequency domain are extracted and combined to reconstruct a high-dimensional feature collection by applying VMD, and isometric feature mapping is adopted to lower the dimension of the highdimensional feature collection. The parameters of KELM are optimized to adapt for the fault feature identification by using SAPSO method. The proposed SAPSO-IKELM can be applied to diagnose the fault automatically and accurately from the two rolling bearing experiments. The results indicate SAPSO-IKELM could remove dependence on artificial feature selection and reach high classification accuracy under varying conditions.

Automated summary

The paper proposes a novel hybrid method for diagnosing rolling bearing faults under variable conditions. By extracting fault features from multiple domains and using improved learning machines, the method allows for efficient and automatic fault detection.