A Hierarchical Decision Fusion Diagnosis Method for Rolling Bearings

Abstract

In order to achieve accurate fault diagnosis of rolling bearings, a hierarchical decision fusion diagnosis method for rolling bearings is proposed. The hierarchical back propagation neural networks (BPNNs) architecture includes a fault detection layer, fault isolation layer and fault degree identification layer, which reduce the calculation cost and enhance the maintainability of the fault diagnosis algorithm. By wavelet packet decomposition and signal reconstruction of the raw vibration signal of a rolling bearing, the time-domain features of the reconstructed signals are extracted as the input of each BPNN and the accuracy of fault detection, fault isolation and degree estimation are improved. By using the majority voting method, the diagnosis results of multiple BPNNs are fused, which avoids the missed diagnosis and misdiagnosis caused by the insensitivity of a vibration characteristic to a specific fault. Finally, the proposed method is verified experimentally. The results show that the proposed method can accurately detect the fault of rolling bearings, recognize the fault location and estimate the fault severity under different operating conditions.