A DPCA-based online fault indicator for gear faults using three-direction vibration signals

Abstract

For online monitoring and identifying gear faults, a new fault indicator is proposed based on a multivariate statistical technique, dynamic principal component analysis (DPCA), under variable load conditions. In this method, a tri-axial vibration sensor is used to acquire the 3-direction vibration signals of gear in the gear box because it can pick up more abundant fault information than a single axis sensor does. By monitoring the value of the fault indicator, the running state of the gear (normal condition or faults) can be directly identified according to the set thresholds without using any other fault classification methods. To verify the effectiveness, the proposed method is applied on the QPZZ-II rotating machinery fault simulation rig in which the root crack and the tooth broken faults are introduced into the gearbox's driving gear. Experimental results show that the fault indicator not only can effectively reveal the health state of the gear, but also is without being influenced by the load fluctuation. And, the accuracy rate of fault diagnosis is over 96 %.

Rotating Machinery Fault Diagnosis Based on Improved Multiscale Amplitude-Aware Permutation Entropy and Multiclass Relevance Vector Machine

“…The rolling bearing experiment adopts the famous public data set provided by Case Western Reserve University Bearing Data Center [29]. The gearbox experiment is carried out at the QPZZ-II vibration analysis and fault diagnosis test platform system of rotating machinery manufactured by Jiangsu Qianpeng Diagnosis Engineering Co., Ltd. (Zhenjiang, China) [30].…”