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 %.
Highlights
The gear transmission is one of the most widely used transmission forms in the mechanical system for the gear box has its own advantages, such as fixed transmission ratio, transmission torque, compact structure
The vibration-signal-based analysis technologies are still the main and popular means of gear and bearing condition monitoring and fault diagnosis, because the change of the vibration pattern can reflect the occurrence of a fault, and vibration signals are acquired by vibration sensors
To evaluate the performance of the proposed method, the experiments were carried out at a QPZZ-II rotating machinery fault simulation rig as shown in Fig. 2, which manufactured by Jiangsu Qianpeng Diagnosis Engineering Co., Ltd
Summary
The gear transmission is one of the most widely used transmission forms in the mechanical system for the gear box has its own advantages, such as fixed transmission ratio, transmission torque, compact structure. A DPCA-BASED ONLINE FAULT INDICATOR FOR GEAR FAULTS USING THREE-DIRECTION VIBRATION SIGNALS. Frequency domain based methods, such as fast Fourier transform, have been successfully used to online monitor tooth root crack faults of gear, the Fourier transform is powerless when the signals contain large amounts of non-stationary or transient information. The biggest advantage of the PCA/DPCA based methods is the models are established offline and online calculating burden is low, so they are very suitable for online applications. A novel fault indicator is presented based on Hotelling T statistic and the square predicted error (SPE) statistic of DPCA aiming at proposing an efficient approach for online gear monitoring and fault diagnosis. The proposed method with simple construction and no complex computation can be used online because of the DPCA model offline established and on need to extract a lot of features. Experimental results show that we can clearly and judge the health state and fault types of the gear only by monitoring the proposed indicator
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