Automatic representation and detection of fault bearings in in-wheel motors under variable load conditions. (August 2021)

• Record Type: Journal Article
• Authors: Wang, Xian-Bo; Luo, Luqing; Tang, Lulu; Yang, Zhi-Xin
• Is Part Of: Advanced engineering informatics. Volume 49(2021)
• Publication Date: 2021-08
• Subjects: Fault diagnosis -- Synchronous resampling -- Convolutional neural network -- In-wheel motor -- Time–frequency representation; Computer-aided engineering -- Periodicals; Engineering -- Data processing -- Periodicals; 620.00285
• DOI: 10.1016/j.aei.2021.101321
• Languages: English
• ISSNs: 1474-0346

Abstract

Abstract: The wear fault of the inner and outer race of bearing in an in-wheel motor is vital as the performance of bearing effects the transmission efficiency. Unlike the commonly used vibration analysis-based monitoring methods, the accelerometer cannot be installed inside the in-wheel motor since it would damage the structural reliability. The electricity-related signals sourcing from the motor controller may be a feasible way. In this paper, a load demodulation and normalization method under the condition of no vibration sensor is proposed to solve the problem of fault detection and diagnosis of the in-wheel motor transmission component under the condition of variable load. First, the stator current is selected to reflect the load changes according to the mathematical model of in-wheel motor in the three-phase stationary coordinates. Second, the order tracking is applied to synchronize the sampled signals on time domain with synchronous resampling, which converts the equally spaced signals into the angularly spaced signals. The merits are improving the definition of time–frequency representations (TFRs) of wear fault and avoiding the difficulty to determine the resolution of TFRs caused by load fluctuations. The short-time Fourier transform (STFT) is introduced to convert the angularly spaced signals into the TFRs. Finally, an adapted convolutional neural network (CNN) with random weight initialization and dropout strategy is employed to classify the wear of inner race and outer race. The proposed framework is verified on the in-wheel motor simulation platform. The experimental results show that the proposed method has a higher fault detection precision than the other methods. The core contribution reveals that this paper does not use the vibration-free sensor. Instead, the electrical signals of the controller are used to realize the fault detection and diagnosis of the inner and outer wear of the in-wheel motor bearing under the variable load conditions. Compared with the commonly used fault diagnosis method based on vibration signal analysis, the proposed method is more suitable for the condition monitoring system of the in-wheel motor transmission mechanism.