Abstract
Wear degree detection of gears is an effective way to prevent faults. However, due to the interference of high-speed meshing vibration and environmental noise, the weak vibration signal generated by the gear is easily covered by the noise, which makes it difficult to detect the degree of wear. To address this issue, this paper proposes a novel gear wear degree diagnosis method based on local weighted scatter smoothing method (LOWESS), wavelet packet transform (WPT), and least square support vector machine (APSO-LSSVM) optimized by adaptive particle swarm algorithm. According to the low signal-to-noise ratio characteristic of gear vibration signal, LOWESS is first used to preprocess the signal spectrum. Then, the characteristic parameters used to characterize gear wear are extracted from different decomposition depths by WPT and, finally, combined with APSO-SVM to diagnose the degree of gear wear. Compared with the basic least squares support vector machine, the improved method has better performance in sample classification. The experimental results show that the method in this paper can effectively reduce the diagnosis error caused by background noise, and the diagnosis accuracy reaches 98.33%, which can provide a solution for the health status monitoring of gears.
Highlights
With the popularity of electric vehicles, the evaluation of mechanical durability of electric vehicles has always been a research hotspot in the field of electric drive systems
As devices that transmit power or rotational motion, the health status of gears is closely related to the life of an electric vehicle’s drivetrain. erefore, there is a great need for advanced wear diagnosis technology to minimize unplanned downtime caused by gear wear and predict its future development trend so that corrective measures can be taken in time before any further damage occurs to the machine [1, 2]
Since the degree of wear detection can prevent the occurrence of fault, it is necessary to develop a new weak fault detection method that can effectively detect gear wear to learn the wear characteristics and accurately evaluate the health of the gear. e gearbox health monitoring technology based on vibration analysis is very effective for the diagnosis of gear wear, as the change of the vibration signal is a response to gear defects and wear growth. is study adopts the diagnosis method based on vibration analysis
Summary
With the popularity of electric vehicles, the evaluation of mechanical durability (reliability) of electric vehicles has always been a research hotspot in the field of electric drive systems. E above studies have proposed new methods or improved related algorithms for the early detection of rotating machinery, but the early health monitoring of gears under the background of strong noise still needs further research. Li et al [15] used the second-generation redundant wavelet packet transform to extract statistical features, diagnosed gear faults through support vector machines, and applied this method to gearbox fault diagnosis. E novelty of the proposed method is the implementation of signal smoothing techniques to filter the noise and the APSO algorithm to select the optimal solution of the vector machine parameters to facilitate a high-quality and efficient training process The extracted features were used to train LS-SVM to classify the health status of gear in the wear process. e novelty of the proposed method is the implementation of signal smoothing techniques to filter the noise and the APSO algorithm to select the optimal solution of the vector machine parameters to facilitate a high-quality and efficient training process
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