油液光谱数据诊断综合传动装置异常磨损定位方法

Wear is one of the important factors affecting the working reliability and service life of the integrated transmission device. Abnormal wear of the components of the integrated transmission device will reduce its operating efficiency and even cause its random failure, resulting in significant economic and military losses. Therefore, it has become an important method to improve the reliability of integrated transmission devices to quickly and accurately detect the characteristics of wear elements and locate the parts with abnormal wear by using oil spectral data. However, oil spectral data samples generally contain many interfering and additive elements, etc. Clustering, principal component analysis, weighted fusion and other methods commonly used in current studies lack consideration of the increase of abnormal wear of specific element concentration indexesover time. In order to analyze the wear state of different parts of the integrated transmission, a method of abnormal wear location analysis of parts based on oil spectral data was proposed. A clustering method based on the correlation distance of the time window was proposed to separate the elements representing the wear states of different parts. The wear trend classification method of wear elements was proposed, with high wear trend elements as the cluster center, so the clustering results could be interpreted. The weight of component wear elements was determined by classification coefficient, and the wear elements of each component were fused to obtain the representation of the wear state of different components. Abnormal wear can be identified by abnormal wear threshold value to locate abnormal wear of parts. The parts and period of abnormal wear were detected and judged. The test results show that Fe, Cu and Pb have the highest wear trend classification coefficient and carry a lot of wear information, which can effectively characterize the wear state of the device. The centralized clustering method based on the correlation distance of the time window successfully divides the oil spectral data into Fe, Cu and Pb, which can effectively characterize the wear state of the whole body, friction plate and gear group. The weighted fusion method based on the classification coefficient can effectively detect and judge the abnormal wear parts and period of the device and provide technical guidance for the subsequent fault prevention and maintenance.