Aircraft bearing fault diagnosis method based on enhanced weighted heterogeneous integrated learning

• Main Authors: HUANG LAN, CAO LIANG, XU ZHI, CHEN LIJING, YAO XIAOHAN, WANG JINGLIN, SHAN TIANMIN, HUANG YUJING, JIE ZHENGUO
• Format: Patent
• Language: Chinese; English
• Published: 21.10.2022
• Subjects: CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; HANDLING RECORD CARRIERS; PHYSICS; PRESENTATION OF DATA; RECOGNITION OF DATA; RECORD CARRIERS

The invention discloses an aviation bearing fault diagnosis method based on enhanced weighted heterogeneous integrated learning, and the method comprises the steps: firstly, extracting the vibration data features of an aviation bearing, forming a multi-dimensional fault feature sample set, dividing the sample set into a training set and a test set, and carrying out the normalization processing of the training set and the test set; then, an enhanced weighted heterogeneous integrated learning model is constructed, and the structure of the model comprises a base classifier model, an enhanced weighted model and a meta classifier model from top to bottom; carrying out parameter setting and optimization on the model; and finally, inputting the test sample into the aviation bearing fault diagnosis model based on the enhanced weighted heterogeneous integrated learning model, and outputting an aviation bearing fault diagnosis result. According to the method, the implicit relationship between the vibration data of the