Design of fault diagnosis algorithm for electric fan based on LSSVM and Kd-Tree

Currently, the complexity of mechanical equipment is increasing rapidly together with the poor working environment. If a fault occurs, how to find the fault in time becomes a poser. Motivated by this existing problem, based on the analysis of the fault characteristics of electric fans, a fault diagnosis algorithm model based on Least Square Support Vector Machine (LSSVM) and Kd-Tree was proposed. This algorithm was based on the LSSVM optimized by the Cuckoo Search (CS). This paper used the “one-to-many” principle and the sigma threshold method to introduce k-Nearest Neighbor (kNN) which was implemented by Kd-Tree as a secondary classifier to optimize the model. In data preprocessing, the data based on time series was first processed by Empirical Mode Decomposition (EMD) and the energy ratios were calculated, and the the above results were degraded by Principal Component Analysis (PCA) and normalized. On top of that, in case of the uncertain fault types, the Fuzzy C-Means clustering algorithm (FCM) optimized by Particle Swarm Optimization (PSO) was proposed to provide a priori knowledge for the model. In this paper, the algorithm model, FCM and other parts were verified to prove that the performance and generality of the algorithm were better than those of general classification algorithms, and relevant experiments were conducted for different data processing methods to expand the universality of the algorithm.