Abstract: This paper defines a fault diagnosis model based on regularized weighted multiple kernel ensemble extreme learning machine under p-norm constraints for the problems of imbalanced distribution of various types of fault samples and the low accuracy of fault diagnosis of existing algorithms. Under the p-norm constraint, the model performs two kinds of adaptive sample weight allocation based on the size of various types of fault samples. At the same time, it combines the multi-source data fusion ability and extreme learning machine of multiple kernel learning (MKL) combined with the characteristics of high efficiency of Extreme Learning Machine (ELM) operation, the weight W of the sample is integrated into the optimization objective function of the multiple kernel extreme learning machine. Through the Adaboost integration strategy, the weight of the information-rich samples in the model is adaptively increased, thereby significantly improving the accuracy of fault diagnosis. Taking 6 UCI public data sets and 1 actual installation case as examples, fault diagnosis experiments were carried out. The results show that compared with KELM, WKELM (W(1) and W(2) weighted methods) and l1-MKELM、lp-MKELM、TRMKELM, the diagnostic accuracy is significantly improved. The diagnostic performance of the model is limited。