In order to effectively extract the characteristic information of bearing vibration signals and improve the classification accuracy, a composite fault diagnosis method of rolling bearing based on the chaotic honey badger algorithm (CHBA), which optimizes variational mode decomposition (VMD) and extreme learning machine (ELM), is proposed in this paper. Firstly, aiming to solve the problem that the HBA optimization process can easily fall into local optimization and slow convergence speed, sinusoidal chaotic mapping is introduced to improve HBA, and the advantages of CHBA are verified by 23 benchmark functions. Then, taking the Gini index of the square envelope (GISE) as the fitness function, the VMD is optimized with CHBA to obtain the optimal number of modes K and the quadratic penalty factor. Secondly, the first four IMF components with the largest GISE values are selected, and the IMF components are grouped by the “Systematic Sampling Method (SSM)” to calculate the signal energy to form the fault feature vector. Finally, taking the classification error rate as the fitness function, the feature vector is input into the ELM model optimized by CHBA to classify and identify different types of faults. Through experimental analysis, and compared with BP, ELM, GWO-ELM, and HBA-ELM, this method has better diagnosis results for composite faults, and the accuracy of fault classification can reach 100%, which provides a new way to solve the problem of composite fault diagnosis.

In this paper, a novel composite fault diagnosis method combining adaptive feature mode decomposition (FMD) and minimum noise amplitude deconvolution (MNAD) is proposed. Firstly, chaos mapping and leader mutation selection strategy were introduced to improve the Northern Goshawk algorithm (NGO), and a chaotic leadership Northern Goshawk optimization (CLNGO) algorithm was proposed. The advantages of the CLNGO algorithm in convergence accuracy and speed were verified by 12 benchmark functions. Then, a new index called sparse pulse and cyclicstationarity (SPC) is proposed to evaluate signal sparsity. Finally, SPC is used as the fitness function of CLNGO to optimize FMD and MNAD. The optimal decomposition mode n and filter length of FMD, and filter length L and noise ratio $\rho$ of MNAD are selected. The CLNGO-FMD is used to decompose signal into different modes. The signal is reconstructed based on the kurtosis criterion and the CLNGO-MNAD method is used to reduce the noise of the reconstructed signal twice. The experimental results show that the proposed method can achieve the enhancement of weak features and the removal of noise to extract the fault feature frequency adaptively. Compared with EMD, VMD, MOMEDA, MCKD and other methods, the proposed method has better performance in fault feature frequency extraction, and it is effective for the diagnosis of single faults and composite faults.