ALIF can effectively analyse and process nonlinear and nonstationary signals. used the basic solution system of Fokker–Planck (FP) differential equations as the filter function to extend the IF algorithm they proposed the Adaptive Local Iterative Filter (ALIF) algorithm. proposed an iterative filter (IF) algorithm, which follows the same algorithm framework as EMD and uses low-pass filtering to obtain the upper and lower mean functions of the signal envelope. In order to improve the stability and convergence of the mean function of the upper and lower envelopes under disturbances, Lin et al. At the same time, they also used the 1.5-dimensional spectrum to suppress noise and enhance the impact signal, combining the two to achieve effective separation of composite faults in the rolling bearings. optimized several important parameters in the variational modal decomposition to improve the decomposition performances. Effectiveness and superiority of the latter method are demonstrated by a comparative analysis. proposed a parameterized local eigenscale decomposition method for the discontinuity of the first derivative of the local eigenscale decomposition method, applied it to the composite fault simulation signal and the bearing experimental signal, and verified the method’s performance. extended the local mean decomposition to a complex local mean decomposition and were successful in applying it to the composite fault diagnosis of rolling bearings. To effectively resolves rolling bearing problems, researchers have proposed many adaptive mode decomposition methods inspired by the idea of EMD, including local mean decomposition (LMD), empirical wavelet transform (EWT), and variational modal decomposition (VMD). Cubic spline interpolation has either underfitting or overfitting and is unstable under the noise interferences. However, due to the lack of EMD’s strict mathematical theoretical derivation, singular points in the signal easily lead to modal aliasing occurrences. Ma Xinna and others combined EMD with an adaptive notch filter to realize the adaptive separation and diagnosis of rolling bearing composite faults. proposed an empirical mode decomposition (EMD) algorithm. In view of the above situation, most of the methods currently proposed by researchers are based on vibration signal processing composite fault diagnosis technology for rolling bearings, in which the signal decomposition method is one of the effective methods for processing vibration signals. Consequently, effective monitoring of the integrity health status of rolling bearings and timely elimination of hidden issues play an important role in ensuring safe and reliable equipment operation, reduction in economic and capital losses, and avoiding accidents. Statistical analysis indicates that approximately 30% of all rotating machinery equipment failures are caused by failure of rolling bearings. In addition to a single failure, the failure types can also easily present as composite failure formed due to simultaneous occurrences of multiple types of failures. After an extended period of operation, these components are prone to failure. However, the actual working environments of rolling bearings are very harsh. Rolling bearings have been widely used in many engineering fields. Rolling bearings are one of the basic components and play an important role in various types of industrial equipment. Experimental results show that, compared with EMD-KELM model, ALIF-ELM model, ALIF-BP model, and IFD-KELM model, the rolling bearing composite fault diagnosis method based on the ALIF-KELM model has higher classification accuracy. Finally, the feature vectors of the training set and the test set are input into the KELM model for fault classification. First, the basic concepts of ALIF and KELM are introduced, and then ALIF is used to decompose the sample data of vibration signals of different bearing states so that each sample can get several IMFs, select the top K IMFs containing the main fault information from each sample, calculate the energy feature and sample entropy of each IMF, and construct a fault feature vector with a dimension of 2K. Aiming at the shortcomings of difficult classification of rolling bearing compound faults and low recognition accuracy, a composite fault diagnosis method of rolling bearing combined with ALIF and KELM is proposed.
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