A New Method for Fault Diagnosis of Hydraulic System Based on Improved Empirical Wavelet Transform and Kernel Extreme Learning Machine

Abstract

To address the problem that multivariable faults in hydraulic systems are difficult to diagnose accurately in a short time, a new signal processing method combining an improved empirical wavelet transform with a kernel extreme learning machine optimized by the Pelican optimization algorithm is proposed. First, the problem of unreasonable spectral partitioning of the empirical wavelet transform is solved using an improved k-means clustering method, which upgrades the empirical wavelet transform in the maximum scale space and then adaptively decomposes the acquired pressure signal to obtain a series of sub-signal components, thus reducing the dimensionality of multivariate faults and saving computation time. Second, 17 features of each sub-signal component are calculated and input to the kernel extreme learning machine for training, and the sequence-forward selection strategy is used to select the optimal features from the kernel extreme learning machine to ensure the basic accuracy and efficiency of prediction. Finally, a Pelican optimization algorithm improved kernel extreme learning machine algorithm is proposed for fast classification of faults. Experiments show that the method can diagnose a variety of faults in hydraulic systems quickly and accurately, with a diagnostic accuracy of 97.24 percent, which is better than other methods.

Keywords

• Improved empirical wavelet transform
• hydraulic systems
• multivariable fault diagnosis
• pelican optimization algorithm
• kernel extreme learning machine