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The health condition of rolling bearing possesses a significant impact on the safety and efficiency of rotating machinery. Accordingly, to diagnose the faults in rolling bearings effectively and accurately, a novel hybrid approach coupling variational mode decomposition (VMD), composite multiscale fine-sorted dispersion entropy (CMFSDE) and support vector machine (SVM) optimized by mutation sine cosine algorithm and Harris hawks optimization (MSCAHHO) is proposed in the paper. Firstly, VMD is employed to decompose raw vibration signals with various fault types into different sets of intrinsic mode functions (IMFs) to weaken the non-stationarity of signals, before which the parameter K of VMD is decided through central frequency observation method. Subsequently, CMFSDE is put forward in this paper to analyze the complexity of fault signals by fully considering the relationship between neighboring elements based on composite multiscale technique, with which the representative features of different fault samples are extracted to construct feature vectors. Later, an enhanced hybrid optimization approach called MSCAHHO is proposed by integrating sine cosine algorithm (SCA) and a periodic mutation strategy to improve Harris hawks optimization (HHO). Then, MSCAHHO is employed to optimize the parameters of SVM, after which the optimal SVM model is utilized for fault classification. Finally, the performance of the proposed methodology is evaluated with four validity indices through comparative experiments. The experimental results reveal that the proposed VMD-CMFSDE-MSCAHHO-SVM method achieves favorable diagnosis results comparing with other relevant methods. INDEX TERMS Fault diagnosis, variational mode decomposition, composite multiscale fine-sorted dispersion entropy, support vector machine, hybrid mutation SCA-HHO.

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A hydropower generator (HPG) is the key equipment for power grid peaking and frequency modulation, whose faults are usually in the form of vibration. Hence, it is of great significance to measure the vibration trend of an HPG which can contribute to achieving advanced management and predictive maintenance, thus improving the stability of the power system and enhancing the economic efficiency. For this purpose, a novel measuring model for the vibrational trend of an HPG based on optimal variational mode decomposition (OVMD) and a least squares support vector machine (LSSVM) improved with chaotic sine cosine algorithm optimization (CSCA) is proposed in this paper. To begin with, the mode number and Lagrange multiplier updating step of the variational mode decomposition (VMD) are determined using the center frequency observation method and the proposed least squares error index (LSEI), thus achieving the OVMD decomposition; after which the non-stationary vibration sequence is decomposed into a set of intrinsic mode functions (IMFs). Then, the inputs and outputs of the LSSVM model for the corresponding IMF are deduced by phase space reconstruction. Subsequently, the LSSVM predictor optimized by the improved sine cosine algorithm (SCA) with the combination of chaotic variables is employed to predict each IMF. Finally, the ultimate measuring results of the original trend are calculated by accumulating all the predicted IMFs. Furthermore, the validity of the proposed method is confirmed by an engineering application as well as comparative analyses.

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As a crucial and widely used component in industrial fields with great complexity, the health condition of rotating machinery is directly related to production efficiency and safety. Consequently, recognizing and diagnosing rotating machine faults remain to be one of the main concerns in preventing failures of mechanical systems, which can enhance the reliability and efficiency of mechanical systems. In this paper, a novel approach based on blind parameter identification of MAR model and mutation hybrid GWO-SCA optimization is proposed to diagnose faults for rotating machinery. Signals collected from different types of faults were firstly split into sets of intrinsic mode functions (IMFs) by variational mode decomposition (VMD), the decomposing mode number K of which was preset with central frequency observation method. Then the multivariate autoregressive (MAR) model of all IMFs was established, whose order was determined by Schwartz Bayes Criterion (SBC), and all parameters of the model were identified blindly through QR decomposition, where key features were subsequently extracted via principal component analysis (PCA) to construct feature vectors of different fault types. Afterwards, a hybrid optimization algorithm combining mutation operator, grey wolf optimizer (GWO), and sine cosine algorithm (SCA), termed mutation hybrid GWO-SCA (MHGWOSCA), was proposed for parameter selection of support vector machine (SVM). The optimal SVM model was later employed to classify different fault samples. The engineering application and contrastive analysis indicate the availability and superiority of the proposed method.

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