Rolling Bearing Fault Diagnosis Based on Successive Variational Mode Decomposition and the EP Index

Abstract

Rolling bearing is an important part guaranteeing the normal operation of rotating machinery, which is also prone to various damages due to severe running conditions. However, it is usually difficult to extract the weak fault characteristic information from rolling bearing vibration signals and to realize a rolling bearing fault diagnosis. Hence, this paper offers a rolling bearing fault diagnosis method based on successive variational mode decomposition (SVMD) and the energy concentration and position accuracy (EP) index. Since SVMD decomposes a vibration signal of a rolling bearing into a number of modes, it is difficult to select the target mode with the ideal fault characteristic information. Comprehensively considering the energy concentration degree and frequency position accuracy of the fault characteristic component, the EP index is proposed to indicate the target mode. As the balancing parameter is crucial to the performance of SVMD and must be set properly, the line search method guided by the EP index is introduced to determine an optimal value for the balancing parameter of SVMD. The simulation and experiment results demonstrate that the proposed SVMD method is effective for rolling bearing fault diagnosis and superior to the variational mode decomposition (VMD) method.

Editorial for Special Issue: Machine Health Monitoring and Fault Diagnosis Techniques

“…The theme of this Special Issue focuses on machine health monitoring and fault diagnosis techniques, especially intelligent fault diagnosis. This Special Issue highlights 18 articles that can be divided into four categories: condition monitoring [ 1 , 2 , 3 , 4 ], degradation process prediction [ 5 , 6 , 7 , 8 ], intelligent diagnostic algorithms [ 9 , 10 , 11 , 12 , 13 , 14 , 15 ], and sensor fault detection [ 16 , 17 , 18 ]. In addition to the traditional bearing vibration signals, the research objects include the electrode signals, blade vibration signals, diesel engine vibration signals, and bearing heat signals.…”

“…In [ 13 ], a rolling bearing fault diagnosis method was proposed based on successive variational mode decomposition (SVMD) and an energy concentration and position accuracy (EP) index. The EP index effectively indicated a target mode for the characteristic fault information, and a line-searching method that was guided by the EP index optimized the balancing parameter of the SVMD.…”

Solving the inverse problem in electrocardiography imaging for atrial fibrillation using various time-frequency decomposition techniques based on empirical mode decomposition: A comparative study

“…( Nazari and Sakhaei, 2020 ) presented SVMD to discover the optimal with the use of a heuristic method to adaptively select the best number of modes k and the weighting factor in VMD. In contrast to VMD, SVMD incorporates a new penalty function to lessen spectral overlap ( Guo et al, 2022 ).…”

Electric shock feature extraction method based on adaptive variational mode decomposition and singular value decomposition

“…In contrast, the variational mode decomposition (VMD) has a simpler algorithm implementation, and superior decomposition performance, making it suitable for nonlinear and non-stationary feature extraction. VMD has been widely applied in overvoltage, and bearing fault identification and transformer partial discharge recognition [16][17][18][19][20][21][22][23]. Nevertheless, VMD cannot adaptively determine the number of mode components.…”

“…Sahani et al [18] used a particle swarm optimization algorithm to determine K values, but the calculation was computationally intensive, and the choice of an optimization algorithm influenced the results. Li et al [22] proposed continuous variational mode decomposition (SVMD), which overcomes the problem of selecting the K value of the modal component of VMD, but it is difficult to select the balance parameter α of the SVMD algorithm [23].…”