Improved group sparse decomposition and its application in gear fault diagnosis

Group Sparse Mode Decomposition (GSMD) is an emerging signal decomposition technique that introduces the concept of group sparsity. It utilizes an ideal filter bank to decompose a signal into a series of orthogonal mode components. However, when decomposing complex vibration signals, GSMD is prone to over-decomposition, which compromises the integrity of fault information, rendering it unsuitable for fault diagnosis. To address this issue, this paper proposes an improved version of GSMD, called Improved Group Sparse Mode Decomposition (IGSMD). IGSMD first employs the group sparsity optimization optimizing strategy to decompose the signal into a series of group sparse sub-components. Further, IGSMD measures the fault intensity of these sub-components based on their periodic characteristics. Finally, the sub-components are fused according to their periodic strength to complete the signal decomposition. It is worth noting that IGSMD overcomes the over-decomposition issue inherent in GSMD when processing complex vibration signals. Additionally, IGSMD preserves the integrity of gear fault information, eliminates noise interference, and is well-suited for practical gear fault diagnosis. Simulation and real-world experimental results demonstrate that the proposed method exhibits superior performance in signal decomposition, enhancing the accuracy of gear fault diagnosis and showing considerable practical value.