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Aiming?at?the?low?diagnostic?recognition?rate?caused?by?the difficulty in determining optimal parameters of fault diagnosis model adaptively for industrial robot rolling parts, a fault diagnosis method based on parameter collaborative optimization variational?mode?decomposition?(VMD) - support?vector?machine (SVM) is proposed. An improved grey wolf optimization based on genetic variation is proposed. In this algorithm, a logistic chaotic map is adopted in population initialization, a nonlinear convergence factor is introduced in updating the location of grey wolf, and a genetic variation strategy is used to solve the stagnation phenomenon when the algorithm is stuck in the local optimum. The algorithm is used to optimize the parameters of VMD and SVM collaboratively. Fault?signals are decomposed into intrinsic mode functions (IMF) by?the parameter?optimization?VMD method, and the improved sample entropy of these IMFs are calculated to form feature vectors, which are then brought to SVM for fault diagnosis for rolling parts of an industrial robot. The?simulation?results?show that the proposed method is effective in fault diagnosing, with the accuracy up to 100% under the condition of both noised and noiseless signal, which is superior than the accuracy of other methods such as empirical mode decomposition (EMD), local mean decomposition (LMD), Dual-tree?complex?wavelets (DTCWT) and VMD.