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The escalator is an essential large-scale public transportation equipment in a subway station. Once a failure occurs, the operation will be affected if the escalator is small, and it will cause a safety accident. As an important structural part of the escalator, the loosening of its fixing bolts will inevitably lead to abnormal operation of the escalator. Aiming at the problem that it is difficult to extract the fault characteristics of cascade vibration signals, this paper proposes a method of combining variational modal decomposition (VMD) and higher order statistics (HOS) to extract escalator fault characteristics. This method first performs VMD decomposition on the original vibration signal to obtain K intrinsic modal components (IMF); then performs singular value decomposition (SVD) noise reduction on the main IMF component, and reconstructs the denoised main IMF component to obtain a new Finally, the new signal fault characteristics are extracted through high-order statistics, and the random forest classification algorithm is used to classify and identify three different vibration signal samples to determine the type of cascade vibration fault. Experimental results show that this method can effectively extract fault features and realize fault diagnosis and classification.