<p>Fault diagnosis of railway point machines becomes an urgent task due to their high fault rate (about 40% of the total fault number of railway signaling systems). Different from the traditional motor current curve-based diagnosis methods, considering the advantages of high precision and easy acquisition of vibration signals, this paper presents a novel vibration signals-based fault diagnosis method, aiming to address the issue of high discrimination feature extraction and selection of complex signals, and realizing high-precision fault diagnosis. First, to address the issue that traditional wavelet packet energy entropy is insufficient to characterize the fault information contained in more complex monitored signals, considering the coarse-grained signals also contain many fluctuations and energy information under different scales, coarse-grain process is introduced into wavelet packet decomposition, forming multi-scale wavelet packet energy entropy. Second, to reduce information loss during the classical coarse-grain process, an improved coarse-grain method using sliding window strategy is proposed. Third, aiming to the problem that the existing feature extraction methods mainly pay attention to the monitored signal itself, considering there are also some important information contained in the multi-order derivatives of the monitored signals, improved multi-scale derivative wavelet packet energy entropy is developed by introducing multi-order derivatives into multi-scale wavelet packet energy entropy, further enriching the fault information. Then, to ensure good robustness of selected features, a two-stage feature selection method combining ReliefF and support vector machine-recursive feature elimination (SVM-RFE) is presented to select the optimal feature subset. Finally, the diagnosis effect is verified using radial basis function (RBF)-SVM and compared to some feature extraction and selection methods. The fault diagnosis accuracies under both normal-reverse and reverse-normal directions reach 100%, demonstrating its feasibility. Besides, the performance of the presented method is also verified on the CWRU data set. This paper can also provide references for feature extraction and fault diagnosis of other machine learning-based diagnosis fields. Besides, it also provide a new way for engineering application of fault diagnosis of railway point machines, and provide theoretical support for on-site maintenance staff.</p>