Intelligent fault detection of rotating machines is essentially a pattern classification issue. At the same time, effectively obtaining fault features from the measured signals is a key step to timely diagnose the health status of rotating machinery and evaluate the results of fault classification. To accurately obtain effective fault information to enhance fault accuracy, this article proposes a novel fault detection scheme based on cyclic morphological modulation spectrum (CMMS) and hierarchical Teager permutation entropy (HTPE). In this scheme, first, CMMS was developed to analyze the measured signal to obtain a series of CMMS slices with different frequency bands, which solved the deficiencies of manual empirical selection of frequency band bandwidth in the traditional cyclic modulation spectrum. Subsequently, by integrating Teager energy operator into hierarchical permutation entropy (HPE), an improved feature selection method named hierarchical Teager permutation entropy (HTPE) is presented to obtain fault information of different frequency band slices, which can improve the fault feature extraction capability of HPE. Finally, the acquired HTPE-based vectors are integrated into the extreme learning machine classifier to achieve fault classification of rotating machinery under different working conditions. The proposed scheme is validated by experimental cases including cylindrical roller bearings and planetary gearboxes. The analysis results indicate that the proposed scheme not only can effectively obtain the fault features, but also accurately realize the classification and recognition of the fault mode. In addition, the proposed scheme can achieve higher detection accuracy than some existing algorithms.