Sensitive and accurate fault features from the vibration signals of planetary gearboxes are essential for fault diagnosis, in which extreme learning machine (ELM) techniques have been widely adopted. To increase the sensitivity of extracted features fed in ELM, a novel feature extraction method is put forward, which takes advantage of the transient dynamics and the reconstructed high-dimensional data from the original vibration signal. First, based on fast kurtosis analysis, the range of transient dynamics of a vibration signal is located. Next, with the extracted kurtosis information, with variational mode decomposition, a series of intrinsic mode functions are decomposed; the ones that fall into the obtained ranges are selected as transient features, corresponding to maximum kurtosis value. Fed by the transient features, a hierarchical ELM model is well-trained for fault classification. Furthermore, a denoising auto-encoder is used to optimize input weight and threshold of implicit learning node of ELM, satisfying orthogonal condition to realize the layering of its hidden layers. Finally, a numerical case and an experiment are conducted to verify the performance of the proposed method. In comparison with its counterparts, the proposed method has a better classification accuracy in the aiding of transient features.

The vibration signal of rolling bearing exhibits the characteristics of energy attenuation and complex time-varying modulation caused by the transmission with multiple interfaces and complex paths. In view of this, strong ambient noise easily masks faulty signs of rolling bearings, resulting in inaccurate identification or even totally missing the real fault frequencies. To overcome this problem, we propose a reinforced ensemble local mean decomposition method to capture and screen the essential faulty frequencies of rolling bearing, further boosting fault diagnosis accuracy. Firstly, the vibration signal is decomposed into a series of preliminary features through ensemble local mean decomposition, and then the frequency components above the average level are energy-enhanced. In this way, principal frequency components related to rolling bearing failure can be identified with the fast spectral kurtosis algorithm. Finally, the efficacy of the proposed approach is verified through both a benchmark case and a practical platform. The results show that the selected fault characteristic components are accurate, and the identification and diagnosis of rolling bearing status are improved. Especially for the signals with strong noise, the proposed method still could accurately diagnose fault frequency.