A new improved Kurtogram was proposed in this paper. Instead of Kurtosis, correlated Kurtosis of envelope signal extracted from the wavelet packet node was used as an indicator to determine the optimal frequency band. Correlated Kurtosis helps to determine the fault related impulse signals not affected by other unrelated signal components. Finally, two simulated and three experimental bearing fault cases are used to validate the effectiveness of proposed method and to compare with other similar methods. The results demonstrate it can locate resonant frequency band with a high reliability than two previous developed methods by Lei et al. and Wang et al. especially for the incipient faults under low load.

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Tracking degradation of mechanical components is very critical for effective maintenance decision making. Remaining useful life (RUL) estimation is a widely used form of degradation prediction. RUL prediction methods when enough run-to-failure condition monitoring data can be used have been fully researched, but for some high reliability components, it is very difficult to collect run-to-failure condition monitoring data, i.e., from normal to failure. Only a certain number of condition indicators in certain period can be used to estimate RUL. In addition, some existing prediction methods have problems which block RUL estimation due to poor extrapolability. The predicted value converges to a certain constant or fluctuates in certain range. Moreover, the fluctuant condition features also have bad effects on prediction. In order to solve these dilemmas, this paper proposes a RUL prediction model based on neural network with dynamic windows. This model mainly consists of three steps: window size determination by increasing rate, change point detection and rolling prediction. The proposed method has two dominant strengths. One is that the proposed approach does not need to assume the degradation trajectory is subject to a certain distribution. The other is it can adapt to variation of degradation indicators which greatly benefits RUL prediction. Finally, the performance of the proposed RUL prediction model is validated by real field data and simulation data.

Bearing and planetary gearbox are important for rotating machinery. However, their faults often cause the stop of the machinery or even fatal casualties. Vibration signal contains the status information of the rotating machinery, which is covered by the strong noise. Stochastic resonance (SR) is a noise-benefit phenomenon, which can detect the weak fault characteristic signal from the vibration signal under strong noise. To detect the fault of bearing or planetary gearbox effectively, SR based on Wood-Saxon potential which only has on potential well called WSSR is studied, and a novel fault diagnosis strategy based on WSSR is proposed. The effect of every WSSR parameter, anti-noise capability of WSSR under different noise intensities and optimal frequency response of WSSR under different driving frequency are analyzed by simulation. To verify the effectiveness of our proposed fault diagnosis strategy based on WSSR, three preset fault tests of bearing and two of planetary gearbox are carried out. Bi-stable SR is also used for comparison. The results show that our proposed fault diagnosis strategy is more effective for the fault detection of bearing and planetary gearbox than bi-stable SR.

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