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In this work, an adaptive envelope spectrum (AES) technique is proposed for bearing fault detection, especially for analyzing signals with transient events. The proposed AES technique first modulates the signal using the empirical mode decomposition to formulate the representative intrinsic mode functions (IMF), and then a novel IMF reconstruction method is proposed based on a correlation analysis of the envelope spectra. The reconstructed signal is post-processed by using an adaptive filter to enhance impulsive signatures, where the filter length is optimized by the proposed sparsity analysis technique. Bearing health conditions are diagnosed by examining bearing characteristic frequency information on the envelope power spectrum. The effectiveness of the proposed fault detection technique is verified by a series of experimental tests corresponding to different bearing conditions.

This paper presents a detailed investigation on an asymmetric magnetic-coupled bending-torsion piezoelectric energy harvester based on harmonic excitation. There is an eccentricity between the shape center of moving magnets and the axis of the piezoelectric beam, which results in the bending and torsion simultaneously in working condition. The distributed mathematical model is derived from the energy method to describe the dynamic characteristics of the harvester, and the correctness of the model is verified by experiments. To further demonstrate the improvement performance of the proposed energy harvester, the bending-torsion energy harvester (i.e. magnetic-coupled was not configured) is experimented and compared. The theoretical and experimental results indicate that the average power increases about 300% but the resonance frequency decreases approximately 2 Hz comparing to the harvester without magnetic-coupled. According to the characteristic of distributed parameter model, the magnetic force and the size of the piezoelectric beam are investigated respectively. And the lumped-parameter model is introduced to analyze the steady-state characteristic. Accordingly, this paper provides a feasible method to improve performance for piezoelectric energy harvester.

This paper presents a magnetically coupling bending-torsion piezoelectric energy harvester based on vortex-induced vibration from low-speed wind. The theoretical model of the energy harvester was formulated and validated by wind tunnel experiments. Numerical and experimental results showed that the power output and bandwidth of the proposed harvester are improved about 180% and 230% respectively compared with the nonmagnetic coupling harvester. Furthermore, the effects of cylinder, piezoelectric layer, load resistance, and magnetic nonlinear parameters on the harvester were investigated based on the distributed parameter model. The results showed that the length of cylinder hardly affect output power, but the diameter of cylinder presented complicated influences. The width of piezoelectric beam was negatively correlated with the torsion angle. With increasing the length of piezoelectric layer, an optimal wind velocity and load resistance can be obtained for the maximum output power. With decreasing of the distance between two magnets, the resonant bandwidth, the optimal power output, and torsion angle can be enhanced, respectively. Besides, the magnetic potential energy increased owing to the magnetically coupling, which led to the improvement of onset speed for the energy harvester. This study provides a guideline on improving the performance of bending-torsion vibration piezoelectric energy harvester.

In order to evaluate the degradation state of the mechanical equipment and master the information of the remaining useful life (RUL) of the bearing accurately, this paper presents a method for predicting the remaining useful life of bearings based on mutual information (MI) and support vector regression (SVR) model. The proposed method includes two steps of online and offline, the offline step is used to build a degradation model of the bearing by learning, the online step uses the degradation model to predict the remaining useful life. By analyzing the experimental data of bearing full lifetime degradation, the results show that the method can effectively simulate the bearing degradation process and predict the remaining useful life of the bearing.