In recent years, wireless sensor networks (WSNs) have grown dramatically and made a great progress in many applications. But having limited life, batteries, as the power sources of wireless sensor nodes, have restricted the development and application of WSNs which often requires a very long lifespan for better performance. In order to make the WSNs prevalent in our lives, an alternative energy source is required. Environmental energy is an attractive power source, and it provides an approach to make the sensor nodes self-powered with the possibility of an almost infinite lifetime. The goal of this survey is to present a comprehensive review of the recent literature on the various possible energy harvesting technologies from ambient environment for WSNs.

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Bearing fault diagnosis has been a challenge in the monitoring activities of rotating machinery, and it's receiving more and more attention.The conventional fault diagnosis methods usually extract features from the waveforms or spectrums of vibration signals in order to realize fault classification. In this paper, a novel feature in the form of images is presented, namely the spectrum images of vibration signals. The spectrum images are simply obtained by doing fast Fourier transformation. Such images are processed with two-dimensional principal component analysis (2DPCA) to reduce the dimensions, and then a minimum distance method is applied to classify the faults of bearings. The effectiveness of the proposed method is verified with experimental data.

Condition monitoring and fault diagnosis play an important role in the health management of mechanical equipment. However, the robust performance of data-driven-based methods with unknown fault inputs remains to be further improved. In this paper, a novel approach of condition monitoring and fault diagnosis is proposed for rolling element bearings based on an improved ensemble empirical mode decomposition (IEEMD), which is able to solve the non-intrinsic mode function problem of EEMD. In this method, IEEMD is applied to process the primordial vibration signals collected from rolling element bearings at first. Then the correlation analysis and data fusion technology are introduced to extract statistical features from these decomposition results of IEEMD. Finally, a complete self-zero space model is constructed for the condition monitoring and fault diagnosis of rolling element bearings. Experiments are implemented on a mechanical fault simulator to demonstrate the reliability and effectiveness of the proposed method. The experimental results show that the proposed method can not only diagnose known faults but also monitor unknown faults with strong robust performance.