Gongbo Zhou

Harvesting Ambient Environmental Energy for Wireless Sensor Networks: A Survey

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.

Robust condition monitoring and fault diagnosis of rolling element bearings using improved EEMD and statistical features

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.

Design of Online Monitoring and Fault Diagnosis System for Belt Conveyors Based on Wavelet Packet Decomposition and Support Vector Machine

Belt conveyors are the equipment widely used in coal mines and other manufacturing factories, whose main components are a number of idlers. The faults of belt conveyors can directly influence the daily production. In this paper, a fault diagnosis method combining wavelet packet decomposition (WPD) and support vector machine (SVM) is proposed for monitoring belt conveyors with the focus on the detection of idler faults. Since the number of the idlers could be large, one acceleration sensor is applied to gather the vibration signals of several idlers in order to reduce the number of sensors. The vibration signals are decomposed with WPD, and the energy of each frequency band is extracted as the feature. Then, the features are employed to train an SVM to realize the detection of idler faults. The proposed fault diagnosis method is firstly tested on a testbed, and then an online monitoring and fault diagnosis system is designed for belt conveyors. An experiment is also carried out on a belt conveyor in service, and it is verified that the proposed system can locate the position of the faulty idlers with a limited number of sensors, which is important for operating belt conveyors in practices.

Bearing fault diagnosis based on spectrum images of vibration signals

Bearing fault diagnosis has been a challenge in the monitoring activities of rotating machinery, and its receiving more and more attention. The conventional fault diagnosis methods usually extract features from the waveforms or spectrums of vibration signals in order to correctly classify faults. In this paper, a novel feature in the form of images is presented, namely analysis of 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.

Fault diagnosis of bearings based on a sensitive feature decoupling technique

Bearings are commonly used in machine industry, and their faults may result in unexpected vibration and even cause breakdown of a whole rotating machine. This paper proposes a novel fault diagnosis approach for bearings by using a sensitive feature decoupling technique. This approach does not require a training procedure as in machine learning methods and can classify the occurred faults by a simple algebraic computation. Firstly, the features of vibration signals which show the most significant difference under different bearing health conditions are selected and defined as sensitive features. Then those sensitive features under different health conditions are used to construct a feature matrix, and its left null space is computed to obtain the so-called feature decoupling vectors. The bearing faults are finally classified with the help of the decoupling vectors according to a simple decision logic. Since the obtained decoupling vectors may not be unique, we also propose an algorithm to select the optimal ones in order to improve the performance of fault diagnosis. Experiments are carried out to test the proposed approach and the results show that the approach is feasible and effective for the fault diagnosis of bearings.

Node deployment of band-type wireless sensor network for underground coalmine tunnel

Proper node deployment is the first step to build a Wireless Sensor Network (WSN) system. Therefore, a detailed study on mathematical 3D node deployment is carried out in this paper with the purpose of increasing the coverage efficiency of WSN in underground coalmine tunnel. Firstly, a 3D band-type node deployment model is proposed and in which part, several important characteristics of node deployment are discussed in detail, such as radio features, sensing efficiency, redundancy principles and coverage features. Secondly, a targeted node deployment algorithm is brought up and the core method of interval computing is put forward, thus the node interval can be computed accordingly. Thirdly, we use simulated annealing method to optimize the deployment algorithm proposed. The results show that the characteristics of node deployment in coalmine tunnel affect the network coverage dramatically. Moreover, comparing with the current deployment strategies, the optimized deployment provided by us can promote the coverage efficiency markedly.

Fault Detection of Markov Jumping Linear Systems

In this paper, the fault detection (FD) problems of discrete-time Markov jumping linear systems (MJLSs) are studied. We first focus on the stationary MJLS. The proposed FD system consists of two steps: residual generation and residual evaluation. A new reference model strategy is applied to construct a residual generator, such that it is robust against disturbances and sensitive to system faults. The generated residual signals are then evaluated according to their stochastic properties, and a threshold is computed for detecting the occurrences of faults. The upper bound of the corresponding false alarm rate (FAR) is also given. For the nonstationary MJLS, similar results are also obtained. All the solutions are presented in the form of linear matrix inequalities (LMIs). Finally, a numerical example is used to illustrate the results.

Performance Analysis of Wind-Induced Piezoelectric Vibration Bimorph Cantilever for Rotating Machinery

Harvesting the energy contained in the running environment of rotating machinery would be a good way to supplement energy to the wireless sensor. In this paper, we take piezoelectric bimorph cantilever beam with parallel connection mode as energy collector and analyze the factors which can influence the generation performance. First, a modal response theory model is built. Second, the static analysis, modal analysis, and piezoelectric harmonic response analysis of the wind-induced piezoelectric bimorph cantilever beam are given in detail. Finally, an experiment is also conducted. The results show that wind-induced piezoelectric bimorph cantilever beam has low resonant frequency and stable output under the first modal mode and can achieve the maximum output voltage under the resonant condition. The output voltage increases with the increase of the length and width of wind-induced piezoelectric bimorph cantilever beam, but the latter increasing amplitude is relatively smaller. In addition, the output voltage decreases with the increase of the thickness and the ratio of metal substrate to piezoelectric patches thickness. The experiment showed that the voltage amplitude generated by the piezoelectric bimorph cantilever beam can reach the value simulated in ANSYS, which is suitable for actual working conditions.

A Zoning Strategy for Uniform Deployed Chain-Type Wireless Sensor Network in Underground Coal Mine Tunnel

In order to be more suitable for underground coal mine tunnel, network models for chain-type wireless sensor network are built, and a 3D uniformed network deployment method is proposed firstly in this paper. Then, a zoning method composed of energy balance strategy and cluster head election strategy is discussed, which is quite different from the commonly used hierarchy and cluster strategy. Finally, the performances of the zoning strategy are evaluated. The results show that, there is a trade-off among number of zones, network latency and network life. It is also shows that, appropriately zoning will prolong network life and achieve the required network performances.

A new fault feature for rolling bearing fault diagnosis under varying speed conditions

Most fault detection methods based on the assumption of working in stationary or approximate stationary conditions are limited under varying operation conditions, for that the frequency aliasing phenomenon is inevitable in the spectrum. Therefore, in order to handle the problem of fault diagnosis under non-stationary conditions, researchers have proposed numerous methods and some achievements have been obtained. In this article, a new feature extraction method is proposed for fault diagnosis of rolling bearings under varying speed conditions. Based on the assumption that the energy will increase when balls cross over fault position, frequency values are divided by instantaneous speed and arranged in the descending order of corresponding amplitude to form a new fault feature array, that is, the ratio of frequency to instantaneous speed reconfiguration arrays. Thereafter, the Euclidean distance classifier is utilized for recognition. The efficacy of the proposed method is demonstrated by simulated and experimental data. Categorized results show that the new approach is capable of handling the bearing fault classification under varying speed conditions.