关键词：
energy harvesting systems;machine condition monitoring;maintenance-free;wireless sensor networks
摘要：
Condition monitoring can reduce machine breakdown losses, increase productivity and operation safety, and therefore deliver significant benefits to many industries. The emergence of wireless sensor networks (WSNs) with smart processing ability play an ever-growing role in online condition monitoring of machines. WSNs are cost-effective networking systems for machine condition monitoring. It avoids cable usage and eases system deployment in industry, which leads to significant savings. Powering the nodes is one of the major challenges for a true WSN system, especially when positioned at inaccessible or dangerous locations and in harsh environments. Promising energy harvesting technologies have attracted the attention of engineers because they convert microwatt or milliwatt level power from the environment to implement maintenance-free machine condition monitoring systems with WSNs. The motivation of this review is to investigate the energy sources, stimulate the application of energy harvesting based WSNs, and evaluate the improvement of energy harvesting systems for mechanical condition monitoring. This paper overviews the principles of a number of energy harvesting technologies applicable to industrial machines by investigating the power consumption of WSNs and the potential energy sources in mechanical systems. Many models or prototypes with different features are reviewed, especially in the mechanical field. Energy harvesting technologies are evaluated for further development according to the comparison of their advantages and disadvantages. Finally, a discussion of the challenges and potential future research of energy harvesting systems powering WSNs for machine condition monitoring is made.
会议名称：
24th IEEE International Conference on Automation and Computing (ICAC) - Improving Productivity through Automation and Computing Newcastle
会议时间：
SEP 06-07, 2018
会议地点：
Newcastle Univ, Newcastle upon Tyne, ENGLAND
会议主办单位：
Newcastle Univ
会议论文集名称：
2018 24th International Conference on Automation and Computing (ICAC)
关键词：
Energy harvesting;machine condition monitoring;wireless sensor networks;maintenance free
摘要：
Condition monitoring (CM) deliveries significant benefits to industries by reducing breakdown losses of machines and enhancing their safe and high-performance operations. Monitoring the machine conditions in real time using an appropriate wireless sensor network (WSN) has the advantages of the avoidance of cable usages, ease of system deployment and hence cost-effectiveness of CM implementation. One of the major challenges for WSN is the battery replacement. Generally, the batteries of sensor nodes are difficult to recharge or replace due to the inevitable layout at inaccessible or risky positions. Recently, energy harvesting (EH) applied to WSNs has increasingly caught the attention of researchers due to the ideal permanent non-maintenance requirements of the autonomous WSN nodes. This paper overviews the principles of several promising EH technologies (including photovoltaic, thermoelectric, pyroelectric, piezoelectric, electromagnetic, triboelectric EH technologies) used in various fields. In addition, the corresponding EH prototypes and fabricated products developed by various researchers are reviewed. After the discussion of the advantages and limitations of different technologies, the EH technologies are evaluated for further development of the energy harvesters to achieve a maintenance-free system for reliable monitoring machines. Finally, a discussion on challenges, applications and future developments of EH applied for machine CM is held.
摘要：
Cumulative fatigue damage detection for used parts plays a key role in the process of remanufacturing engineering and is related to the service safety of the remanufactured parts. In light of the nonlinear properties of used parts caused by cumulative fatigue damage, the based nonlinear output frequency response functions detection approach offers a breakthrough to solve this key problem. First, a modified PSO-adaptive lasso algorithm is introduced to improve the accuracy of the NARMAX model under impulse hammer excitation, and then, an effective new algorithm is derived to estimate the nonlinear output frequency response functions under rectangular pulse excitation, and a based nonlinear output frequency response functions index is introduced to detect the cumulative fatigue damage in used parts. Then, a novel damage detection approach that integrates the NARMAX model and the rectangular pulse is proposed for nonlinear output frequency response functions identification and cumulative fatigue damage detection of used parts. Finally, experimental studies of fatigued plate specimens and used connecting rod parts are conducted to verify the validity of the novel approach. The obtained results reveal that the new approach can detect cumulative fatigue damages of used parts effectively and efficiently and that the various values of the based nonlinear output frequency response functions index can be used to detect the different fatigue damages or working time. Since the proposed new approach can extract nonlinear properties of systems by only a single excitation of the inspected system, it shows great promise for use in remanufacturing engineering applications.
摘要：
In the production of polypropylene, powder conglutination in pipelines easily leads to blockage, which can seriously affect the operation safety of the pipeline, so it is very important to detect and quantitatively evaluate the powder conglutination. This paper proposed an acoustic-ultrasonic (AU) quantitative evaluation method for powder conglutination detection in polypropylene production pipelines. A simulation model was developed to investigate the wave propagation characteristics of conglutinated layers with different areas and thicknesses using stress wave factors (SWF). Experiments were then conducted to develop a quantitative evaluation method for polypropylene powder conglutination. The results show that the relative attenuation coefficients of peak amplitude, peak-to-peak amplitude and the energy and the peak of the power spectrum all follow an approximate linear relation with the areas and thicknesses of the conglutinated layers. For either area or thickness evaluation, the energy or the peak of the power spectrum of AU signals has higher sensitivity than peak amplitude or peak-to-peak amplitude. Moreover, compared with conglutinated area evaluation, all the SWF models for thickness evaluation were more reliable, where the errors were all less than 7%. As a result, the AU technique is an effective means to detect powder conglutination in polypropylene production pipelines, and high sensitive and accurate quantitative evaluation is feasible with some of the stress wave factors of AU signals. (C) 2017 Elsevier B.V. All rights reserved.
作者机构：
[毛汉领; 李欣欣; 黄振峰; 刘永坚] College of Mechanical Engineering, Guangxi University, Nanning, 530004, China;[王向红] Hunan Province Key Laboratory of Safety Design and Reliability Technology for Engineering Vehicle, Changsha University of Science and Technology, Changsha, 410004, China;[毛汉颖] College of Automobile and Transportation, Guangxi University of Science and Technology, Liuzhou, 542506, China
通讯机构：
College of Automobile and Transportation, Guangxi University of Science and Technology, Liuzhou, China
作者机构：
[王向红; 王泽湘; 胡宏伟] Hunan Province Key Laboratory of Safety Design and Reliability Technology for Engineering Vehicle, Changsha University of Science &, Technology, Changsha, 410114, China;[徐娜] AVIC Beijing Institute of Aeronautical Materials, Beijing, 100191, China;[胡宏伟] Hunan Province Engineering Laboratory of Bridge Structure, Changsha, 410114, China
通讯机构：
Hunan Province Key Laboratory of Safety Design and Reliability Technology for Engineering Vehicle, Changsha University of Science & Technology, Changsha, China
作者机构：
[胡宏伟; 王向红; 尹东] Hunan Province Key Laboratory of Safety Design and Reliability Technology for Engineering Vehicle, Changsha University of Science &, Technology, Changsha, 410004, China;[毛汉领] School of Mechanical Engineering Guangxi University, Nanning, 530003, China;[王向红] Engineering Research Center of Catastrophic Prophylaxis and Treatment of Road &, Traffic Safety of Ministry of Education, Changsha University of Science &, Technology, Changsha, 410004, China
作者机构：
[向建军; 王向红; 谢炜; 胡宏伟] Hunan Province Key Laboratory of Safety Design and Reliability Technology for Engineering Vehicle, Changsha University of Science and Technology, Changsha, China;[毛汉领] School of Mechanical Engineering, Guangxi University, Nanning, China;[王向红] Key Laboratory of Lightweight and Reliability Technology for Engineering Vehicle, College of Hunan Province, Changsha University of Science and Technology, Changsha, China;[王向红] Key Laboratory of Efficient &, Clean Energy Utilization, the Education Department of Hunan Province, Changsha University of Science &, Technology, Changsha, China
通讯机构：
Hunan Province Key Laboratory of Safety Design and Reliability Technology for Engineering Vehicle, Changsha University of Science and Technology, Changsha, China
通讯机构：
Hunan Province Key Laboratory of Safety Design and Reliability Technology for Engineering Vehicle, Changsha University of Science and Technology, Changsha, China
