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RISS 인기검색어
- 검색키워드
- 저자 : Robert X. Gao (검색결과 6 건)
- 무료
- 기관 내 무료
- 유료
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Sensor placement strategy for high quality sensing in machine health monitoring
Gao, Robert X.,Wang, Changting,Sheng, Shuangwen Techno-Press 2005 Smart Structures and Systems, An International Jou Vol.1 No.2
This paper presents a systematic investigation of the effect of sensor location on the data quality and subsequently, on the effectiveness of machine health monitoring. Based on an analysis of the signal propagation process from the defect location to the sensor, numerical simulations using finite element modeling were conducted on a bearing test bed to determine the signal strength at several representative sensor locations. The results showed that placing sensors closely to the machine component being monitored is critical to achieving high signal-to-noise ratio, thus improving the data quality. Using millimeter-sized piezoceramic plates, the obtained results were evaluated experimentally. A comparison with a set of commercial vibration sensors verified the developed structural dynamics-based sensor placement strategy. It further demonstrated that the proposed shock wave-based sensing technique provided an effective alternative to vibration measurement, while requiring less space for sensor installation.
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A bond graph approach to energy efficiency analysis of a self-powered wireless pressure sensor
Robert X. Gao,Dengfeng Yang,Yong Cui,David O. Kazmer 국제구조공학회 2007 Smart Structures and Systems, An International Jou Vol.3 No.1
The energy efficiency of a self-powered wireless sensing system for pressure monitoring in injection molding is analyzed using Bond graph models. The sensing system, located within the mold cavity, consists of an energy converter, an energy modulator, and a ultrasonic signal transmitter. Pressure variation in the mold cavity is extracted by the energy converter and transmitted through the mold steel to a signal receiver located outside of the mold, in the form of ultrasound pulse trains. Through Bond graph models, the energy efficiency of the sensing system is characterized as a function of the configuration of a piezoceramic stack within the energy converter, the pulsing cycle of the energy modulator, and the thicknesses of the various layers that make up the ultrasonic signal transmitter. The obtained energy models are subsequently utilized to identify the minimum level of signal intensity required to ensure successful detection of the ultrasound pulse trains by the signal receiver. The Bond graph models established have shown to be useful in optimizing the design of the various constituent components within the sensing system to achieve high energy conversion efficiency under a compact size, which are critical to successful embedment within the mold structure.
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A bond graph approach to energy efficiency analysis of a self-powered wireless pressure sensor
Cui, Yong,Gao, Robert X.,Yang, Dengfeng,Kazmer, David O. Techno-Press 2007 Smart Structures and Systems, An International Jou Vol.3 No.1
The energy efficiency of a self-powered wireless sensing system for pressure monitoring in injection molding is analyzed using Bond graph models. The sensing system, located within the mold cavity, consists of an energy converter, an energy modulator, and a ultrasonic signal transmitter. Pressure variation in the mold cavity is extracted by the energy converter and transmitted through the mold steel to a signal receiver located outside of the mold, in the form of ultrasound pulse trains. Through Bond graph models, the energy efficiency of the sensing system is characterized as a function of the configuration of a piezoceramic stack within the energy converter, the pulsing cycle of the energy modulator, and the thicknesses of the various layers that make up the ultrasonic signal transmitter. The obtained energy models are subsequently utilized to identify the minimum level of signal intensity required to ensure successful detection of the ultrasound pulse trains by the signal receiver. The Bond graph models established have shown to be useful in optimizing the design of the various constituent components within the sensing system to achieve high energy conversion efficiency under a compact size, which are critical to successful embedment within the mold structure.
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Sensor placement strategy for high quality sensing in machine health monitoring
Changting Wang,Shuangwen Sheng,Robert X. Gao 국제구조공학회 2005 Smart Structures and Systems, An International Jou Vol.1 No.2
This paper presents a systematic investigation of the effect of sensor location on the data quality and subsequently, on the effectiveness of machine health monitoring. Based on an analysis of the signal propagation process from the defect location to the sensor, numerical simulations using finite element modeling were conducted on a bearing test bed to determine the signal strength at several representative sensor locations. The results showed that placing sensors closely to the machine component being monitored is critical to achieving high signal-to-noise ratio, thus improving the data quality. Using millimeter-sized piezoceramic plates, the obtained results were evaluated experimentally. A comparison with a set of commercial vibration sensors verified the developed structural dynamics-based sensor placement strategy. It further demonstrated that the proposed shock wave-based sensing technique provided an effective alternative to vibration measurement, while requiring less space for sensor installation.
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Recurrence plot entropy for machine defect severity assessment
Ruqiang Yan,Yuning Qian,Zhoudi Huang,Robert X. Gao 국제구조공학회 2013 Smart Structures and Systems, An International Jou Vol.11 No.3
This paper presents a nonlinear time series analysis technique for evaluating machine defect severity, based on the Recurrence Plot (RP) entropy. The RP entropy is calculated from the probability distribution of the diagonal line length in the recurrence plot, which graphically depicts a system’s dynamics and provides a global picture of the autocorrelation in a time series over all available time-scales. Results of experimental studies conducted on a spindle-bearing test bed have demonstrated that, as the working condition of the bearing deteriorates due to the initiation and/or progression of structural damages, the frequency information contained in the vibration signal becomes increasingly complex, leading to the increase of the RP entropy. As a result, RP entropy can serve as an effective indicator for defect severity assessment of rolling bearings.
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Recurrence plot entropy for machine defect severity assessment
Yan, Ruqiang,Qian, Yuning,Huang, Zhoudi,Gao, Robert X. Techno-Press 2013 Smart Structures and Systems, An International Jou Vol.11 No.3
This paper presents a nonlinear time series analysis technique for evaluating machine defect severity, based on the Recurrence Plot (RP) entropy. The RP entropy is calculated from the probability distribution of the diagonal line length in the recurrence plot, which graphically depicts a system's dynamics and provides a global picture of the autocorrelation in a time series over all available time-scales. Results of experimental studies conducted on a spindle-bearing test bed have demonstrated that, as the working condition of the bearing deteriorates due to the initiation and/or progression of structural damages, the frequency information contained in the vibration signal becomes increasingly complex, leading to the increase of the RP entropy. As a result, RP entropy can serve as an effective indicator for defect severity assessment of rolling bearings.
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