|Kiang, Chung Chau Nefield
|Condition monitoring of escalator system using fibre bragg grating sensors and signal processing techniques : a case study of public service escalators
Elevators -- Testing.
Elevators -- Maintenance and repair.
Escalators -- Testing.
Escalators -- Maintenance and repair.
Hong Kong Polytechnic University -- Dissertations
|Faculty of Engineering
|xx, 184 p. : ill. (some col.) ; 30 cm.
|Vibration signals are key index to monitor the usage and stability of rail-wheel interaction systems because they carry the dynamic information of the machines. These signals contain useful information for asset condition monitoring and control. The research is to explore the technical feasibility of developing a condition monitoring approach to extract signatures from in-situ measurement signals for energy-efficient operation and safety enhancement. The research comprises mainly three stages with a view to improve the system efficiency and safety of an escalator as mentioned above. The first is to brief the existing energy saving technologies applying to escalator industry including two conventional methods of auto-off and auto two-speed operation. However, without due consideration of passenger flow profile, these two control methods cannot drive the escalators in an intelligent way as the speed of escalator will operate at rated speed even if there is only one passenger riding on it. The latest Variable Voltage Constant Frequency control method for energy saving technique being under trial is also presented. Second, the integrity of the signals can be obtained by embedding FBG sensors on escalator. The architecture of condition monitoring framework contains various interconnected modules including sensing module, signal acquisition module, signal pre-processing module, and signatures extraction module has been designed and implemented to extract specified escalator signals for developing escalator-on-demand operation, broken step prediction and abnormal vibration detection with the aid of real-time software tool i.e. LabView. Signal processing algorithms have been developed to identify the numbers of passengers and the abnormal vibration signal generated by a defect wheel of the escalator. By using the time-domain based hysteresis detection method proposed in the paper, accurate passenger counting of an in-service escalator is successfully demonstrated. Frequency-domain based signal processing techniques including autocorrelation, WPT, FFT and variance have been used in a combination to decompose the strain signal into several distinct frequency bands. Hidden frequency components due to step wheel defect is clearly identified. Moreover, the variance of each frequency band provides a more generic index on classification of escalator's condition, such as no-load and abnormal condition of the escalator. This index can be simply used as an indication of the actual operating condition of escalator. The final stage of research work is to analyze the field data and confirm the technical viability of an empirical approach that can be used to enhance the reliability of heavily-traffic type of escalators. The results obtained by the system developed in this study could be further validated by industrial applications.
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