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|Title:||Fiber optics sensor technologies for innovative condition monitoring applications||Authors:||Lai, Chun Cheung||Advisors:||Ho, S. L. (EE)
Tam, H. Y. (EE)
|Keywords:||Optical fiber detectors
Railroads -- Safety measures
|Issue Date:||2019||Publisher:||The Hong Kong Polytechnic University||Abstract:||This study aimed to explore a new area of development in optical fiber sensing - using the Fiber Bragg Grating (FBG) as a sensing element with real-life applications. The proposed new development is in the field of information technology, and fully utilize the advantages of FBG-based sensors as the foundation of the new area, benefiting many modern industries in this information age. To demonstrate the concept, a case study focused on railway application is presented. The case study included setting up a Smart Railway Health-Condition Monitoring (SRHM) system, including an FBG-based sensors network and information technology algorithms, for monitoring the health condition of all trains in the fleet. The FBG has become very popular in recent years because of its advantages of high capacity, small size and long-distance of application. Thus, it has been used in different fields and excellent performances have been recorded. Railway systems involve numerous high-voltage systems that may be sensitive to, or emit, electromagnetic (EM) waves. Compared to electronic type sensors, the FBG is the most suitable candidate for railway application because of its EM interference (EMI) immunity and zero-emission of EM waves. The SRHM system proposed, connects four FBG-based sensors as follows: (1) Track Settlement Level Sensor, (2) Axle Counting Sensor, (3) Weight Balance Sensor, and (4) Vibration Index (VI) Sensor. The VI was selected for investigation to demonstrate the proposed new research direction and extended usage. Furthermore, to distinguish the identity of each train and its corresponding data, radio frequency identification (RFID) tags were installed in all cars of the trains. After a year of experimentation, the data and results illustrated that SRHM performance was satisfactory. The research showed that, the VI values of a train is correlated to the Out-of-Roundness (OOR) of the wheels. The SRHM system recorded that VI dropped to very low values after re-profiling of the wheels. Thereafter, the VI grows gradually and then increases more rapidly over longer timescales. This finding fits the Weibull distribution for lifetime failure analysis. The data from a year of testing was used as a reference for comparison with OOR levels, which is one of the factors measured in determining the maintenance quality of the fleet.
The findings show that VI data did help in improving maintenance efficiency. In the experiment, historical VI values for the whole fleet were fed back to the maintenance planning personnel. Any train with a higher VI was given a higher priority within the maintenance process, which includes wheel re-profiling. After three months, the overall VI values dropped gradually and continued to drop in the next three months. In fact, the maximum VI values dropped from 18.4 to 12.9 during the first three-month period and then to 9.9 by the end of the second three-month trial period. Additionally, a 3A-Warning system was proposed which related to the degree of vibration. The results from the SRHM system also showed that 55.2% of the wheel re-profiling normally carried out was not necessary, when VI values showed that the related OOR values were within the acceptable range. In other words, to skip re-profiling these wheels can save a lot of cost and manpower, as well as extending the lifetime of the wheels. Utilizing the historical data and statistical algorithms, useful information has been extracted and maintenance efficiency increased. The rates of vibration growth predicted by the trend of historical VI values, is used to facilitate a decision-making support system (DSS) for maintenance scheduling. A balanced regime is proposed for wheel re-profiling making use of the Weibull distribution curve, specifically the point at which the curve turns from the low growth region to the high growth region, as the trigger for re-profiling. Hence, the maximum lifetime with minimum resources balance is reached. A Fleet Health Condition Level (FHCL) chart was also derived. Monitoring the health and maintenance quality of the fleet in real-time and in-service was thereby achieved. Although the SRHM system would improve the quality of railway maintenance, there are some limitations and challenges. Since a train is a very complex machine, vibration derives from different sources and this can vary through time. In addition, OOR is only one of the factors used to estimate the health condition of a train. For accurate estimation, further investigation and more sensors may be added. Finally, more benefits have been gained by applying FBG sensors in railway maintenance. Safety, cost, labor time, and the lifetime of the wheel are improved significantly. Thus, this research project has demonstrated that, the power of the FBG is expanded if it is applied in collaboration with different sensors and information technologies.
|Description:||xx, 174 pages : color illustrations
PolyU Library Call No.: [THS] LG51 .H577P EE 2019 Lai
|URI:||http://hdl.handle.net/10397/81463||Rights:||All rights reserved.|
|Appears in Collections:||Thesis|
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