MTR, the major Hong Kong public transport provider, has been operating for 40 years with more than 1000 escalators in the railway network. These escalators are installed in various railway stations with different ages, vertical rises and workload. An escalator’s refurbishment is usually linked with its design life as recommended by the manufacturer. However, the actual useful life of an escalator should be determined by its operating condition which is affected by runtime, workload, maintenance quality, vibration etc., rather than age only. Escalators in the same station are usually of the same age. Under the “time-based” strategy, escalators need to be refurbished more or less at the same time. This will inevitably cause inconvenience to the passengers and hence affect the level of service. If the refurbishment work is postponed without fully understanding the health condition of the escalators, the escalators may not operate well.
The objective of this project is to develop a comprehensive health condition model for escalators to support the refurbishment decision. The analytic model consists of four parts: 1) online data gathering and processing; 2) condition monitoring; 3) a health index model; and 4) a remaining useful life model. The results can be used for 1) predicting the remaining useful life of the escalators, in order to support asset replacement planning and 2) monitoring the real-time condition of escalators; including signaling when vibration exceeds the threshold and signal diagnosis, giving an indication of possible root cause (components) of the signal. To develop the model the following data sources are utilized and combined: real-time vibration signals from eight sensors, continuous energy usage, as well as fault and maintenance history.
In this talk, we will provide an overview of this project and discuss how the statistical engineering framework was used for successful execution.
Dr. Inez Maria Zwetsloot is an assistant professor in the Department of Advanced Design and Systems Engineering, City University of Hong Kong. She is also an affiliated faculty member of the Data Science School at CityU. Her research interests include statistical process monitoring, network analysis, outlier detection, data science and statistical engineering. She received the Feigenbaum Medal (2022) from ASQ and the young statistician award from ENBIS (2021). She is a member of the board of ISEA, the International Statistical Engineering Association.