A deep learning based non-intrusive health monitoring and fault prognosis system for lifts

I&T Solution A deep learning based non-intrusive health monitoring and fault prognosis system for lifts
(REF : S-0220)
Solution Feature
  • Field measurement of lift system will be conducted to collect current and other important signals from lift system considering various normal and abnormal scenarios.
  • The lift electric signals can be simulated using our lab equipment. Typical lift faults will be considered. Then, an efficient data mining algorithm will be developed to extract the mostly important features that contribute to the specific faults.
  • Considering high volume and high sampling frequency of the collected data, we propose to employ deep learning framework to learn the complex relationship between the extracted features and different fault events. The model will be built and implemented efficiently on our powerful computing facilities.
  • The developed intelligent algorithms will be integrated to construct a software package with a friendly graphical user interface. The functions should include real-time lift condition display, abnormal events diagnosis and reporting, maintenance needs prediction.
  • Besides, an effective demonstration showcase to illustrate the proof-of-concept of the entire NICM system based on e.g. multimedia and animation technologies will be developed too.
Trial Application and Expected Outcome
  • Two Progress Reports at one third and two third of project period state the a) Project Summary and Plan; b) Milestone Review; c) Deliverables and Quality; d) Extra resources& supports required.
  • A final report at project end documenting the details of this trial including how the AI models are built-up, testing and validation process, evaluation of the effectiveness of the models and recommendations on further improvement or study.
  • A software package system that provides an effective demonstration showcase to illustrate the proof-of-concept of the entire NICM system, which can be further applied to the lifts of different brands.
  • A future plan of further research works on NICM system.
Info on I&T Solution Provider
Solution Provider:The Hong Kong Polytechnic University
Address:Room CF611, Department of Electrical Engineering, Hong Kong Polytechnic University, Hung Hom, Kowloon, HONG KONG
Contact Person:Prof Zhao XU
Position:Professor
Tel: +852 27666160
Email: eezhaoxu@polyu.edu.hk

If you have interest in trial application of the I&T solutions, please click HERE.