Predictive Maintenance and fault diagnosis of lift key components based on a hybrid deep learning framework

I&T Solution	Predictive Maintenance and fault diagnosis of lift key components based on a hybrid deep learning framework (REF: S-1136)
Trial Project
Solution Feature	This solution adopts a hybrid deep learning framework, comprised by a supervised learning model, a self-supervised/unsupervised learning model and an expert system, to predict the abnormal/fault events. The architecture and of underlying framework and the functionality of each model can be referred in the uploaded graph. In order to improve the learning efficiency and accuracy, transfer learning technology will be applied if multiple cross-brand/model lifts are considered. In order to identify the correlation across components and other potential impact factors, both linear correlation analysis and machine learning based feature significance analysis will be performed. If the dataset is collected with high sampling frequency, the features of both frequency domain and frequency domain need to be studied to give a full portrait of each sample. The AI model will be deployed on the designated server (either cloud or local) to process the online data stream and render the results in realtime. A dedicated web-based dashboard will be also developed to showcase the AI results, including the component status and timely warnings of the detected faults.
Trial Application and Expected Outcome	Literation review, existing relevant technology summarize and preliminary validation of technology feasibility. Preprocess the collected dataset, offline training of the AI candidate models and identify an “optimal” generalized model dedicated for our dataset through continuous performance analysis and comparison. A web-based dashboard will be provided to demonstrate the effectiveness of proposed solution, the functions include but not limited to the abnormal/fault event logging and alert, statistics of KPI and trend analysis. A Interim Study Report (at the middle stage of project) and a Final Study Report (at project end) will be provided, including but not limited to the following aspects: a) Project Summary and Plan; b) Method statement; c) Performance Verification; 4) Conclusion. Source code and OM manual for the developed AI models will be provided at the end of project.
Additional Solution Information	W-0394_Schematic of Our Solution.pdf
Info on I&T Solution Provider	Solution Provider : Ergatian Limited Address : Office Room 18, Smart-Space IT Street, Level 3, IT Street, Cyberport 3, 100 Cyberport Road, Hong Kong Contact Person : Cynthia Yu Position : Project Manager Tel : 6231 7146 Email : chaisongjian@gmail.com

For details of the above I&T solution, please contact the I&T solution provider.