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Data driven Predictive Maintenance and Optimal Plan* (40 hours)

Data driven Predictive Maintenance and Optimal Plan
*This is a non-WSQ module.

Effective management of maintenance in industries is a major concern to reduce the cost and ensure reliable operation of high-value equipment/machines. With modern complex complicated equipment and time pressure from production, an informative decision with timely support from relevant data is critical for an optimal plan and cost-effective maintenance.

However, data are normally diverse and scattered around anywhere in the office or the shopfloor, and need to be collated with time-stamp first. To get insights from data for improving efficiency and quality of maintenance, profound data analysis knowledge, techniques and skills are needed. 

About the Programme

This course aims to provide the participants with the knowledge, techniques and skills of data analysis for predictive maintenance and optimal maintenance planning. Data from operation management systems, machine sensors, maintenance activities will be analyzed. Latest technology such as machine learning-based predictive engine, maintenance planning system is introduced and allow participants to discuss industrial cases and gain hands-on experience.

At the end of the course, participants can:

  • Understand basic concepts and issues in data analysis Identify imbalanced data classes
  • Perform data processing effectively
  • Understand the concept of data driven predictive maintenance
  • Understand the concept of optimal maintenance planning
  • Apply data analysis techniques for maintenance planning for optimisation
Course Outline
Upon Completion of This Course

Pre-Requisites and Nett Course Fee

Contact Us

Please register for this course under 1. Modular Programmes > 2. Data driven Predictive Maintenance and Optimal Plan in the online registration form.


Skills Course Reference Number Training Period Registration Status
Data driven Predictive Maintenance and Optimal Plan (40 hours)TGS-2020504648
PM 14 Oct 2020 - 27 Nov 2020
FD: Full Day AM: MorningPM: AfternoonEVE: Evening