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This course is currently not available for registration. Please contact the Knowledge Transfer Office for enquiries.
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

Course Outline

The programme covers the following key topics:

Introduction of Data Analysis for Predictive Maintenance and Optimal Plan

  • Introduction predictive maintenance in the age of Industrial 4.0
  • Introduction of fundamental concepts of data analysis
  • Issues in the data and methods of preparing data
  • Case study: Data analysis for predictive maintenance

Data Processing and Modelling Techniques

  • Data processing and modelling techniques for machine learning
  • Issues in using imbalance data
  • Oversampling techniques to handle imbalanced data
  • Case study: Equipment health analysis using machine learning

PdM-enabled Maintenance Management System

  • Introduction of PdM enabled maintenance management system
  • Make use of integrated MMS dataset for predictive maintenance
  • Architecture and system design for integrating MMS with ML engine
  • Case Study and hands-on: MMS to collect data for predictive maintenance

Downtime Forecast and Optimal Maintenance Plan

  • Models for Manufacturing Systems and Maintenance
  • Optimal Forecasting Methods and Applications
  • Optimal Maintenance Plan Forecasting Tool and Hands-on
  • Case Study: Production Line Downtime Forecast

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Data driven Predictive Maintenance and Optimal Plan

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Upon Completion of This Course


Participants will be awarded with a Certificate of Attendance (COA) by SIMTech if they meet the following criteria:

  • Achieve at least 75% course attendance;
  • Take all assessments; and
  • Pass the course.

Pre-Requisites, Full and Nett Course Fees


  • Applicants should possess a degree in any discipline or a diploma with a minimum of 3 years of related working experience.
  • Applicants who do not have the required academic qualifications are still welcome to apply, but shortlisted candidates may be required to attend an interview for special approval.
  • Proficiency in written and spoken English.

Full Course Fee

The full course fee for this module is $4,000 before funding and prevailing GST.

Nett Course Fee

Singapore Citizens
(39 yrs old or younger), SPRs or LTVP+ Holders
Singapore Citizens
(40 yrs or older)²
Enhanced Training Support
for SMEs¹
$4,280 $1,284 $484
All fees are inclusive of prevailing GST.

Please note that fees and funding amount are subject to change.

  • Long Term Visit Pass Plus (LTVP+) Holders
    The LTVP+ scheme applies to lawful foreign spouses of Singapore Citizens with (i) at least one Singapore Citizen child or are expecting one from the marriage, or at least three years of marriage, and (ii) where the Singapore Citizen sponsor is able to support the family.

    All LTVP+ holders can be identified with their green visit pass cards, with the word ‘PLUS’ printed on the back of the card.

  • Singaporeans aged 25 years old and above are eligible for SkillsFuture Credit which can be used to offset course fees (for self-sponsored registrations only).

    For more information on the SkillsFuture funding schemes you are eligible for, please visit www.ssg.gov.sg

    How course fees are calculated

    fees breakdown

    download brochure

    Data driven Predictive Maintenance and Optimal Plan

    download brochure 

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