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 the reliable operation of high-value equipment/machines. With modern complex complicated equipment and time pressure from production, a data-driven decision-making approach with timely support from relevant data is crucial for generating optimal plans and cost-effective maintenance.

However, data is normally diverse and scattered around everywhere in the shopfloor, and needs to be collated with their 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 participants with knowledge, techniques and skills in data collection, analysis for predictive maintenance and optimal maintenance planning. Data from machine sensors, operation management systems and maintenance activities are analyzed during the training sessions. The latest technology, for example, machine learning-based predictive engines and maintenance planning systems, will be introduced to participants for applications on industrial cases and gain hands-on experience.

At the end of the course, participants can:

  • Understand the basic concepts and issues in data analysis
  • Identify imbalanced data classes
  • Perform data processing effectively
  • Understand the concept of data-driven predictive maintenance
  • Apply data analysis techniques for predictive maintenance modelling
  • Leverage the predictive model for optimal maintenance planning

Image_Data driven Predictive Maintenance and Optimal Plan

Who should attend

This course is recommended for operations and maintenance managers and engineers, equipment designers, process managers and engineers, software analysts, machine, line or cell system integrators, project managers and other professionals in all industries where there is a need for machine health condition analysis including precision engineering, electronics, aerospace, marine engineering, renewable energy, MedTech, and remanufacturing.

Course Outline

Course Outline

The programme covers the following key topics:

Introduction to Data Analysis for Predictive Maintenance and Optimal Plan

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

Data Collection and Processing Techniques

  • Introduction to sensors, DAQ and MQTT protocol
  • Signal processing and feature extraction in the time domain
  • Frequency analysis and feature extraction in the frequency domain
  • Hands-on practice with Python: data pre-processing and feature engineering with data collected from a machine

Machine Learning (ML) and Modelling Techniques

  • Data modelling techniques with machine learning
  • Case study on an industrial case
  • Issues in using imbalanced data
  • Oversampling techniques to handle imbalanced data

PdM-enabled MMS for Optimal Plan

  • Introduction to PdM-enabled Maintenance Management System (MMS)
  • Use of an integrated MMS dataset for predictive maintenance
  • Architecture and system design of PdM-enabled MMS for Optimal Maintenance Planning
  • Hands-on practice: using integrated MMS for predictive maintenance and planning

Upon Completion of This Course


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

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

Note: Trainees will have to bear the full courses fee upon failure to meet either one of the criteria.

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 aged 39 years and below, Singapore Permanent Residents and LTVP+ Holders
Employer-sponsored and self-sponsored Singapore Citizens aged 40 years and above
(MCES) ²
SME-sponsored local employees (i.e Singapore Citizens, Singapore Permanent Residents and LTVP+ Holders) (ETSS) ¹
All fees are inclusive of GST 9%.

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.

    • ¹ Under the Enhanced Training Support for Small & Medium Enterprises (SMEs) scheme (ETSS), subject to eligibility criteria shown below.

      SMEs that meet all of the following eligibility criteria:

      • Registered or incorporated in Singapore
      • Employment size of not more than 200 or with annual sales turnover of not more than $100 million

      SME-sponsored Trainees:

      • Must be Singapore Citizens or Singapore Permanent Residents.
      • Courses have to be fully paid for by the employer.
      • Trainee is not a full-time national serviceman. 

      Further Info: This scheme is intended for all organisations, including non-business entities not registered with ACRA e.g. VWOs, societies, etc. Only ministries, statutory boards, and other government agencies are NOT eligible under Enhanced Training Support for SMEs Scheme. Sole proprietorships which meet all of the above criteria are also eligible.

    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

    How course fees are calculated

    International Participants Singapore Citizens aged 39 years and below, Singapore Permanent Residents and LTVP+ HoldersEmployer-sponsored and self-sponsored Singapore Citizens aged 40 years and above SME-sponsored local employees (i.e Singapore Citizens, Singapore Permanent Residents and LTVP+ Holders)
    Not applicable for SkillsFuture Funding SkillsFuture Funding (Baseline)  SkillsFuture Mid-career Enhanced Subsidy (MCES) ²SkillsFuture Enhanced Training Support for SMEs (ETSS) ¹
    Full Course Fee $4,000$4,000$4,000 $4,000 
    SkillsFuture FundingNot Applicable  ($2,800)($3,600)($3,600)
    Nett Course Fee$4,000$1,200$400$400
    GST 9%
    Total Nett Course Fee Payable to Training Provider$4,360$1,308$508$508
    * Based on 30% of Full Course Fee

    Contact Us

    • For technical enquiries, please contact:

    Dr Ian CHAN,

    • For general enquiries, please contact:

    Ms Charlotte LIM,


    • Please register for this course through our Course Registration Form for Public Classes.
    • For the first question, please select Qn1: Modular Programmes
    • Applicants will be placed on our waiting list if the course does not have an upcoming scheduled intake.
    • Once the next intake is confirmed to commence, SIMTech will contact the applicants to share the class information.


    Skills Course Reference Number Training Period
    (Click on dates to view schedule)
    Registration Status
    • Data-driven Predictive Maintenance and Optimal Plan (40 hours)
    EVE4 Jan 2024 - 5 Mar 2024
    The Jan 2024 intake is postponed. We will publish a new schedule soon.

    Note: SIMTech and ARTC reserve the right to change the class/schedule/course fee or any details about the course without prior notice to the participants.


    • From 1 Oct 2023, attendance-taking for SkillsFuture Singapore (SSG)'s funded courses must be done digitally via the Singpass App. More information may be viewed here.
    FD: Full day
    AM: Morning
    PM: Afternoon
    EVE: Evening