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DATA-DRIVEN PREDICTIVE MAINTENANCE AND OPTIMAL PLAN* (40 HOURS)

*This is a non-WSQ module.
Data driven Predictive Maintenance and Optimal Plan
Use Your SkillsFuture Credit Before It Expires! 

Tap on your $500 SkillsFuture Credit (which expires in December 2025) to offset our course fees! Whether you're upskilling for a career switch or looking to enhance your current expertise, make full use of this credit before it expires and invest in your professional growth today.

Introduction

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

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

Pre-Requisites

  • 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 course is $4,000 before funding and prevailing GST.

Nett Course Fee

International
Participants		òòò½ÍøCitizens aged 39 years and below, òòò½ÍøPermanent Residents and LTVP+ Holders
Employer-sponsored and self-sponsored òòò½ÍøCitizens aged 40 years and above
(MCES) ²		SME-sponsored local employees (i.e òòò½ÍøCitizens, òòò½ÍøPermanent Residents and LTVP+ Holders) (ETSS) ¹
$4,360$1,308$508
$508
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 òòò½ÍøCitizens with (i) at least one òòò½ÍøCitizen child or are expecting one from the marriage, or at least three years of marriage, and (ii) where the òòò½ÍøCitizen 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.

ENHANCED TRAINING SUPPORT FOR SMALL & MEDIUM ENTERPRISE (SMEs) SCHEME (ETSS)

  • The ¹ is for company-sponsored participants who are subject to the 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 òòò½ÍøCitizens or òòò½ÍøPermanent 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.

SKILLSFUTURE MID-CAREER ENHANCED SUBSIDY (MCES)

  • The ² is meant for employer-sponsored and self-sponsored òòò½ÍøCitizens aged 40 years old and above.

SkillsFuture Credit

  • All Singaporeans aged 25 years old and above are eligible for  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

TYPECATEGORY OF INDIVIDUALS
International Participants òòò½ÍøCitizens aged 39 years and below, òòò½ÍøPermanent Residents and LTVP+ HoldersEmployer-sponsored and self-sponsored òòò½ÍøCitizens aged 40 years and above SME-sponsored local employees (i.e òòò½ÍøCitizens, òòò½ÍøPermanent Residents and LTVP+ Holders)
FUNDING SOURCE
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%
$360$108*$108*$108*
Total Nett Course Fee Payable to Training Provider$4,360$1,308$508$508
* Based on 30% of Full Course Fee

About the trainers

Dr Ian Chan

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Dr Ian Chan is a Principal Scientist with A*STAR SIMTech. His research interests include sensing, data pre-processing, machine learning, and data analytics for fault detection, fault diagnosis and failure prediction to enable condition monitoring and predictive maintenance. He has a PhD from the Department of Aeronautics & Astronautics, Stanford University.

Dr Sun Yajuan

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Dr Sun Yajuan is a Senior Data Scientist with A*STAR SIMTech. She holds a PhD specialising in intelligence systems from the National University of Singapore. She has worked closely with industry partners such as Rolls-Royce, Delta Electronics and has more than 8 years of experience in the application of data analysis and AI in a smart factory.

Dr Van Tung Tran

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Dr Van Tung Tran is a Senior Scientist with A*STAR SIMTech whose research focus is on data driven-based predictive maintenance. His research interests include sensing, IoT, signal and image processing, feature engineering, machine learning and deep learning, and time series techniques for condition monitoring and predictive maintenance of manufacturing systems.

Contact Us

  • For technical enquiries, please contact:

Dr Ian CHAN,
Email: hlchan@SIMTech.a-star.edu.sg

  • For general enquiries, please contact:

Registration

Sign up now
  • Please register for this course through our online form: Course Registration Form for Public Classes.
  • For the first question, please select "Modular Programmes (Standalone Modules)".
  • Applicants will be placed on our waiting list if the course does not have an upcoming scheduled intake.
  • When the next intake is confirmed, a confirmation email with payment information will be sent to applicants to finalise their participation.

Collateral

Schedule

Module
Skills Course Reference Number Next Intake(s)'s Training Period
(Click on the dates to view its schedules)		Registration Status
  • Data-driven Predictive Maintenance and Optimal Plan (40 hours)
TGS-2020504648
The next intake's schedule is still in the planning stage.  

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.

Announcement:

  • From 1 Oct 2023, attendance-taking for SkillsFuture òòò½Íø(SSG)'s funded courses must be done digitally via the Singpass App. More information may be viewed here.
  • Participants will be provided with digital course materials when attending our courses. Please note that printed copies will not be available.
Sessions
FD: Full day
AM: Morning
PM: Afternoon
EVE: Evening

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