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

*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

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

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

download brochure OR register of interest for course

Please register your interest for this course under 1. Modular Programmes > 2. Predictive Maintenance in the online form.

Note: SIMTech will contact you once registration for the next run of the course you are interested in opens.

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

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

International Participants	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	$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.

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

SME-sponsored Trainees:

Must be Singapore Citizens (SCs), Singapore Permanent Residents (SPRs) and/or Long Term Visit Pass Plus (LTVP+) holders.
Courses have to be fully paid for by the employer.
Trainee is not a full-time national serviceman.

SME companies are reminded to visit https://www.enterprisejobskills.gov.sg and click on ‘Digital Services’ to check that the status of your company is updated as “SME” so that their sponsored participants are eligible for the ETSS funding.

² Under the SkillsFuture Mid-Career Enhanced Subsidy (MCES) for employer-sponsored and self-sponsored Singapore Citizens aged 40 years old and above.

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

Full Course Fee	International Participants	Singapore Citizens (39 yrs old or younger), SPRs or LTVP+ Holders		Singapore Citizens Only (40 years old and above) ²
	$4,000	$4,000	SME Sponsored ¹	Singapore Citizens Only (40 years old and above) ²
	$4,000	$4,000	$4,000	$4,000
SkillsFuture Funding	Not Applicable	($2,800)	($3,600)	($3,600)
Total Gross Fee	$4,000	$1,200	$400	$400
7% GST	$280	$84*	$84*	$84*
Total Course Fee Payable to SIMTech	$4,280	$1,284	$484	$484

* Based on 30% of Full Course Fee

Contact Us

For technical enquires, please contact Dr LUO Ming, Email: mluo@SIMTech.a-star.edu.sg
For general enquiries, please contact Mr Alex TAY, Email: Alex_Tay@SIMTech.a-star.edu.sg

download brochure OR register of interest for course

Please register your interest for this course under 1. Modular Programmes > 2. Predictive Maintenance in the online form.

Note: SIMTech will contact you once registration for the next run of the course you are interested in opens.

Schedule

Module	Skills Course Reference Number	Training Period (Click on dates to view schedule)	Registration Status
Data-driven Predictive Maintenance and Optimal Plan (40 hours)	TGS-2020504648	31 Jan 2023 - 16 Mar 2023
Digitalising Maintenance Operations for Productivity Improvement (16 hours)	TGS-2022014293