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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 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



download brochure OR register of interest for course

Data driven Predictive Maintenance and Optimal Plan

download brochure  SIMTech Website_Buttons-04
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

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

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.

  • 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 ParticipantsSingapore 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
     $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




    download brochure OR register of interest for course

    Data driven Predictive Maintenance and Optimal Plan

    download brochure  SIMTech Website_Buttons-04
    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
    EVE 31 Jan 2023 - 16 Mar 2023
    Register interest to Course
    Digitalising Maintenance Operations for Productivity Improvement (16 hours)TGS-2022014293  
    Sessions
    FD
    : Full Day  AM
    : Morning PM
    : Afternoon EVE
    : Evening