course-banner-img Data Analytics-3

Introduction to Predictive Analytics for Maintenance

Topic:Data Analytics & Visualisation

Course Type:Short & Modular Courses

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Overview

  • Course Date:

    TBA
  • Registration Period:

    TBA
  • Time:

    TBA,
    8 hours / 1 day
  • Mode of Training:

    Practical Training
  • Venue:

    Singapore Polytechnic
  • Funding:

    Eligible for SkillsFuture Credit

Predictive Analytics is the science of engaging statistical and data driven approaches to build models to represent real processes, for the purpose of comparative analysis as well as making predictions / forecasts. This is particularly so in the manufacturing environment where process monitoring and predictive models are very critical in pre-empting failure. In an automated manufacturing environment, process data is produced as at incredible rate and the ability to collect, store, analyse, model and predict process behaviour has become increasingly more important. Predictive Analytics for maintenance is an integral approach that unifies analysis, modelling and predictive approaches to help pre-empt failure and thus better manage maintenance activities. This course introduces basic skills to perform analytics for maintenance.

Course Objective

This is a basic course in Predictive Analytics for Maintenance The course aims to equip the participant with the following:

1. An appreciation of statistical concepts that underpin Predictive Maintenance ideas
2. An understanding of simple statistical process monitoring techniques used in tracking developments of critical measurements
3. An understanding of Correlation and Simple Regression Analysis used in degradation data monitoring / modelling
4. Simple ideas in failure time modelling that are useful in scheduling of preventive maintenance activities.

By the end of the course, learners will be able to:

• Recognise and apply simple statistical concepts used in Predictive Maintenance analysis
• Apply simple statistical process monitoring techniques to monitor process outputs
• Use simple regression analysis to model degradation data in order to predict expected failure time
• Model failure time of product / system to better manage maintenance activities

Course Outline

Topics:

• Basic Statistical Concepts and Distributions 
• Statistical Process Monitoring of measurements 
• Correlation and Simple Regression Analysis of Degradation Data 
• Failure Time Modelling 

Minimum Requirements

Participants with an engineering background who are involved in
monitoring manufacturing processes or equipment failure.

Certification / Accreditation

• Certificate of Attendance (electronic Certificate will be issued)
A Certificate of Attendance will be awarded to participants who meet at least 75% attendance rate

• Certificate of Performance (electronic Certificate will be issued)
A Certificate of Performance will be awarded to participants who pass the examination and meet at least 75% attendance rate

Suitable for

Manufacturing Engineers, Engineers monitoring process/product / system degradation or failure

Course Fees

Full Fees (before GST): $480.00

Applicants/Eligibility SkillsFuture Funding GST Subsidised Fee (after GST)
Singapore Citizens aged 40 and above¹ $432.00 $12.96 $60.96
Singapore Citizens aged below 40 $336.00 $12.96 $156.96
Singapore Permanent Residents and LTVP+ Holders $336.00 $12.96 $156.96
SME-sponsored Singapore Citizens, Permanent Residents and LTVP+ Holders² $432.00 $12.96 $60.96
Others (Full fees payable) $0.00 $43.20 $523.20

Singaporeans aged 25 years and above may use **SkillsFuture Credit balance to offset respective course fees.

1 Under the SkillsFuture Mid-career Enhanced Subsidy. For more information, visit the SkillsFuture website here
2 Under the Enhanced Training Support for Small & Medium Enterprises (SMEs) Scheme. For more information of the scheme, click here. To view SP’s list of similar funded courses, click here. Please submit the attached “Declaration Form for Enhanced Training Support Scheme for SME” together with your online application.


Funding Incentives

Please click here for more information on funding incentives.


Application Procedure

1. All applications must be made via Online Registration at www.pace.sp.edu.sg
Course fees can be paid by the following payment modes:

a) Credit Cards, Internet Banking, NETS (Not Applicable for company sponsored)
For e-payment using Visa/Master cards and Internet Banking, please click on the ‘Make e-Payment’ button on the acknowledgement page to proceed.

b) For NETS payment, you can pay at:
Singapore Polytechnic
One-Stop Service Centre

c) For payment via PayNow, please enter the UEN No. T08GB0056ACET and indicate the invoice/registration number. 

*With effect from 1 August 2021, cheque payment will not be available.

2. All successful applicants will be notified with a letter of confirmation via email.
 

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