Introduction to Predictive Analytics for Maintenance

Course Date:
04 Jun 2019

Registration Period: 
01 Apr to 17 May 2019

8 hours / 1 day

9am to 6pm

Mode of Training:

SP Laboratory

Course Introduction

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 moniitor 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


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

Suitable for

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

Minimum Entry Requirements / Assumed Skills and Knowledge

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

Award / 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

Course Fees payable:

Singapore Citizens aged below 40 and Singapore PRs Singapore Citizens Others
Non-SME sponsored 1Sponsored by SME 2Aged 40 and above
(incl. GST)
(incl. GST)
(incl. GST)
(incl. GST)

Singaporeans aged 25 years and above may use **SkillsFuture Credit balance to offset respective course fees.
1 Under the Enhanced Training Support for Small & Medium Enterprises (SMEs) Scheme. For more information, click herePlease submit the attached “Declaration Form for Enhanced Training Support Scheme for SME” together with your online application.
2 Under the SkillsFuture Mid-career Enhanced Subsidy. For more information, visit the SkillsFuture website here.

Funding Incentives

Please click here for more information on funding incentives.


Register Here
Eligible for SkillsFuture Credit

Terms and Conditions

Application Procedure

1. All applications must be made via Online Registration at
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
PACE Academy

c) Cheques
Please make cheques payable to “Singapore Polytechnic”. Do cross the cheque and write the Registration Reference ID, Applicant Name and NRIC/FIN number at the back of the cheque. When you have completed required details, you may mail the cheque here.

Please note that an administrative charge of $15 will be imposed for any returned cheques from the bank or financial institution.

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

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