Course Schedule
Time: 9am to 5pm
Delivery Mode: Face-to-face Classroom
Venue: Ngee Ann Polytechnic
For self-sponsored applicant, you may proceed to apply after signing in with Singpass / Student ID.
For company-sponsored applicant, please approach your company HR to put in the application via the company portal using Corppass at STEP.
If you are not able to join the above course intake(s) or the course intake is currently not open for registration, please click on the "register interest" button to register your interest for the course.
By registering interest for the course, you will be notified to register for the course when the next course intake is open for application again.
About Course
Course Objectives
This is a 4-day course which, consist of theory lessons and practical session. The participants will take an assessment on the final session.This course covers theory and practical skills which will equip participants with skillsets covering electrical installation, motor drives and predictive maintenance for electrical installations/assets.
Predictive maintenance in electrical systems uses data-driven monitoring to detect faults early, reducing unplanned downtime and energy inefficiency. By ensuring equipment runs at optimal conditions, it minimises energy losses, lowers carbon footprint, and extends asset lifespan – supporting cleaner and more sustainable operations and contributing to the broader goals of a green economy.
Participants will be introduced to designing concepts, installing and troubleshooting an electrical installation, and knowledge in motors and drive systems. Building on the fundamental electrical installation and motor drives concepts, participants will then gain exposure to data gathering and applying predictive maintenance strategies.
Topics such as statutory requirements for electrical installation, design/selection considerations for protection devices, and power cables and how to replace them, motor and drive systems will be covered. Participants will also analyse electrical parameters like voltage, current, and power to assess the health of electrical assets and their associated circuits. Changes in these signatures can reveal problems with the assets, enabling predictive maintenance.
Course Description
At the end of the course, participants should be able to:Understand the purpose of predictive maintenance and how predictive maintenance can reduce the frequency of interruptions and prolong an equipment lifespan.
Using data from predictive maintenance to reduces unplanned disruptions which may lead to inefficient energy surges. Optimizing the system energy consumption and leaving cleaner carbon footprint for a green economy.
Interpret electrical layouts and schematic diagrams to identify areas to implement predictive maintenance regime.
Size protective devices, select cable sizes and use of appropriate earthing system. Plan replacement of cables or protective devices upon signs of deterioration for good predictive maintenance practices.
Understand operation of key sensors and signals from motor and drive systems. Interpret data gathered from motor circuit analysis to detect any possible fault which may lead to failure as part of predictive maintenance routine.
Terms & conditions
Terms & conditions details
Disclaimer
Ngee Ann Polytechnic owns and maintains this website as a service to its students, staff and members of public. We invite you to visit frequently to browse through our pages and download and copy materials for your personal use, subject to the restrictions outlined below.
Links to Third Party Information
This website has links to many third party information providers who are not affiliated with the Polytechnic. The Polytechnic does not endorse any of these sites, nor the information or merchandise contained or offered by these sites. Ngee Ann Polytechnic cannot make any warranties as to the accuracy, reliability or security of any information you might be requested to give any third party and, in no event shall the Polytechnic be liable for any damages whatsoever.
Information and documents provided on this website are provided “as is” without warranty of any kind, either express or implied, including without limitation warranties of merchantability, fitness for a particular purpose and non-infringement. Ngee Ann Polytechnic provides, with reasonable effort, accurate and up-to-date information on this website. It does not, however, make any warranties or representations as to its accuracy or completeness. It periodically adds, changes, improves or updates the information and documents on this website without notice.
Limitation of Liability
Ngee Ann Polytechnic assumes no liability or responsibility for any errors or omissions in the content of its website. You are using the website at your own risk. Under no circumstances shall Ngee Ann Polytechnic or any other party involved in creating, producing or delivering the contents of this website be liable to you or any other person for any direct, indirect, special, incidental or consequential damages arising from your access to, or use of, this website.
The use, reproduction, copying, or redistribution of the Ngee Ann Polytechnic logo is strictly prohibited. Written permission must be obtained from the Corporate Communications Office for any such request.
All software and/or documentation made available on this website for downloading is the copyrighted work of the relevant suppliers. Ownership of the software and documentation belongs to the copyright holder. Thus ownership is not transferred to you if you download. The use of any such software and/or documentation is subject to the terms of a license agreement as provided by the supplier of the software. Please read the licence agreement if you intend to install the software.
These Terms of Use shall be governed and construed in accordance with laws of the Republic of Singapore.
The use of datasets that Ngee Ann Polytechnic publishes to the public free-of-charge (Open Data) will be governed by the Singapore Open Data Licence.
The Singapore Government may, from time to time, issue new versions of the Singapore Open Data Licence. Your continued Use of the datasets constitutes your acceptance of the new version of this Licence.
Course Structure
TPG course reference No.
TGS-2025059233
This course covers theory and practical skills which will equip learners with skillsets covering electrical installation, motor drives and predictive maintenance for electrical installations/assets.
Learners will be introduced to designing concepts, installing and troubleshooting an electrical installation, and knowledge in motors and drive systems. Building on the fundamental electrical installation and motor drives concepts, learners will then gain exposure to data gathering and applying predictive maintenance strategies.
Topics such as statutory requirements for electrical installation, design/selection considerations for protection devices, and power cables and how to replace them, motor and drive systems will be covered. Learners will also analyse electrical parameters like voltage, current, and power to assess the health of electrical assets and their associated circuits. Changes in these signatures can reveal problems with the assets, enabling predictive maintenance.
Entry Requirements
Financial matters
Course fees payable (incl. GST & excl. supplementary fee)
| Description | Predictive Maintenance for Electrical Systems and Motor Drives | Total course fee |
|---|---|---|
| Full Course fee | $741.20 | $741.20 |
| Enhanced Training Support for SMEs for Singapore Citizen | $86.36 | $86.36 |
| Enhanced Training Support for SMEs - SPR & LTVP+ | $86.36 | $86.36 |
| SkillsFuture Mid-Career Enhanced Subsidy for Singapore Citizen aged 40 and above | $86.36 | $86.36 |
| SkillsFuture Baseline Funding for Singapore Citizen Aged Below 40 | $222.36 | $222.36 |
| SkillsFuture Baseline Funding for SPR/LTVP+ | $222.36 | $222.36 |
GST rate
Payment option
Allowed payment by
Post-Secondary Education Account (Adhoc withdrawal form);
SkillsFuture Credits (SFC);
Credit card (e-payment);
Debit card (e-payment);
PayNow (e-payment).
Refund and withdrawal policy
- Please note that a 100% refund will be available if the withdrawal request is submitted more than or equal to 14 days before the course start date.
- Please note that a 50% refund will be available if the withdrawal request is submitted less than 14 days before the course start date.
- Please note that no refund will be available if the withdrawal request is submitted on or after the course start date.
Remarks
(i) Course fees payable;
(ii) Assessment fees which are part of the course;
(iii) Certification fees which are part of the course; and
(iv) GST imposed on components supported for SFC use.
Penalty charges apply if you:
(i) are absent/no-show;
(ii) withdraw from a course within 14 days of the course start date;
(iii) fail to meet terms and conditions for funding eligibility.