Course Overview
In Industry 4.0, smart maintenance enhances equipment efficiency using digital tools, reducing downtime, and optimising performance, creating significant value for companies.
Smart maintenance centres on integrating and analysing data collected from machines, facilities or buildings through advanced sensors. These sensors continuously monitor equipment functionality and performance. Leveraging real-time data enables companies to implement proactive maintenance strategies and enhance equipment health.
This micro-credential provides learners with the knowledge and skills needed to implement a comprehensive smart maintenance strategy. It also introduces the integration of algorithms and monitoring techniques with Failure Mode, Effects, and Criticality Analysis (FMECA) to ensure a robust maintenance approach.
Key topics include maintenance engineering principles, which cover practical methods and tools for developing and maintaining highly reliable plants, products, and services. It focuses on meeting design requirements while reducing life-cycle costs and promoting sustainability.
Learners will gain expertise in smart sensing for condition monitoring, equipping them with the essential skills to design and implement machine monitoring systems with smart sensors for engineering applications. Additionally, they will learn about condition-based maintenance, focusing on machine health monitoring techniques. This includes topics in data cleaning and noise filtering, feature extraction, fault detection, fault diagnosis and machine condition prediction.
This micro-credential is part of the CSM Pathways in Infrastructure and Systems Engineering.
Who Should Attend
-
Learners with relevant polytechnic backgrounds seeking to augment their skill set with smart maintenance technologies.
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Maintenance, production or process engineers in manufacturing, as well as engineers responsible for critical facilities.
Assumed Prior Knowledge
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Learners are required to have knowledge of programming, engineering mathematics and data analytics.
What You Will Learn
This micro-credential is predominantly delivered through a competency-based education (CBE) approach where learners acquire and demonstrate mastery of knowledge and skills that are directly relevant to job functions. This prepares them to be industry-ready where they can apply their newly acquired competencies to their work.
List of Competency Units
Code | Competency Unit Title | Credits |
---|---|---|
ENG2400C | Maintenance Engineering | 6 |
ENG3400C |
Smart Sensing for Condition Monitoring |
6 |
ENG3401C | Condition-based Maintenance | 6 |
The above are competency units that constitute this micro-credential. Upon completion of the micro-credential, you will be able to:
- Analyse the reliability of engineering systems and recommend improvements in maintenance-related activities using tools such as Root Cause Analysis, Fault Tree Analysis and Failure Mode, Effects, and Criticality Analysis (FMECA).
- Design and build machine condition sensing and data acquisition solutions by applying sensing principles for machine conditions, integration of sensors with embedded hardware and data acquisition and utilisation of IoT and cloud technology for ‘smart’ sensing.
- Implement a fault detection and diagnosis system and apply data analytics for machine condition prediction.
Coaching for Success
During the course, you will have access to a team of qualified success coaches who can work with you on learning strategies or to develop a personalised learning plan. Through the success coaches, you can gain access to a wide range of resources and support services, and be empowered with the necessary tools to navigate your learning journey successfully.
Teaching Team
Zhou Junhong
Associate Professor, Engineering, Singapore Institute of Technology
Hoh Hsin Jen
Assistant Professor, Engineering, Singapore Institute of Technology
Gan Hiong Yap
Associate Professor, Engineering, Singapore Institute of Technology
Kiew Choon Meng
Assistant Professor/Prog Leader, Engineering, Singapore Institute of Technology
Ng Bor Kiat
Associate Professor, Engineering, Singapore Institute of Technology
Wang Yu
Senior Professional Officer, Singapore Institute of Technology
Vincent Chan Siang Huat
Senior Professional Officer, Singapore Institute of Technology
Schedule
Week | Learning Activity | Delivery, Location and Time |
---|---|---|
1 – 12 |
Self-directed learning (pre-recorded videos) and discussion forum |
Asynchronous online |
1 – 12 | Integrative session | Synchronous online |
3, 5, 8, 11, 12 & 13 | In-class assessments |
In-person |
Certificate and Assessment
A Specialist Certificate in Smart Maintenance will be issued to learners who:
- Attend at least 75% of the course and
- Undertake and pass all credit bearing assessments
Assessment Plan
- The learner will undertake a combination of quiz tests and projects
Fee Structure
The full fee for this course is S$10,006.20.
Category | After SF Funding |
---|---|
Singapore Citizen (Below 40) | S$3,001.86 |
Singapore Citizen (40 & Above) | S$1,165.86 |
Singapore PR / LTVP+ Holder | S$3,001.86 |
Non-Singapore Citizen | S$10,006.20 (No Funding) |
Note:
- A one-time, non-refundable matriculation fee of $54.50 will be collected before course commencement.
- All fees above include GST. GST applies to individuals and Singapore-registered companies.
Course Runs
Learning Pathway
Earn Stackable Specialist Certificates
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