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.
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:
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.
The programme covers the following key topics:
Introduction to Data Analysis for
Predictive Maintenance and Optimal Plan
Data Collection and Processing Techniques
Machine Learning (ML) and Modelling Techniques
PdM-enabled MMS for Optimal Plan
Participants will be awarded with a Certificate of Attendance (COA) by SIMTech if they meet the following criteria:
Please note that fees and funding amount are subject to change.
¹ Under the Enhanced Training Support for Small & Medium Enterprises (SMEs) scheme (ETSS), subject to eligibility criteria shown below.
SMEs that meet all of the following eligibility criteria:
Further Info: This scheme is intended for all organisations, including non-business entities not registered with ACRA e.g. VWOs, societies, etc. Only ministries, statutory boards, and other government agencies are not eligible under Enhanced Training Support for SMEs Scheme. Sole proprietorships which meet all of the above criteria are also eligible.
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
Dr Ian CHAN, Email: hlchan@SIMTech.a-star.edu.sg
Mr WONG Ming Mao, Email: mmwong@SIMTech.a-star.edu.sg
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