Develop Machine Health Monitoring Techniques

TDeecvhenloiqpuMesac(4h2ihnoeurHs)ealth Monitoring Skills Course Reference Number: CRS-Q-0038721-AER This course aims to provide participants with knowledge and practical skills on using machine health monitoring techniques in sensor, data acquisition, signal processing, machine monitoring and fault diagnosis. The course training also elaborates on the latest technology such as the Internet of things (IoT), maintenance management system and overall equipment effectiveness (OEE) monitoring system. The comprehensive structure of this course incorporates classroom lectures, hands-on experimental work, and suitable case studies in the areas of rotary equipment, tool condition monitoring, semiconductor and other service and equipment maintenance industry to enhance the knowledge of participants and apply the newly-learnt skills to address practical problems in their workplace. Why This Course • Designed specifically to meet Singapore’s industry demand • Delivered by technology experts with many years of industrial projects’ experience • Highly practical and hands-on work intensive • Latest knowledge and up-to-date technologies via industrial case-studies Who Should Attend This course is designed for operations and maintenance managers and engineers, equipment designers, process managers and engineers, hardware and instrument specialists, machine, line or cell systems integrators, project managers and professionals from industries utilising machine health monitoring, including precision engineering, electronics, aerospace, marine engineering, renewable energy, medTech, and remanufacturing, etc. Upon Completion of this Course Electronic certificates (e-Certs) are issued by SkillsFuture Singapore (SSG) to participants who have attended and attained competency in the Singapore Skills Framework training modules. When & Where Please visit our website at KTO.SIMTech.a-star.edu.sg for the course schedule. Training Venue: Singapore Institute of Manufacturing Technology 2 Fusionopolis Way, Innovis, Level 8, Singapore 138634

What You Will Learn Overview of Machine Health Monitoring • Preventive Maintenance and Predictive Maintenance • Advantages and Disadvantages Comparison Machine Health Monitoring Techniques • Sensors Selection • Data Acquisition • Signal Processing and Feature Extraction • Remote Monitoring of Machines Conditions Machine Monitoring Application in Semiconductor Industry • SEMI, SEMATECH, ARAM Standards • Semicon Roadmap on Equipment Health • Concept and Components of Potential System Machine Health Monitoring and Maintenance Management System • OEE Monitoring System • Tool Condition Monitoring • Maintenance Management System (MMS) • IIoT-enabled Equipment Condition Monitoring and Alert (IECOM) About the Course Leaders Dr Zhou Junhong is a Principal Research Engineer at SIMTech. She has many years of research and industry experience in sensor selection, machine interfacing, data acquisition, automated signal processing, advance feature extraction, remote monitoring, fault detection and diagnosis. She is SIMTech’s Overall Equipment Effectiveness (OEE) Initiative Lead for improving overall equipment effectiveness. She obtained her PhD Degree in Engineering from the Nanyang Technological University, Singapore. Dr Ian Chan is a Senior Scientist and Group Manager at SIMTech. He received his BEng in Mechanical Engineering from the National University of Singapore, and his M.S. and PhD from the Department of Aeronautics & Astronautics, Stanford University. His previous experience includes aerodynamics research at DSO and structural health monitoring development at Acellent Technologies, Inc (CA, USA). Dr Chan has worked in several areas of condition monitoring for aerospace structures, concrete structures and production machines. His interests are in guided wave sensing, vibration sensing, power sensing, signal processing and data analytics for machine fault diagnostics, health trending and machine operating mode classifications. Dr Goh Kiah Mok is a Principal Research Engineer at SIMTech with over 30 years of research and industrial experience. He specialises in factory systems design, integration, equipment controllers, embedded systems, CIM framework and semiconductor equipment SECS/GEM standards. Dr Goh has been conducting numerous courses to the industries especially in SECS/GEM standards and embedded controller. He is a SEMI Interface and communication voting member. Course Fee and Funding • The full course fee for this module is S$3,000 before SkillsFuture funding & GST. • All Singaporeans and Permanent Residents aged 21 years and above can enjoy SkillsFuture funding of up to 70% of the course fee. • Singaporean or Permanent Resident employees fully sponsored by SMEs can enjoy SkillsFuture funding support of up to 90% of the course fee under the Enhanced Training Support for Small & Medium Enterprises (SMEs) scheme, subject to eligibility criteria. • Singaporeans aged 40 years and above can enjoy SkillsFuture funding of up to 90% of the course fee under the Mid-career Enhanced Subsidy (MCES). • Singaporeans aged 35 years and above with earnings not more than S$2,000 per month can enjoy SkillsFuture funding for up to 95% of the course fee under the Workfare Training Support (WTS) scheme. • 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 about the funding, please visit SkillsFuture Singapore website at www.ssg.gov.sg Singapore Institute of Manufacturing Technology For enquiries, please contact: 2 Fusionopolis Way, Innovis, #08-04, Singapore 138634 Dr Lin Wei Tel: +65 6590 3193 Email: [email protected] Email: [email protected] | Website: KTO.SIMTech.a-star.edu.sg 02/2020