Smart Maintenance (Synchronous & Asynchronous e-Learning)
About This Course
Within the context of Industry 4.0, smart maintenance leverages digital tools to enhance the efficiency of maintenance and servicing, thereby fostering increased value creation for the company. Its significance lies in minimizing unforeseen machine downtime, optimizing machine health and performance, and promoting a more effective maintenance program. Smart maintenance utilizes sensors for continuous measurement of equipment functionality and performance, generating substantial volumes of data. These sensors, along with algorithms and monitoring techniques, are integrated with the Failure Mode, Effects and Criticality Analysis (FMECA) method to implement a comprehensive smart maintenance strategy. This course covers the following technologies for implementation of smart maintenance:
• Maintenance Engineering: Reliability modelling and prediction, Failure Mode, Effects and Criticality Analysis (FMECA) methods and tools to analysis both component and system perspectives. It helps to maintain highly reliable plants, products, and services, not only to meet design requirements but also reduce overall life-cycle cost and achieve sustainability.
• Smart Sensing for Condition Monitoring: Technical infrastructure of sensors, embedded system, cloud storage system and web Application programming interface (API) collect and store real-time data about machines and facilities.
• Condition-based maintenance: data and signal noise filtering, feature extraction and data analytics on the raw data helps to understand the machine condition. Fault detection, diagnosis and prognosis techniques enable condition-based maintenance.
Upon completion of this course, you will be awarded a Specialist Certificate in smart maintenance.
What You'll Learn
1. Analyze the reliability of engineering systems and recommend improvements in maintenance-related activities through various tools such as Root Cause Analysis, Fault Tree Analysis and Failure Mode, Effects and Criticality Analysis (FMECA).
2. Design and build machine condition sensing and data acquisition solutions with the components of understanding of sensing principles for machine conditions, integration of sensors with embedded hardware and data acquisition, use of Internet of Things (IoT) and cloud technology to realise the ‘smart’ sensing.
3. Implement a fault detection and diagnosis system and apply data analytics for machine condition prediction.
Entry Requirements
Students are required to have knowledge of programming, Engineering Mathematics, data analytics.