Description
In this programme, you will learn to make (better) use of Artificial Intelligence applied in maintenance and asset management as a maintenance or reliability engineer. After all, AI allows to predict upcoming failures, thus avoiding unplanned downtime and technical incidents.
In the future, you will have to deal with Artificial Intelligence (AI) in your organisation. It is therefore important to get acquainted now with the possible applications of this technology and its impact on your range of tasks. I-care has therefore set up an awareness training for maintenance & reliability engineers around AI in collaboration with various specialists in the field.
Recommended audience
Reliability and/or maintenance engineers, and any technical profile in maintenance and reliability who is interested in the building blocks of AI, in the technical aspects such as software and data but equally in the organisational and human impact of AI.
Learning Objectives
- Understand the role and impact of Artificial Intelligence (AI) in maintenance and asset management
- Learn how to use AI to predict failures and prevent unplanned downtime
- Become familiar with the various applications of AI in an industrial context
- Be able to anticipate the impact of AI on maintenance and reliability tasks
- Develop an understanding of the challenges related to AI integration within an organization
- Raise awareness of the opportunities and limitations of AI in maintenance processes
- Acquire a solid foundation to collaborate effectively with AI specialists
Program
- What is AI and how it can be applied in maintenance and operations
- AI analysis techniques and commonly used terms in AI, predictive analytics and big data analytics
- Maturity levels of applying AI in maintenance and operations
- Impact AI algorithms on quality, reliability and productivity
- What is data capture and what are the possibilities
- Importance of maintenance data quality / quality maintenance intervention reports for the (later) application of AI
- Importance of Cybersecurity and Data Security and how this can be addressed
- Opportunities to capture data through additional and existing sensors and devices
- M2M communication, wireless and wired, over short and long distances
- Software tools and data platforms are available to run and manage AI applications
- Critical success factors to achieve reliable results with AI and predictive analytics for maintenance applications
Prerequisites
Nihil
Course duration
2 days