De essentiële basics van Artificiële Intelligentie voor Maintenance & Reliability Engineers
This pilot training is part of the ESF project vormAInt. Through this project, BEMAS, together with its partners, wants to offer a range of practice-oriented training courses to enable operators, technicians, managers and engineers in industrial production companies and other asset-intensive environments to make (better) use of Artificial Intelligence applied in maintenance and asset management. After all, AI enables the prediction of upcoming failures, thus avoiding unplanned downtime and technical incidents.
In the future, your organisation will also have to deal with Artificial Intelligence (AI). It is therefore important to already get acquainted with the possible applications of this technology and the impact it will have on your tasks. Therefore BEMAS, in cooperation with several specialists in the field, has set up an AI awareness training for maintenance & reliability engineers.
In this 2-day training, we will introduce the building blocks and terms for you as technical engineer in maintenance and reliability to successfully introduce AI in your organisation. We will touch upon technical aspects like software and data, but also the organisational and human impact of AI.
learning objectives
- Understand what AI is and how it can be applied in maintenance and operations
- Defining some AI analysis techniques and commonly used terms in AI, predictive analytics, and big data analysis
- Understand the different maturity levels of applying AI in maintenance and operations
- Understanding the impact of AI algorithms on quality, reliability, and productivity
- Understanding the possibilities of data capture
- Understanding the importance of maintenance data quality/quality maintenance intervention reports for the (later) application of AI
- Understand the importance of cyber security and data security and how this can be addressed
- Understanding the possibilities of capturing data via additional and existing sensors and devices
- Understanding the possibilities for M2M communication, wireless and wired, over short and long distances
- Understand which software tools and data platforms are available to run and manage AI applications
- Understanding the critical success factors for using AI and predictive analytics to achieve reliable results for maintenance applications
Programma
-Sub-module 1: Introduction to Artificial Intelligence (AI)
- What is AI / what is it not?
- Difference from the classic approach
- Frequently used terminology
- Applications and examples from everyday life
- Applications and examples specific to maintenance and engineering
-Sub-module 2: Data capture, M2M communication
- Types of data
- Layer model
- Communication protocols
- Data typology
- Compression
-Sub-module 3: Software and platforms (part 1)
- Commercial / open source
- Types of providers
- Overview of tools
- Demo of tools
-Sub-module 3: Software and platforms (part 2)
- Commercial/open source
- Types of providers
- Overview of tools
- Demo of tools
-Submodule 4: Maturity
- Technical equipment - sensors/data capture
- Data quality/availability
- IT (Data management, security)
- Light version of maturity scan
-Sub-module 5: Implementation
- Critical factors
- Cybersecurity
- (Process) safety
- Contingencies
Method
By explaining practical applications of AI in the industry (quality control, defect detection, predictive maintenance, ...) and demonstrating how certain actions can be put into practice by means of examples and/or concrete advice, you can test and apply what you have learned in practice afterwards. In addition, the material learned during the training will be tested by means of a quiz.
Target group
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.
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