Ingenjör 4.0 learning module: Predictive Maintenance

Unleash predictive maintenance: High reliability, minimum downtime, optimal asset performance

Predictive Maintenance is a dynamic and evolving area that focuses on maximizing the efficiency, reliability, and longevity of assets through proactive and data-driven maintenance strategies. This field combines the principles of maintenance engineering, data analytics, and asset management to optimize maintenance activities and minimize downtime.

In this module you will delve into the principles and benefits of predictive maintenance. They explore how predictive maintenance techniques leverage advanced sensors, data collection methods, and analytics to monitor asset performance and predict potential failures. You will gain a comprehensive understanding of different predictive maintenance techniques and tools, such as vibration analysis, thermal imaging, oil analysis, and many others condition monitoring methods.

A crucial aspect of this learning module is developing skills in data collection, analysis, and interpretation. You will learn how to efficiently collect relevant data, apply statistical methods, and interpret the findings to make informed maintenance decisions. They also acquire the ability to identify failure modes, analyze failure patterns, and develop maintenance plans tailored to specific assets or systems. The focus will be put on early detection of anomalies, facilitate condition-based maintenance scheduling, and optimize resource allocation.

A holistic understanding of predictive maintenance includes its integration into overall asset management and organizational strategy. You will learn how predictive maintenance aligns with strategic objectives, enhances asset performance, and contributes to cost savings and operational efficiency.

Overall, this learning module of predictive maintenance equips you with the knowledge, skills, and tools to proactively manage assets, enhance operational reliability, and optimize maintenance practices in various industries and sectors.

Learning objectives

Understand the Fundamentals of Maintenance and Reliability: Gain proficiency in the fundamental concepts and basics of maintenance to establish a strong foundation in the field. Learn how to identify various failure modes and develop comprehensive maintenance plans tailored to specific assets and systems.
Master the Analysis of Failures: Acquire the skills needed to identify and analyze failure modes effectively. Understand how to develop maintenance plans that are both proactive and reactive, aimed at minimizing downtime and maximizing asset longevity.
Become Proficient in Data Analysis for Maintenance: Develop expertise in collecting, analyzing, and interpreting data relevant to maintenance activities. Learn how to use this data to make informed decisions, optimize maintenance schedules, and improve overall asset performance.
Implement Predictive Maintenance Strategies: Understand the principles and benefits of predictive maintenance within the context of smart maintenance. Gain hands-on experience with various predictive maintenance techniques and tools, and learn how to integrate predictive maintenance into the broader asset management and organizational strategy.

Module structure

This module offers a comprehensive range of self-study materials to support your learning journey. You’ll have access to a variety of resources including lectures, exercises, webinars, teacher-led discussions, and laboratories. The teaching material is presented in diverse formats such as videos, simulations, and signal samples for exercises. You’ll also find reference materials, quizzes, and assignments to reinforce your understanding and assess your progress. These resources are thoughtfully designed to provide a well-rounded learning experience.

Targeted participants

Managers, Engineers and Technicians within the areas of maintenance

1. Maintenance Fundamentals (240 min)

It is an introduction to the core principles of maintenance. It covers the definition of maintenance, the different types of maintenance strategies, discusses the safety and the factors affecting dependability of assets. Further, we talk about the different steps for developing a comprehensive maintenance plan, resource allocation coordination and scheduling, and summarize the maintenance management issues in industrial settings. We provide also an comprehensive overview of RCM – Reliability Centered Maintenance as it is the best way to understand the essence of maintenance.

2. Failure Analysis (240 min)

Presents the methods of failure mode identification and root cause analysis based on practical exercises and examples. We also discuss why the equipment fails, diving into the physics of failures.

3. Data Collection and Analysis (240 min)

Presents methods for collecting and analyzing data.

4. Predictive Maintenance Techniques (240 min)

Overview of predictive maintenance methods: vibration analysis, oil analysis, thermal imaging, acoustic emission testing, ultrasonic testing, electric signature analysis, laser alignment, radiography, Barkhausen effect, load monitoring, wear particle analysis, etc. Tools and software used in predictive maintenance are presented together with hands-on exercises and demonstrations. Finally, the role of predictive maintenance in asset management, connection to SMART Maintenance, and the impact on operational efficiency is discussed.

5. Case Study & Maintenance Practices (240 min)

In-depth case study exploring a real-world maintenance scenarios to exercise on all the methods presented in this course. Let’s look at it in detail on the next slide.

6. Predictive Maintenance Techniques (90 min)

Vibration analysis
Infrared thermography
Ultrasonic testing
Oil analysis
Magnetic particle inspection
Eddy current testing
Laser alignment
Barkhausen noise other
*Other techniques

7. Predictive Maintenance Software and Tools (90 min)

Overview of predictive maintenance software and tools
Data visualization and reporting tools
Machine learning and artificial intelligence algorithms
Predictive maintenance case studies and examples

8. Maintenance Planning and Implementation (90 min)

Developing maintenance plans based on predictive maintenance data
Maintenance scheduling and optimization
Maintenance implementation best practices
Maintenance plan review and evaluation

9. Predictive Maintenance and Asset Management (45 min)

Predictive maintenance as part of an overall asset management strategy
Cost-benefit analysis of predictive maintenance
Risk management and asset prioritization
Integrating predictive maintenance with other maintenance strategies

10. Communication and Collaboration (45 min)

Communication and collaboration skills for predictive maintenance teams
Working with other maintenance teams and stakeholders
Predictive maintenance project management

11. Project. Case study /Maintenance Practices. (300 min)

12. Wrapping up (30 min)

* The module is divided into 5 sections:

Maintenance fundamentals
Failure analysis
Predictive techniques
Maintenance management
Predictive Maintenance project

To complete this module, you are expected to schedule approximately 4 hours per week over 5 weeks (20 hours in total). We assume that you will be able to complete one content section per week. Therefore, we have scheduled interactive webinars at the end of each week to provide you with the opportunity to discuss any issues with the teachers continuously throughout the module.

Important
Please note that a minimum of 10 participants is required for the module to start.

After applying to the program, please secure dates in your calendar to be able to join planned webinars and assure time available required for learning.

Partners