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Predictive Maintenance
Module developed by ingenjör4.0

Predictive Maintenance

Starting date: 2024-01-22
Price: 1 995 SEK ex. vat

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

Overview
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 modul students 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. Students gain a comprehensive understanding of different predictive maintenance techniques and tools, such as vibration analysis, thermal imaging, oil analysis, and and many others condition monitoring methods.
 
A crucial aspect of this learning module is developing skills in data collection, analysis, and interpretation. Students 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.
 
Furthermore, students gain proficiency in using predictive maintenance software and tools that aid in data visualization, trending, and predictive modeling. They explore how these tools enable 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. Students learn how predictive maintenance aligns with strategic objectives, enhances asset performance, and contributes to cost savings and operational efficiency.
 
Effective communication and collaboration skills are emphasized to foster productive teamwork with maintenance teams and other stakeholders. Students learn to effectively communicate maintenance recommendations, share insights, and collaborate on implementing predictive maintenance strategies.
 
Overall, this learning module of predictive maintenance equips students with the knowledge, skills, and tools to proactively manage assets, enhance operational reliability, and optimize maintenance practices in various industries and sectors.
The schedule
Starting date: January 22nd
January 22, 10:00 AM - 10:30 AM CET — 1st Webinar –
January 29, 10:00 AM - 10:45 AM CET — 2nd Webinar –
February 5, 10:00 AM - 10:45 AM CET — 3rd Webinar –
February 12, 10:00 AM - 10:45 AM CET — 4th Webinar –
February 19, 10:00 AM - 10:45 AM CET — 5th Webinar –
February 26, 10:00 AM - 10:45 AM CET — 6th Webinar –
Learning objectives
Understand the principles and benefits of predictive maintenance.
Gain knowledge of different predictive maintenance techniques and tools.
Develop skills in data collection, analysis, and interpretation.
Learn how to identify FAILURE MODES and develop maintenance plans.
Gain proficiency in using predictive maintenance software and tools.
Understand the role of predictive maintenance in overall asset management and organizational strategy.
Develop communication and collaboration skills for working with maintenance teams and other stakeholders.
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 Technicianns within the areas of maintenance.
Time commitment

To complete this module, the participant is expected to schedule approximately 5 hours per week over 5 weeks (20 hours in total).

Content
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
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