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Session 1: October 19th (13:30 -15:00 EST) - Best Practices, Benefits and the Roadmap
This course will provide a framework that can help accelerate the introduction of reliability programs and asset analytics by maximizing the use of existing data and systems. The instructors will discuss best practices when it comes to reliability that supports more condition and predictive asset management. The instructors will cover the following topics:
- Why should you move towards RbAM and what foundational elements are required at different stages?
- How can you leverage your existing IT infrastructure? What other systems should you invest in?
- How can you unlock your asset data? How to manage your asset data to avoid data quality issues?
Session 2: October 20th (13:30-15:00 EST) - From RCM to RAM to Predictive Analytics, a hands-on workshop
This second session will use hands-on, real examples from mines & mills of methods such as life data analysis, RAM simulation, and predictive models. Participants will get a richer understanding of the different reliability methods with different data and systems requirements to achieve goals.
- How and when to apply different reliability methods from RCM, RAM to predictive analytics?
- What are the data sources, knowledge, and systems required to implement the different methods successfully?
- How to integrate these techniques within existing systems and processes for maximum impact?
Session 1: October 19 (13:30 -15:00 EST) - Best Practices, Benefits and the Roadmap
Learn about proven strategies, to successfully institute reliability-based asset management practices at mines and mills, that leverage existing operational and IT systems to its full extent.
Session 2: October 20 (13:30-15:00 EST) - From RCM to RAM to Predictive Analytics, a hands-on workshop
Using examples, learn about proven techniques like life data analysis, RAM simulation and new predictive analysis methods and how they build upon foundations (RCM, RCA) for reliability-based asset management
Who:
Reliability and Asset Management practitioners and Mine Management
Why:
Mines & mills are constantly looking to improve equipment reliability and institute new, best work practices. However, a clear understanding of the requirements in terms of data, systems, and work processes is not widely known and accessible. This session aims to provide participants with practical approaches to overcome data quality issues and pursue more data-driven reliability practices.
Mines & mills are looking to continually improve equipment reliability in a sustainable manner while leveraging investments being made into digital initiatives. Often this is a gradual and stepwise process and not without roadblocks. This course aims to outline common approaches and critical steps sometimes overlooked from multiple industries that can lead to successfully instituting reliability-based asset management practices at mines and mills.
To purchase access to the 2 session of this short course, please look for the "Add to cart" option in the Session 1 or Session 2 boxes below.
Non-Member price: $499 (Includes session 1 & session 2)
A discounted price is available for Members and Students.
Member price: $299 (Includes session 1 & session 2)
Student price: $149 (Includes session 1 & session 2)
Group registration: for registrations of 5 participants or more please contact Mélanie Gauthier: mgauthier@cim.org.
Session 1: September 9 (13:30 -15:00 EST) - Best Practices, Benefits and the Roadmap
Adam Mettas has over 20 years of experience in the development and implementation of Asset Lifecycle Performance management systems and solutions across industries. He is also the founding member of ReliaSoft a leading software solutions brand. Adam will outline how organizations can justify and pursue transitioning to more reliability-based asset management and what are the different requirements along the journey.
Iain Dodds has over 25 years of experience in the development and deployment of enterprise-level engineering software systems. Previously he worked at General Motors – OnStar as Senior IT Innovation Manager. His broad experience spans multiple domains such as engineering analysis, vehicle telematics, scalable data monitoring, and management systems. Iain will outline successful strategies to scale asset data collection, monitoring, and prognostic systems.
Dr. Kevin Knill has over 35 years of experience in developing software products and modeling techniques for industrial applications. He has numerous products and patents to his credit currently used across multiple industries. Dr. Knill will outline what systems are required to support data-driven methods and how best to integrate them with existing systems.
Session 2: September 10 (13:30-15:00 EST) - From RCM to RAM to Predictive Analytics, a hands-on workshop
Dr. Kevin Knill has over 35 years of experience in developing software products and modeling techniques for industrial applications. He has numerous products and patents to his credit currently used across multiple industries. Dr. Knill will outline what systems are required to support data-driven methods and how best to integrate them with existing systems.
Dr. Vasileios Geroulas combines expertise in reliability, vibrations, fatigue, and machine learning to develop methodologies for estimating the remaining serviceable life of various assets. Dr. Geroulas will showcase a unique approach to combine machine learning and engineering models to predict maintenance requirements for critical assets.
Adi Dhora focuses on designing data-driven methodologies to improve the reliability of critical assets at mines and fixed plants. He will outline different reliability methods, pre-requisites for its successful application, and how to overcome typical data constraints.
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Session 1: Best Practices, Benefits and the Roadmap
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Session 2: From RCM to RAM to Predictive Analytics, a Hands-on Workshop
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