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Home All Courses ASSET MANAGEMENT 4.0 [AM40]


Machine Learning; AI Applications in Physical Asset Management

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Course Overview

3705 - Machine Learning & AI Applications in Physical Asset Management

Managing physical assets in today’s digitized, networked environments can be readily improved through data science. In this five-day course, you’ll learn how to analyze operational and maintenance data from a variety of sources. You’ll examine asset-management processes and strategies and identify those most relevant to your organization. You’ll probe applications of machine learning and artificial intelligence, evaluate their suitability, and implement basic machine learning algorithms in Python. You’ll emerge better prepared to lead your organization through the Industry 4.0 revolution.

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5 Key Takeaways


Describe the main components of Industry 4.0, their key benefits and drawbacks

Discern asset management processes and strategies, and identify those most relevant to your organization



Understand the concepts and the workings of various machine learning algorithms.

Identify potential applications of machine learning in maintenance and reliability problems



Evaluate suitability of different machine learning algorithms’ suitability for a variety of applications



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Course Outline


Introduction to Asset Management in the 21st Century

We will introduce some of the international standards commonly used in Asset Management (AM), such as ISO 55000 standards, GMAM documents, the AM anatomy developed by the Institute of Asset Management, the International Infrastructure Management Manual, and the latest products of the Asset Management Council of Australia. We will explain how they apply to you and your organization.

Then we will discuss how to define and set the right policies in asset management, including how to set SMART goals for your organization and for your specific assets and how to mark your progress towards those goals over time to achieve world class performance.

Finally, we turn to questions of leadership and cultural change. How can we manage all this new technology and its potential applications, and what will it mean for our people? We will still need people to perform tasks and managers to define performance requirements. How will this work in our field, as AM advances into an increasingly technological world?

Background to Asset Management (AM)

  • Why we need AM
  • What AM intends to achieve
  • International standards, documents, and frameworks, including ISO 55000, GFMAM, AM framework, IAM AM anatomy, IIMM, etc.
  • AM program structure and components

Asset Management Policy and Strategy

  • How policy and strategy work together
  • Defining organizational goals
  • Transforming goals into action through strategy

Asset Management Objectives

  • Objectives vs. goals
  • Using the AM strategy as a basis for long term implementation and sustainment

Asset Management Plans

  • What are the various life cycle AM processes, and are they all relevant to you?
  • Defining how to manage the various life cycle AM processes
  • Applying processes and implementing strategy in each asset class using technologies and various tactical approaches

Leadership and Cultural Change

  • What technology and its applications mean for the workers in an organization
  • How to manage change, including organizational culture

Performance based contracts

  • The significance of performance-based contracts



Basic Concepts in PAM

We will review the foundational concepts that enable the use of maintenance and condition monitoring data to make optimal asset management decisions, potentially saving companies millions of dollars. We will explain the use of probability distributions (and the Weibull distribution in particular) as powerful tools to describe and predict asset health over time. We will also offer some detailed procedures for using limited data to make optimal replacement decisions.

Another dimension of asset management is inspecting the asset or collecting condition monitoring data and using those readings to detect pending expensive failures and make appropriate actions to manage them proactively. For protective devices, it is necessary to periodically inspect them to ensure there are no hidden failures, and they will function in the case of an emergency to prevent costly consequences of multiple failures. With assets equipped with sensors or those with regular condition monitoring measurements, the data can be used to provide information on the health of the asset; this, in turn, is a critical tool for capital replacement planning or fit for service analysis.

Basic Concepts of PAM

→ Analysis of component failure data

→ Component replacement procedures

→ Reliability improvement through inspection

→ Life cycle costing management

Basic Conceptions in Machine Learning

The course will cover some of the most fundamental machine learning methods. C-MORE has actively applied machine learning methods to interesting real-world problems, such as the categorization of power generation units according to reliability characteristics and anomaly detection in linear assets to optimize required maintenance actions. Specific topics include types of machine learning three perspectives, steps in machine learning project, foundations of machine learning covering probability, optimization and information theory. Finally, the class will discuss how to evaluate the performance of machine learning model, pitfalls and some of remedies.

Introduction to Machine Learning

  • Computing and Big Data
  • Data science, AI, Machine learning and deep learning
  • History of AI and Big Data
  • ML in Practice

Taxonomy of Machine Learning

  • Tasks
  • Main Theories
  • Underlying Models

Steps in Machine Learning

  • Data acquisition and preprocessing
  • Algorithm selection
  • Training and Evaluation

Optimization, Probability and Information Theory

  • Random variables and probability distributions
  • Common probability distributions
  • Baye’s rule
  • Gradient-based optimization
  • Information theory

Performance Evaluation

  • Error measures
  • Bias-variance trade-off
  • Cross-validation
  • Over-fitting and under-fitting





The Centre for Maintenance Optimization and Reliability Engineering is directed by Professor Andrew K. S. Jardine, the internationally recognized maintenance optimization expert, within the Department of Mechanical and Industrial Engineering at the University of Toronto. C-MORE’s research is driven by close interactions with industry, in particular with C-MORE consortium members and with researchers at universities worldwide. Our focus is on real-world research in engineering asset management in the areas of condition based maintenance, spares management, protective devices, maintenance and repair contracts, and failure-finding intervals. These strong industry connections not only benefit the companies we work with, but also our graduate students, who find work in the maintenance divisions of industry leaders after graduation. We apply our research with prototype software tools that obtain valuable information from data in corporate databases. Two of these tools are now commercially available through the Ontario-based C-MORE spinoff company OMDEC, and through Ivara an industry leader and innovator in asset reliability solutions. C-MORE is also the driving force behind IMEC: The Asset Management Conference, which brings together leaders in the global maintenance field. For information about the conference, you can visit our site, or view Maintenance Technology’s article about the conference. C-MORE welcomes maintenance professionals as visitors and collaborators.



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Who Should Attend?

This highly practical and interactive course has been specifically designed for:

This year marks the nineteenth year that the Physical Assent Management (PAM) program has run, and the first time we have offered our exciting advanced-level Machine Learning & AI Applications in Physical Asset Management program. Managers from all corners of the globe, and from a wide variety of industrial and governmental organizations, have attended our PAM sessions.

Attendees have included line managers responsible for the maintenance of their machinery and equipment, reliability specialists who must recommend effective maintenance practices, asset managers responsible for their organizations’ maintenance strategies, and plant managers who seek excellent and proven strategies that give them competitive advantage over their competitors. If your responsibilities or interests include any aspect of managing physical assets in relation to Machine Learning, you can expect to gain a competitive edge with this exceptional learning opportunity.






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The deadline to register for a public course is 14 days before the course starts. Kindly note that occasionally we do accept late registrations as well, but this needs to be confirmed with the project manager of the training program or with our registration desk that can be reached at +971 4 447 5711 or [email protected].

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Yes, we can provide discounts for group bookings. If you would like to discuss a discount on a corporate level, we will be happy to talk to you.

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