10.5 Asset Reliability Improvements

Artificial Intelligence (AI) technology is developing rapidly and is appearing more and more often in asset intensive organizations. It is both a Threat and Opportunity. To some it is a tool for making better or faster decisions; and on systems where the output is king that could be a driving factor. For others, it is another layer of fog hiding decision making processes. Evolution of this technology is moving much faster than regulation, and in an unregulated space within a capitalist economy, where AI can provide a commercial advantage it will be used and advanced. How is AI being used in asset intensive organizations today, what could tomorrow look like? What skills and knowledge should Asset Management Professionals be investing in to work in a world with fast evolving AI systems?

If we were to do reliability analysis on certain assets, which assets are worth our effort and provide the greatest impact? To solve this, our team developed a focused methodology to help us identify which asset types need to be further analyzed in the coming years. Our goal is to identify the asset type that causes the highest generation of lost opportunity cost and the highest maintenance costs. Ideally, the generation of lost opportunity cost should be added to maintenance cost. Lost opportunity cost is calculated from existing operational performance data, which is collected and used for NERC GADS (North American Electric Reliability Corporation Generating Availability Data System) reporting. Maintenance cost is calculated from maintenance history in CMMS (Computerized Maintenance Management System). The operational performance and maintenance history data are illustrated using a Pareto chart. As a result, the asset type that causes the highest generation of lost opportunity cost can be identified from the chart. However, asset types with the highest maintenance costs may not indicate the asset type with the most issues. Besides, the NERC GADS codes may be related to one or more assets in CMMS and, therefore, need a relationship to be created if the costs are to be added.

Cameco Corp. has recently deployed approximately 1,500 wireless asset condition monitoring sensors across four of its operations. This presentation will explore all aspects of this project, from initial identification of business pain, all the way through to deployment and management of the system. Condition-based monitoring of rotating assets typically involves a route that is executed at a fixed interval to collect asset condition data. This data can include vibration, temperature, acoustic emissions, and others. This data is then downloaded into software and analyzed for faults and trends. This method has many shortcomings that can be solved with remote sensing technology. This presentation will take you through Cameco’s journey of identifying the limitations of traditional data collection and why an alternative was investigated. Some of the key topics will include problems and inefficiencies with the current system, methodology used to determine which sensor company to partner with, potential cost savings and benefits, deployment strategy and execution, and some screen captures of actual asset detections. Finally, we will conclude with lessons learned and benefits realized from deploying a sensor solution.

An Intelligent machine can sense its environment, take a decision and apply an action or give a recommendation. How this can transform maintenance? With every Industrial Revolution, Maintenance tools and strategies grow to provide the needed service for this industrial era. Now what shall we do with the Augmented reality and other technologies that Industry 4.0 introduces in the production environment??! It is important to understand what AI -Artificial Intelligence in details is because it is currently part of our work and life even if we do not realize this. When we understand how AI works, we can use it as our ally. Otherwise, we shall resist its existence specially when it starts to give recommendations and report of what went good and what went bad. AI is a title frequently applied to the project of developing systems with the intellectual similar to those of humans, such as the ability to reason, discover meaning, generalize, or learn from past experience. The human maintenance team applies its intellectual process at every situation it encounters. This intellectual process includes the ability to reason, discover meaning, generalize, or learn from experience. How AI does this? It is all based on software and Algorithms. There are two (2) ways for the software to yield intelligent advices or actions. You either add to it all the possible solutions of a problem and the software searches through all possibilities to find a one matching to this situation, then returns the stored actions for this possibility. Alternatively, the other way is to let the algorithms of the intelligent software infer some reasoning based on the inputs then solve the problem. . Let us relate this to maintenance

Are we converting our data to decisions? What is the state of digital adoption in asset management? What has changed since the onset of Covid? What has stayed the same? Using the DIKW Pyramid as our guide, combined with the experience and insights of our panelists; we will explore best practices in data-informed decision-making. Are we now in a much different place on our digital adoption journey?  

Recently there was a lot of discussion on various forums around the term ‘Human Asset’. It was prompted by a paper/presentation made by Dr. Monique Beedles in Australia where she questioned the term and the appropriateness of using it based on the definition of an asset in ISO 55000 – ‘an item, thing, or entity that has value or potential value to the organisation (ISO, 2014)’.There were questions posed on the various forums as to the use of ‘thing’ to describe humans – it’s legal meaning, it’s cultural implications and though most posting felt the term was appropriate, there was definitely a difference of opinion. What wasn’t brought out in most of the discussions was the intent or suitability of the decision to word the definition that way. No-one disagreed with the idea that a human ‘has value or potential value to the organization’ and so rather than argue about the term, I’d like to explore how we could apply the tools and lifecycle approach we use for physical assets, to humans. Let’s walk through the lifecycles and see if we can find how it might apply to humans.Concept Design Acquire Operations and Maintenance Decommission So as I said I won’t address the term ‘human asset’ but hopefully you can see that taking the same approach to ensure that the ‘value or potential value to the organization’ is certainly a good alternative.

An Asset Health Index or AHI refers to analysis performed using various asset data to determine the state or condition of the asset. AHI can be used to better assess asset condition, used and useful life, progression toward potential failure, and failure probability. Further, using AHI can also enable the development of optimized maintenance and replacement strategies for assets using a set of objective criteria to assess the true health of the asset. However, entities vary widely in whether they develop Asset Health Indexes (AHIs) for their key assets. For those that do, there are marked differences in the level of rigour and sophistication employed in developing and applying AHIs for effective asset management decision-making. AHI calculations involve identifying and collecting data which may include a review of core asset attributes such as manufacturer, inspection data including field observations, destructive and/or non-destructive test data, maintenance data including historical records, operational records, and asset failure/refurbishment data. In other words, some are core inventory data, some work records, and some inspections or tests. This presentation will go through how to make the best use of asset SMEs and how you can start to develop useful AHIs from what you already know/have. Technically, the process begins with identifying the most critical assets and determining which can best benefit from AHI formulation development. The next steps are used to develop proposed condition factors (CF) and weighting factors (WF) that provide insight into the condition of the assets. Finally, CFs and WFs are used to develop a mathematical algorithm or formulas for the Health Index. We will also discuss how AHI can be used to develop asset management and maintenance strategies – the whole point of the data and analysis in the first place.

We know that reliability has value to your business, but many of us with technical backgrounds struggle to present a good business case to decision-makers. We are very often held back by budget constraints and we are not in a position to make decisions involving financial risk-taking. Most of us don't have a business background, nor do we speak "finance". It is a whole different language than maintenance and reliability, yet we all want the same things for our business. This presentation will give you some ideas on what you will need to determine in order to show what reliability is worth, and how to present that to decision-makers.

Sharing a recent mandate with a mining client where we assessed the maturity of maintenance management. The webinar will also cover the findings and our actions for improvement.Approach   1. After dozens of interviews from trades to maintenance manager, from operators to supervisors, we started to gather an understanding of the situation.   2. Then, a thorough analysis was performed on work order history, PM percentage vs total available work hours, schedule compliance and production KPIs to help pinpoint several recurring issues.   3. The analysis was concluded with on-site shadowing of supervisors and trades. We selected key dates to execute day-in-the-life-of exercises (i.e., shutdown days) to assess the quality of shutdown management and observe the execution effectiveness. Actions in partnership Tool time is a powerful metric because it helps to expose the majority of the teams’ shortcomings: engagement of the team, respect of processes, communication between departments and productivity of all the maintenance members. After the assessment and analysis, we got to work:   1. Work management process   2. Maintenance tactics review     3. Maintenance execution   4. Spare parts analysis Results With a SWAT team approach, we reduced maintenance backlog by 20%, which allowed us to focus on high criticality assets. In less than 8 weeks, we started to see a reduction in major breakdowns of equipment. We started performing root cause analysis on each breakdown and defect elimination workshops to achieve better reliability. By reducing waste related to maintenance activities in planning & execution, the team managed to transfer the work hours toward preventive tasks. The actions got translated into an improved plant availability and higher engagement

Presenting Tactical Asset Management deployment and case study overviews implemented at the Regional Municipality of Halton, Water and Wastewater Treatment Division. Demonstrating how collaboration between Maintenance, Operations and Capital can effectively optimize asset value and promote innovation to how municipalities generally view asset management in an industrial setting.Sharing a vision to rebrand the function of Maintenance to “Tactical Life Cycle Management” when aligned with Municipal asset management plans and how this can be achieved through Assessment based Maturity Mapping of the maintenance function.