2.02 Operations & Maintenance Decision-Making

The keynote will look at engagement in the topic of Asset Management with leaders within Manitoba Hydro, describe how at Manitoba Hydro we are continuing and expanding the conversation on Asset Management, and some feedback received on Manitoba Hydro’s Asset Management. It will also touch on an Asset Management system approach.

The City of Winnipeg faced challenges with the condition of their Return Activated Sludge (RAS) piping within the North End Sewage Treatment Plant. Non-destructive and destructive condition assessment techniques were used to determine that the critical process piping was life expired, and required replacement or rehabilitation to maintain process level of service until the plant is upgraded. Replacement of a RAS piping system is akin to major heart surgery on a sewage treatment plant. To manage the process risk, in situ structural renewal using an engineered Carbon Fibre Reinforced Polymer (CFRP) external wrap system to encapsulate the original carbon steel piping was chosen. Selection of the rehabilitation method reduced the schedule by two years compared to the replacement option and minimized operational risk, as process outages were reduced to a few short-term events. Offline testing of mock-ups and emphasis on environmental and quality control further managed the risks associated with CFRP installation. The project faced unique challenges due to the complex configuration of the RAS piping, which was located inside a congested plant gallery with surrounding equipment, piping, and electrical services in continuous operation. To effectively convey information during planning, design, tender, and construction, a digital 3D model was developed using laser scanning to capture the detailed configuration of the piping and surrounding physical constraints. The 3D model was embedded with data to define the rehabilitation scope, locations of existing pipe leaks requiring immediate repair, rework of pipe supports to accommodate the CFRP installation, and other aspects relevant to the work. This model was a highly effective tool used for collaborative review by all project team members throughout design and construction, leading to successful completion of the RAS piping rehabilitation.

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.

As maintainers, we know there is a lot of value in what we do. Without our work, plant, and equipment will soon stop and our companies will then go out of business. What we do impacts safety, health, revenues, costs, and company reputation. A dirty little truth about maintenance is that it is only we who work in it, that really know the value of what we do – or do we? We do know our value in qualitative terms, but can we quantify it? Most maintenance can be improved and we know it. We can do things more efficiently, and we can keep things running more reliably. We often know how to do that, but when we want to make those improvements there is no money for them. Why? Most business people know very little about what we do and how it impacts their business. They see maintenance as a repair shop. We fix what breaks. And they know little, or nothing, more. They may know that maintenance represents a significant cost, and they may even know that they can’t get away with cutting it too much. But they do not know the full value of what maintenance can deliver, nor what it takes to deliver it. If you want to make improvements you need a decision-maker, someone with executive-level authority, to back you up. To get that, you will need to explain what value you can deliver, and in terms they can understand. You will need to show them the savings that are possible from doing things more efficiently, and the added revenues that can arise from investment in defining the right work. You will also need to show them how their support is needed to bring operators and the supply chain into the team with you to make those changes happen so that benefits are fully realized. Quantifying value and being simple in how you say it matters.

Presentation highlights the importance and interdependency of three pillars of success (people, processes, and technology) and Cameco’s asset management improvement efforts through each of these, including lessons learned. Audience will learn about the importance of organizational change management, business process management and agile methodologies, some of the technologies supporting asset maintenance and reliability, and its new Asset Management & Reliability Center of Excellence.

Within the mining industry, the library of KPIs has not kept up with data that has become increasingly available through digitization, therefore leading to an overuse of lagging performance indicators. Additionally, due to the fact that strategic corporate goals are oftentimes built on the basis of soft or perceptual measures from stakeholders, which are subjective in nature, organizations struggle with the process of linking strategic level goals to KPIs on the shop floor. Therefore, creating a communication gap between technical teams and senior management. As a consequence, maintenance leaders have a difficult time demonstrating the added value that maintenance activities create with an organization, leading to challenges in securing the resources required for things such as continuous improvement projects. Considering that the implementation of a robust KPI framework is an alignment of three key areas: people, process, and technology, the first portion of this presentation will be investigating a holistic approach to developing maintenance KPIs that are integrated into corporate strategic goals. Therefore, outlining the steps required for organizational leaders to begin standardizing data gathering procedures and creating a trustworthy system that can be leveraged for decision making. Aside from the previously discussed administrative challenges associated with data gathering within organizations, it must also be highlighted that KPIs are collected on a monthly or quarterly basis, meaning that training sets for forecasting models are extremely limited. Therefore, in order to address the reliance on lagging performance indicators within maintenance departments, a variety of time series models capable of achieving high accuracy on small datasets will be discussed along with direct business applications. Through achieving these goals, the intended outcome is to create a more contemporary data driven methodology for selecting organizational KPIs, as well as directly demonstrating the added value that the various business units create within the organization.

Traditional asset management historically prioritized infrastructure over the natural environment. This approach has led to many conflicts between the natural and built environment (e.g., flooding, pipeline exposures, property loss, etc.). As we respond to these conflicts and experience new weather patterns related to climate change, the design approaches of the past are transitioning to techniques that re-frame water and natural systems as valuable assets. Natural systems provide value to communities through the provision of ecosystem services. The realm of ecosystem services is diverse and includes flood mitigation, mental well-being, wildlife habitat, and pollution control. Understanding these services and systems allows proponents to manage and maximize the value of their natural assets. This presentation will describe two types of approach for managing natural systems as assets. The natural system type used for this discussion will be surface water drainage systems (creeks, channels, rivers, or ditches). The first approach to management is using proactive planning around natural systems to prevent conflicts between watercourses and infrastructure from occurring in the future. The second approach is used to manage existing conflicts in and around watercourses when built infrastructure cannot be relocated according to the recommended planning measures. In this case, existing conflicts can be resolved through applying knowledge of natural system function and design. Examples will be used to explore how these approaches benefit communities and project proponents alike.

Organizations are observing the change in the maintenance landscape with the use of data-driven analytics for decision-making. Underpinning these analytics is Machine Learning. The algorithms form the model that ingests data to represent the system and predict its future state. While this method has found rapid applicability in other sectors, the field of Reliability & Maintenance Engineering is still exploring ways to adapt this idea to its conventional Asset Management programs. The objective of this paper is to explain the predictive power of machine learning by wrapping it around a prevalent reliability tool: the P-F Curve. Initially proposed in the Reliability-Centered Maintenance (RCM) framework, the P-F Curve is ubiquitous and the practitioners understand its simple and elegant description of the failure behavior. In spite of its understanding, the use of the P-F Curve has been minimal in everyday analysis to estimate when and how soon the failure will occur. Predictive Maintenance (PdM) Tools such as Vibration Analysis, Ultrasound, and Thermography have made the P-F Curve more accessible. Adding Machine Learning to these PdM tools, with a real-time data stream, will amplify the value of this analysis with better detection of Potential Failure (Pf) and forewarning of Functional Failure (Ff). Having real-time data and a real-time P-F Curve plot will enable the users to capture the changing conditions. We define this new curve as the Dynamic P-F Curve™. Dynamic P-F Curve™ Machine Learning models will estimate the time available (P- F Interval) for the maintenance team to respond to an asset before catastrophic failure. This interval will change if the asset experiences an external force causing it to wear out sooner. Thus, a dynamic curve makes the maintenance plan itself changeable and agile, improving the plan's efficiency. The final part of the presentation will showcase an example of how a Dynamic P-F Curve™ is calculated and represented by using an open-source data set. The resulting change in the maintenance plan actions is also prescribed to fully explain this idea, concluding with the list of use cases.    

Asset Managers understand that the best way to achieve success with their strategy is to have executive sponsor support. Unfortunately, many Asset Managers are frustrated because they cannot get senior leadership to pay attention to the importance of the company's assets. Asset management goals and objectives should align with corporate strategies, but often, there is a disconnect. Some of the gaps are: •    Corporate strategy vs. asset management strategy; do you have a line of sight?•    What Asset Managers say vs. what executives hear; are you speaking the same language? •    Operations production targets vs. asset lifecycle value; do these conflict?•    Executive education; can you help senior leaders who don't know the difference between their assets and their asses?•    Executive decisions; are you in the loop on this secret process? •    Corporate performance vs. asset management measures; do your KPIs align?•    Managing up; are you providing executives solutions or problems?Understanding what executives are thinking and how they make decisions is crucial to an Asset Manager's success. Ron Bettin has spent many years guiding operational and project success by bridging the gap between executives and asset management. This presentation explores why executives often don't appear to care about critical assets and what Asset Managers can do about it