4.4 Preventive Maintenance

This presentation is a case study on the Maintenance Journey for the Simcoe Muskoka Catholic District School Board guiding participants through the transformation of the school board's maintenance department which originally consisted of an informal program focussed on breakdown maintenance and reactive work, and to the current state of a formal maintenance program.The journey includes leveraging resources to baseline maturity and establish focus areas for evolving practices and adding structure. I will speak about specific initiatives completed and are in process which have established the organisation's programs, engagement, clarity, and purpose. It's a good news story which will also share how the tools and practices inherently part of the PEMAC community and body of knowledge have assisted in modernizing this team and organisation.

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?

SIGGA Technologies deployment with Votorantim Cimentos SAP PM

Shutdown is a generic term for what may be called an outage, turnaround (TAR), or overhaul. Running a shutdown well has always been key to the financial contribution of a business. However, something important has changed. The post-pandemic reality is that turnover and extended vacancies are eroding established maintenance processes that now struggle to yield the required plant availability. One tactic to mitigate this is to make the process “turnover-proof.” This is achieved through disciplined execution of documented processes backed by role definitions, training material, cross-training, and training the trainers, etc. The good news is, that tactic is entirely within the control of the maintenance area to create. This presentation proposes a framework to manage plant shutdowns in the mining industry and discusses four case studies, one of which is currently ongoing. The case studies are selected to show application over a range of different size shutdowns, from micro to mini to macro. Shutdowns vary immensely in nature, scope, and duration but all have four common objectives: do no harm (safety and environment); reliability restoration, (i.e., plant runs reliably until the next planned outage); schedule compliance—complete the work within the planned shutdown duration; and shutdown budget compliance. Like any other work, executing a shutdown is a process. It can be thought of as a combination of the weekly maintenance work cycle and a project, as it has elements from both, and any work process can be optimized. Shutdowns have two main loss drivers that impact production: shutdown duration, including schedule overruns; and unplanned outages, post-shutdown, due to work not done or work poorly done during the shutdown. While this presentation focuses on the management of shutdowns to control schedule and restore reliability, it should be noted that these improvements in execution will also benefit safety and cost.

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.

Reliability and maintenance teams in all manufacturing plants have a common goal; that is, to plan and execute initiatives that will help sustain the inherent reliability of the physical assets and increase the availability of the plant. Productivity and profitability of the manufacturing plant and overall organization are highly dependent on the reliability and availability of the plant. A thorough understanding of the importance of reliability has made the top management of major corporations invest in reliability and physical asset management. When the top management invests in reliability, they typically set the corporate strategy and directions for reliability through a road map for all the manufacturing plants that operate under the corporation. When the commitment or support from the top management is available for reliability initiatives, the onus is now on the individual manufacturing plant to develop a reliability plan that aligns with the corporate strategy and/or reliability road map and the current needs of the plant. The reliability and maintenance teams typically build all the strategic, tactical, and operational reliability plans that have initiatives that would make the biggest impact on continuous improvement in reliability and bring the desired benefits for the site. This presentation will explain the systems thinking approach, along with the seven golden rules and seven key factors, that will help reliability and maintenance teams to build an effective reliability plan. In addition, this presentation will also address the top three challenges in building a reliability plan and how to overcome those challenges through two case studies. The first case study is about developing a three-year reliability plan, and the second case study is about developing an annual reliability plan. Both the case studies will explain the application of the systems thinking approach, seven golden rules, seven key factors, and top three challenges that were dealt with and solved.

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.

We are in unprecedented times. Covid-19 wreaked havoc on supply chains; decreased production during times of increased demand. Labor shortages, chip shortages, long lead items turning into “maybe next year, if you’re lucky” items. The Russia Ukraine war added further stress to supply chains through sanctions, port closures, fuel shortages and much more. What once was reliable is now unreliable. So how can companies overcome an unreliable supply chain to maintain their reliability? There are several ways to mitigate unreliability; scenario planning, supplier management, and technology. There is no one size fits all and what may work for one company will not necessarily work for another. Scenario planning involves reviewing every potential situation that could occur, then working through to see how the company would be impacted. Ultimately this results in mitigation plans for each scenario. These can then be reviewed and implemented. Proper Supplier Management includes ensuring all suppliers have their scorecards reviewed on a regular basis. Their information updated and kept current. It can also include reviewing which suppliers can become substitutes for others in the event one is not able to provide the required product in time. Technology is important as it links all the information together. Algorithms can be created to let management know that certain parts are low, equipment is wearing out sooner, it also collects information on suppliers for the scorecards. Overall technology is the glue that binds and provides real time information updates. This presentation will review how to best use technology to help mitigate reliability and supply chain issues.

3D printing has important advantages over traditional manufacturing processes. However, as it is a relatively new class of manufacturing technologies, problems of reliability and parameter optimization remain largely unresolved. Our work focuses on addressing some of these issues through in-situ monitoring and closed-loop control, using machine learning as a tool for the endeavour. The idea is to analyze the condition of the process by predicting key characteristics of the final product, and then to use this analysis for adjusting process parameters on the fly. We imagine that this framework of predictive analysis leading to closed-loop control can be extended to a variety of applications outside of 3D printing. In a more general maintenance scenario, sensor readings can be used to assess the condition of equipment and to predict the condition at a future time. This information can then be used to determine appropriate maintenance activities, such as triggering preventive maintenance, scaling back on the intensity of use, and ordering replacement parts, as well as the timing of these events. For our case study in 3D printing, we have implemented in-situ monitoring hardware for a fused deposition modelling (FDM) printer and have constructed a dataset for modelling the process. The dataset consists of in-situ observations (photographs) and select mechanical property measurements for 359 fabricated parts. With this data, we demonstrate the ability of machine learning methods to capture the complex dynamics of a 3D printing process. Specifically, we train a neural network-based model which is able to predict mechanical properties of the final product based on in-situ photographs as well as parameter information. Predictions made by these models can then be used to assess the quality of products as they are being fabricated, thereby making it possible to correct errors or to improve the expected outcome through online parameter adjustments.

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.