6.05 Assets Performance & Health Monitoring

The structural configuration of an organizational design is the way work is divided and how it achieves co-ordination among its various work activities around the assets’ lifecycles. An organizational design structure resolves two basic tasks to get work done: dividing up the work into logical units, which enables performance management, and ensuring the work gets done by providing the co-ordination and control of work. In this webcast we’ll look at four models and discuss their advantages and disadvantages and present suitable information on typical roles and responsibilities that will be reflective of the selected model. The goal of asset management (AM) is to ensure that an organization’s staff is always working on the right activities at the right time, for the right reason, and for right cost. The AM governance model is intended to ensure there is effective collaboration and co-ordination to make this happen around all business processes. With the right AM governance model, overall AM program development can be expedited and new ways of working can be quickly integrated into the organization’s AM culture. We’ll provide the actual results from a number of case studies to demonstrate the value of designing and implementing the most appropriate AM governance model for your organization.

Most organisations have implemented processes and tools to collect data to facilitate informed decision-making. Often, they will seek out best practices and measures to assist in decisionmaking or rely on technology to guide the basis of decisions. However, in many cases these same organisations approach a gap in tactical deployment and the ability to draw a connection to the follow-up or pre-emptive actions required to derive value from assets. This presentation will review the processes for establishing a framework for alignment and priority setting, while looking at the techniques employed for resiliency and risk management using a technology agnostic approach. We will review potential data sources which can be leveraged for decision-making and which reflect the needs and current state of the business environment. Further, we will discuss the relationship and application to the decision-making process. An overview of the fundamental outcome of key performance indicators and visualized metrics will be demonstrated. Finally, we will investigate the influence on decision making and the level of data confidence.

There are many new lubricants, bearings, seals, and lube accessories, but we need to do better. Ninety percent of rolling element bearings don’t reach their design life, and the main contributing factors relate to lubrication. This can mean the wrong type, too much, too little, not often enough, or not applied right. Generally, such things can be easily corrected, but a learning, productive working environment is key. Similar to hydraulics, the leading cause of equipment issues is contamination. This can be water, dirt, and/or wear. In this presentation, we’ll give you a number of examples and study results, as well as present some solutions.

The tenets of reliability can be fun and messy. In this workshop, we’ll help you better understand these concepts and where the confusion creeps in. This session will be great for those studying for an exam or for those who want to geek out. You’ll leave with new knowledge, interesting facts, and explainable models that you can take back to your facility. We’ll look at turbo implementations; the criticality of criticality (both of them); the P-F interval; the funky failure curves of RCM; why root cause analysis is a lie; and why predictive tools can’t predict. Bring your own confusions and a willingness to participate in the dialogue as we break each of these down and toss out a few lies.

Asset condition management (ACM) teaches on-condition monitoring for any business with high-capital assets looking to harness machine learning to avoid unexpected failures and control rising equipment maintenance costs. Many businesses are already using continuous condition monitoring technologies like IoT-connected devices. However, beyond simple threshold alerts from condition sensors, extracting real value from the data generated by these sensors for true predictive monitoring requires expert analysis and interpretation. To generate actionable results from condition sensor data, these experts also apply knowledge about the asset’s operation. This limits the value that IoT-enabled ACM can provide to the business. By taking the next step and using advanced algorithms and machine learning to automatically extract real-time insights that drive action, we can now achieve the full potential of ACM. Modern, cognitive online ACM takes data from multiple and varied sources, combines it, and uses AI and machine learning techniques to anticipate equipment failure before it happens. Many reliability professionals recognize the potential of IoT, machine learning, and AI, and are trying to learn these technologies. However, the available training is complex and assumes learners have a background in data science and computer programming. This workshop will provide a beginners’ level understanding of terminology, basic concepts, and techniques to determine how and where you can apply AI in your facilities for meaningful ACM.

This webcast will highlight Potash’s extensive implementation of mobile devices to support its business processes. Aligned Mobile Applications are now in use or being implemented at Potash’s Allan, Augusta, Aurora, Geismar, Lanigan, Lima, Rocanville & Trinidad sites. Potash has partnered with Viziya to develop a single integrated mobile app to meet its maintenance and supply chain business requirements, and Postash continues to deploy ‘out of the box’ apps from its Enterprise Resource Planning (ERP) system. Vendor mobile devices are now a commodity which provide a cost effective way to drive efficiencies. Importantly, apps are available across various platforms; hardware choices do not drive decision making when it comes to selecting the best tools for our business. If you are thinking about implementing a shift to mobile devices on the front lines, this will be a great opportunity to learn from the Potash experience.   Reviewer's comments;  Excellent presentation outlining how Potash Corporation of Saskatchewan has deployed a combination of technologies, enabled on mobile devices (tablets / laptops) integrated fully with their EAM and KPI monitoring systems. Author provides an overview of the situation "before" deployment, through the deployment (which took place over several years) to the "after" or current state. If you want to know what can be done and has been done, this is pretty leading edge stuff and well worth the time to listen.

Jeffboat is a company with a long history.  Originally named the Howard Steamboat Company, Jeffboat is America’s largest inland ship builder and has been manufacturing ships for over 100 years.  Jeffboat has built such famous ships as the Mississippi Queen, the General Jackson showboat and the Casino Aztar riverboat casino. Like most manufacturing firms, Jeffboat has an enormous amount of equipment stretched out over a shipyard that is over a mile in length that is needed to make its boats.  Also like many old-line manufacturing firms, Jeffboat has both equipment and employees who have been there for several decades. Overall, because of the size of the shipyard and age of the equipment, Jeffboat’s maintenance was used to working in reactive mode.  There was no CMMS software in place and equipment was put into numerous Excel spreadsheets.  In addition, it was often hit or miss whether the right parts were in the stores room and finding the right equipment often took maintenance technicians a significant amount of time.  There was no Scheduler/Planner and maintenance procedures were done informally and based on need at that particular moment.When implementing a maintenance management strategy, a critical component is the resistance to change. Whether it is the introduction of new software or a complete overhaul of the maintenance function, the process of change represents disruptive technology (Christenson, …). According to Christenson, most changes are really improvements on something old and the old paradigms can be used. However, there are changes that organizations need to make that disrupt the dominant paradigm, rather than sustaining it. These are disruptive technologies and make the old things less important or obsolete. The problem with these disruptive changes is that people are still applying the old paradigms to the new realities. They are trying, in a sense, to understand the car as nothing more than a carriage without horses.

Due largely to the release of ISO55000x:2014 family of standards, Asset Management is gaining worldwide acceptance as a valid business practice for asset-intensive organizations. The challenge that organizations now face is how to operationalize the principles and move it from “being understood in theory” to being “the way that we work”, to truly distill effective asset management practices and principles to the nooks and crannies of the organization. One key tenet of ISO55000x is the management of asset risk at all levels of asset interaction. On the other side, one area that has been struggling to understand asset management beyond maintenance management is the traditional Maintenance Department. This paper will capture the steps that Veolia North America is taking one of its Municipal Clients through to understand risk at the more granular levels and build risk resilience into its maintenance strategy.Yet for the average Maintenance Manager, the challenge of interpreting asset risk for the organization is still uncharted waters. There are several ways in which the traditional Maintenance Manager can understand the wide breadth of risks facing the asset, determine appropriate responses and communicate them to the appropriate stakeholders. In fact, one or more of these may already be in place in the organization but may not be seen as building risk resilience. This presentation will explore one methodology used by Veolia to develop an asset-centric, risk-based Maintenance Strategy at the City of Winnipeg’s, Waste Water Treatment Plants using a Maintenance Management Maturity Assessment.The City of Winnipeg’s Waste Water Department is at a very interesting juncture in its history, in that there are several major capital upgrades being undertaken, whilst the plants continue to run. The goal of the Maintenance Strategy is therefore two-fold. To maintain the existing levels of service at least whole life cost with risk balanced against the cost of meeting objectives, whilst ensuring that there is a plan to maximise maintenance for the future asset base to realise the benefit of the investment over the whole life of the assets. As a result, in 2016, in collaboration with its selected O&M improvement partner, Veolia North America, the City of Winnipeg’s Waste Water Treatment Plants, went on a path of discovery. Two significant tools of investigation were employed: 1. An Asset Management Maturity Assessment was conducted and 2. The City participated in the National Waste Water Benchmarking Initiative (NWWBI) Maintenance Task Force Survey implemented by AECOM. The Asset Management Maturity Assessment examined 8 fundamental areas of Maintenance Management and outlined positions of excellence that the City hoped to achieve both at the 1-year and 3-year mark from the date of assessment with 2017 being Year 1. The NWWBI Maintenance Task Force Survey examined 42 granular yet, over-lapping areas of Maintenance Management, with 18 of them reporting significant gaps for the City’s Waste Water Treatment Plants. The results of the two analyses were combined into eight (8) key Objectives and the underlying activities required to achieving them over the next three (3) years. These eight (8) Objectives are: 1. Implementation of Asset Condition Assessment Plan (ACAP) 2. Inventory Management Optimization Plan (IMOP) 3. Work Organization Improvement Plan (WOIP) 4. Implementation of Maintenance Quality Strategy (MQS) 5. Financial Capability Improvement Plan (FCIP) 6. Asset Registry Improvement Plan (ARIP) 7. Implementation of Document Management (DM) 8. Revision and Implementation of Asset Criticality Model (ACM)This presentation will examine the detailed plans for each objective, the inter-connectivity and alignment of the Objectives, the Road Map for the next 3 years, the processes for monitoring and continual improvement and the benefits of implementing this approach. Presented at MainTrain 2017 

This presentation covers the journey at ENMAX Generation of establishing foundations for Asset Management Plans and Lifecycle Asset Management Practices incorporating into the Capital Planning Portfolio Optimization and Budgeting Process. One of the challenges that we are facing today is in bridging the results from  many technology sources occurring at different time continuum into actionable information that can be consistently used across the fleet. This initiative is driving our engineers and consultants to devise a Health Index (HI) for critical assets that can be consistently used across the fleet for similar assets for the prioritization of capital projects.Annual spending on new projects, major maintenance, and sustaining capital require careful consideration, which has led to an increased scrutiny at ENMAX Generation. A data driven and financial model-based decision-making process for Capital Planning and Portfolio Optimization can be significantly improved using asset analytics to provide meaningful insights.The implementation of this involved review of existing business process including current and future state mapping, gap analyses, alignment with Project Management Office (PMO) Stage Gate Process and with Authorization for Expenditure (AFE). It also included a redesign of value measures and modeling to appropriately value projects/investment opportunities. We developed preliminary Health Index based on asset condition, operating age, probability of failure curves, replacement costs/parameters, and consequence of failure and risk levels. This journey has utilized practices by ISO 55000 for data-driven decision making and Value Measures and Value Frameworks in the Capital Planning and Budgeting Process. The results are probabilistic “optimal” replacement dates. We use Reliability Centered Maintenance methodology to manage our plant physical assets. One of the challenges faced today is in integrating technology sources, which is driving our engineers and consultants to devise a Health Index (HI) for critical assets, starting with the high-value assets.In conclusion, a key element of effective data and model-based decision making in Capital Investment and Management Planning relies heavily on predictive asset analytics. For asset analytics to effectively work, we require a lot of meaningful data to populate newly enhanced Capital Budgeting Software (C55). These are used today in C55 to compute the optimal replacement dates.Presented at MainTrain 2017 

As I spend more and more time in and around maintenance, reliability and asset management professionals, and though my own experiences as both an end user and now a contractor, it has become more and more clear that there is a definitive gap in most maintenance and reliability plans....the electrical system. This is not to say that there is not maintenance being done, or that people are not recognizing that their electrical system is critical. But do you understand what you are doing? Do you understand why? Is what is being done correct? Is the budget that is set aside for electrical adequate or too much? How do you know? What are the best practices and where do you start? As discussed this is not a technical presentation but rather a look at a basic electrical system and where an end user can start in regards to assuring themselves that they are doing the right things. There are some new technologies that are in the market place that can assist in determining if there is a potential problem with parts of your system...this presentation is not about those. Alternatively it is about "the basics", learning to walk before you can run: Looking at the system as a whole and learning where most trouble areas are; Assisting end-users in looking at past test results and planning next steps; Determining what needs to be done based on predictive tests such as transformer oil samples or IR scans, and what can be pushed into next year’s budget; What cannot be skipped because, if it is, it may not only cause catastrophic plant failures but potential fatalities. In conclusion what this presentation will focus on is assisting Maintenance Management professionals to treat their electrical assets with the same care that they keep their mechanical assets. It is not overly technical and you do not have to be an electrical professional to understand or benefit.Presented at MainTrain 2017