6.06 Asset Management System Monitoring

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.

Recent developments in AI, ML and related techniques see wide adoption in many industries. However, in the asset management area, such technical advances are still in their infancy, especially in the maintenance, repair, and operations (MRO) area. Part of the reason is that, contrary to production, MRO data has its unique characteristics (e.g. incompleteness, inconsistency and heterogeneous), and most organizations are still planning to introduce diagnostic sensors dedicated to maintenance and equipment reliability. We face both challenges and opportunities in advancing data-driven continuous improvements within the asset management world. This presentation shares the findings of our current research and development focus. Titled “How to use historical data to find opportunities to improve the effectiveness of equipment reliability programs, optimize MRO inventory operations, and enhance MRO workflow management processes”, we will first examine the characteristics of MRO data as their uniqueness to a specific company, plant or equipment and their commonality across all sectors. Then we evaluate the feasibility of applying AI/ML techniques with MRO history for better operational efficiencies. We need to understand what data is related to human knowledge, human interaction and process, and what data is associated with the actual condition of the asset, and if there are patterns and models that can be learned. Last, we will demonstrate that AI/ML can find equipment agnostic models and patterns which help continuously improve MRO operations across different industries. Based on the findings, we will also show how AI/ML models learned from historical MRO data can be translated into prescribed actions for improvements in equipment reliability, MRO inventory and workflow operations for individual organizations.

The energy landscape is shifting with the rise in electrification of transit and the rise of renewable energy shaping a new energy era that is changing the way we think about infrastructure decision making. This presentation will articulate how electrification of transit and an increase in renewables will impact medium and long-term infrastructure planning by providing examples and a practical perspective (case study) to demonstrate how Asset Management decision-making played a vital role in a utility company’s response to this change. This utility company is a key contributor to several electrification initiatives. They recognized the challenge associated with these initiatives and the overall success of the first implementation phase with minimal disruption to current operations. They are also preparing for electrification of the government transit’s first all-electric bus garage to support future procurements of battery-electric buses (eBuses) and will be working on the design and implementation of charging systems infrastructure across the city(?). Over the past 20 years, more than 50 renewable energy systems have been installed on City buildings and properties. In 2020, the city developed recommendations for the utility to achieve greater outcomes for energy efficiency, demand management, and renewable energy. The city also mandated installation of renewable energy systems on all buildings, where feasible, by 2020. The rate of development in electrification and technology in the transit sector is faster than implementation of major infrastructure developments; changes in demand patterns impact everything from the transmission and distribution networks to generation, dispatch and peak-load system capacity design; so it is not possible to “wait and see” before committing to infrastructure investment decisions. This presentation will cover how the utility is dealing with these changes by ensuring an appropriate long-term decision-making framework is in place to assure business continuity and reduce the impact on climate because it poses a particular risk for asset owners and operators. AMCL will present best practices for long-term decision-making and how the impact of change should be taken into account during the development of long-term infrastructure planning processes, in the context of a public utility.

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.

Asset Criticality is an important input to production system design, maintenance strategy definition and short term work execution management processes. The value the supporting FEMA exercises provide in determining these categorizations is well understood in the Reliability Community. Less common is the extension of this analytical rigor to the spare parts required to maintain equipment. Establishing and maintaining robust part criticality values can be an invaluable link between operations and the supporting supply chain, helping to set stocking strategies, inform alternative material management approaches and quickly flag when expediting is required. Despite the value, part criticality values (or Risk Priority Numbers) are rarely objectively derived and even less frequently maintained. This presentation is intended to: 1. Establish the link between asset health and spare part availability 2. Illustrate common item criticality practices 3. Provide an overview of a robust item criticality assessment approach 4. Highlight the benefits to be gained from an enhanced approach to item criticality determination.

Business improvements require changes. In reliability and maintenance, some of the change factors are within our control, but many are not. If we stick with small changes, they can often happen but they often fail to achieve their full potential. Why? The short answer is that there are many factors we can’t control and usually we have limited influence. Some of those are related to people and are dealt with by “change management”, but others are related to how our businesses are structured and organized. If we want to make big changes we need to get past that! This presentation will give you something to think about and share with your senior management. If they want miracles from you, then they will need to make it possible!

The ISO standard ‘ISO 55000 Asset Management’ was published in January 2014.This new standard provides a fresh focus upon the purpose and application of asset management within organisations. The significant interest in the standard shown by asset owners, regulators and certification bodies reflects a universal business desire to improve the value returned to organisations, through the effective management of physical assets.To realise that benefit, organizations require knowledgeable people capable of applying an asset management certification process who, at the same time, can use that process to provide the value that business desires.As a non-commercial, worldwide association of professional asset management bodies, the GFMAM has published a document that specifies the appropriate asset management knowledge for personnel who apply an audit or assessment process against the requirements of ‘ISO 55000 Asset Management’.The aim of that specification the ‘GFMAM ISO55001 Auditor Assessor Specification’ is to help asset owners identify people who are able to provide that value to businesses.Certification bodies likewise will benefit from the use of the Specification to select people with the GFMAM recognised asset management knowledge, providing surety that the added value that organisations seek can be realised.The Auditor Assessor Specification is provided free by the GFMAM.

Airports are an important economic hub for a region. They help regional economies extend their reach and often create a localized cluster of companies around it, termed “airport cities.” Indeed, airports face many of the same challenges that municipalities do. They have a portfolio of diverse infrastructure that supports a variety of services to customers in a challenging, dynamic environment. There are many stakeholders who have an interest in service levels, and revenues are often less than what is required to meet expectations. Edmonton International Airport (EIA) has implemented an asset management program that began with the successful implementation of a computerized maintenance management system to better understand its cost of service. It has also recently established an asset management strategy that systematically employs risk, strategic goals, and service priority for investment decision-making. This presentation will provide a case study of EIA's progress to date as well as future steps.

Metrics, best practices, more than 40 key elements to implement, challenges, and opportunities all combine to make a successful implementation difficult. Where do you start, and how do you know how to work on what matters? Once you understand how it’s all related, you can focus on the vital few to leverage the maximum ROI. This presentation will clarify the importance of culture and employee engagement, along with other key plant floor performance indicators that will be clarified with data. We'll look at the current state of R&M; what’s working and what's not; survival skills for the next decade; impacts of connected technologies (edge computing, big data, machine learning, AI, 3D printing, augmented reality); the importance of getting your data ready for what's coming next; and relationships between R&M and safety, people engagement, quality, throughput/uptime, and cost.

Quelle est l’opportunité pour les gens de maintenance dans l’Industrie 4.0 ?- Constat de la maturité de la maintenance au Québec- Rappel de vieux concepts d’ingénierie de maintenance- Survol des concepts de l’Internet des objets et de l’Industrie 4.0- Analyse de l’opportunité 4.0