6.05 Assets Performance & Health Monitoring

Do those that manage the performance, understand the measures?

Let us look at an ordinary omnipresent part of Asset Management: bolting and fastening the machinery or structure. This single vital activity turns out to be remarkably neglected with undesirable consequences for reliability. Motion Amplification®, a new technology has the capacity to show machinery and structural movements, and so naturally brings out looseness or unnatural behaviors. Fastening errors seem to be one of the most prevalent occurrences and it is therefore important to look at what we can learn and how the technology can drive improvements and uses of best practices. A variety of problems are shown, such as forgotten difficult to access bolts, rusted/corroded fasteners, improper tension, improper washers, bolt grade or size and custom modifications such as a bunch of small shims. Some of the issues can be attributed to design errors, some to lack of asset information provided, some to installation errors, lack of training and proper tools or a culture of installation and maintenance excellence. Sometimes and improvement push comes with the integration of more advanced tools, for example tools exist where the torque can be properly managed. This requires that many other items come into place, such as the missing information, correct parts, training and cultural to get the job done properly.

New technologies regularly enter the world of asset management (AM), often leveraging new inventions, which, in turn, are driven or supported by other advances such as computing power and big data handling. Such is the case for Motion Amplification, a new technology impacting crucial aspects of vibration analysis, machinery and structural troubleshooting, root cause analysis, and communications. This presentation will outline which areas of AM are impacted so we can have a road map facilitating the integration of the new tool into a global strategy and get an overall picture of the impacts it will have. We’ll also provide a brief technical introduction and some practical illustrations.Originally presented at MainTrain 2020

The asset hierarchy is often thought of as a way to organize assets so they’re easy to find in the CMMS. While a well-structured asset hierarchy does make work management easier, it’s much more than that. The asset hierarchy, when well conceived and utilized, will ensure the right reliability and costing data can be extracted from the CMMS. This enables more than just micro improvements in reliability involving a single asset; instead, it enables macro views of reliability and cost trends across the entire organization. Setting up an asset hierarchy to support these types of activities requires forethought and planning, but by following some guidelines, any organization can be set up for success. First, the asset hierarchy must have a standard that identifies how all assets will be categorized and described, and the specific data required for each asset class. This is vital, as not all assets warrant the collection of specific data, reducing the burden of the setting of the hierarchy. As assets are categorized, the failure code library can be developed and linked to the specific asset classes. This ensures only relevant failure codes are displayed for the assets, improving the adoption of failure data collection. With the asset hierarchy built and relevant failure data collected, trends can be established across asset classes, similar processes, etc. The trends enable reliability improvements to be implemented across larger swaths of assets, providing rapid improvements in reliability. This presentation will provide guidance in how to develop an effective asset hierarchy based on ISO 14224, how to implement the changes in the CMMS, and finally how to leverage the asset hierarchy to identify macro trends. Without a proper asset hierarchy, any organization will struggle to get meaningful and actionable data from their CMMS to drive reliability.

As the influence of the asset management approach continues to expand within Nova Scotia Power, we need a structured approach to ensure we continue to seek opportunities to optimize maintenance strategies. In a new installation, techniques such as failure modes and effects analysis (FMEA) and reliability centred maintenance (RCM) can be used to develop an optimized maintenance strategy from the start, in a top-down approach. However, the vast majority of Nova Scotia Power’s equipment was in place long before the asset management office—and, therefore, the asset management approach—existed. The result of that is a collection of value-added, but developed after-the-fact maintenance strategies. Each maintenance strategy has components of operator surveillance (rounds), testing, predictive pattern recognition (also known as advanced pattern recognition, APR), predictive maintenance (condition-based monitoring and risk-based inspections), online monitoring, and preventative maintenance. While efforts had been made to “baseline” the equipment processes when maintenance strategies were developed (i.e., “clean out” existing activities), the organic growth of the approach and the distributed nature of assets and personnel have made this difficult to maintain. Therefore, we needed an approach to optimize existing maintenance strategies, without recreating them. Nova Scotia Power has therefore undertaken an effort known as maintenance strategy optimization, and has made this activity a core accountability for the asset management team, which recognizes the need to seek continuous improvement (vs. a one-time exercise). With a focus on digitization wherever appropriate, Nova Scotia Power has asked a number of questions to streamline, standardize, and optimize its maintenance strategies. Is there opportunity to reduce PM frequency? Is there opportunity to collect more information such that we can strengthen our APR models? Can our in-house standards be revalidated to sustainably reduce operating and maintenance costs? Nova Scotia Power is answering yes to these questions, and more, and pursuing opportunities to optimize its maintenance strategies—from the bottom up! 

Many organizations 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 decision-making or rely on technology to guide their basis. In many cases, however, these same organizations approach a gap in tactical deployment and in the ability to draw a connection to the follow-up or pre-emptive actions required to derive value from assets. We'll review the processes for establishing a framework for alignment and priority setting while looking at the techniques used for resiliency and risk management using a technology-agnostic approach. We'll review potential data sources that can be leveraged for decision-making and can reflect the needs and current state of the business environment. Additionally, we'll discuss the relationship and application to the decision-making process.  

One major challenge at the operate and maintain phase of an asset is achieving and sustaining the forecasted availability and reliability as intended at the project delivery phase. Many problems arise—equipment failures, underperformance, high costs—that are caused by numerous issues. The resolution demands thorough understanding of the causes of the issues, which we usually attempt to achieve through RCA methodologies. I've experienced many repeated failures even when RCAs have been conducted, due, mainly, to most of the RCAs focusing attention on solutions to the problem outcomes with limited focus on the human and system causes that drive the outcomes. The Causal Learning Approach brings in the understanding of these other causes that ensure effective and sustainable solutions development. There are three levels of causes: the physical outcomes; the human causes; and the system causes. The Causal Learning Approach also focuses on causal reasoning instead of defensive and solution reasoning. This presentation will provide the understanding of these causes and the three key elements of this approach: discovery, learning, and solution generation.

The U.K. railway network dates back to 1825 and is the oldest railway in the world. Several electrical assets on the network such as track power cables, switchgears, overhead line isolators, circuit breakers, and insulators are beyond their design life and the business must decide whether to renew or replace them—even though they're still operating at the optimum performance level. These assets are still being maintained at the original regimes; the challenge to the business is to understand the degradation models and change them to achieve different maintenance regimes for the aging assets. The work we're currently undertaking is intended to influence and change our asset policies—in particular, the assignment of asset regimes for assets that remain in service at the end of their design life and beyond. The philosophy behind the maintenance regimes is that they're based on degradation models, which are algorithms that consider various factors such as the environment, the loading, the utilization, the reliability, and the cost for interventions. The approach we pursued was to review the parameters of the degradation models for their “fit,” based on the knowledge asset managers have gained on the ground and through large volumes of asset data. The asset data was analyzed with data visualization software to gain further insight to influence the review of the degradation models. The findings of the work are summarized here: asset population is aging and future renewals bow wave are predicted; asset policy pushes all assets to maximum asset technical life and fix-on or run-to failure; safety-related works prioritized over asset performance/resilience; there's a need to modify some factors associated with the degradation models to cater for extension of technical asset life and maintain a more realistic/sustainable asset renewal profile; composite asset condition scores are required to manage bow wave of asset renewals and implement sustainable obsolescence management techniques (this is predominantly driven by organizational investment decisions where enhancements are the main driver of asset acquisition, making future renewals difficult due to the requirement to renew similar age assets at the same time); and determination of useful asset life required for assets that are being left in service longer than their originally predicted life.

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.