Shared Learning Library
Welcome to PEMAC's Shared Learning Library, a growing body of community created knowledge, built up and maintained by the PEMAC member community. Explore a range of articles, presentations and webcasts covering a wide range of maintenance, reliability and asset management subject areas. You can even find presentations from past MainTrain conferences and PEMAC Lunch & Learn webcasts.
To easily find what you are looking for the content of the Shared Learning Library can be filtered by both Maintenance Management and Asset Management subject areas using the options in the menu to the left of the screen.
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BoK Content Type:Presentation SlidesWebcastPresentation PaperBoK Content Source:MainTrain 2019Original date:Sunday, March 8, 2020As 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!
Democratizing Predictive Maintenance through the Industrial Internet of ThingsBoK Content Type:Presentation SlidesPresentation PaperBoK Content Source:MainTrain 2018Original date:Wednesday, February 28, 2018With all the talk about big data and the IIoT, many are asking how can we use this in maintenance? The IIoT enables us to put sensors in any location where we might want to collect and analyze equipment condition and performance data. There are companies that offer predictive maintenance services, and some companies do this for themselves, in-house. Typically, it’s the larger companies that can afford this, but democratization has meant this has become available to a much broader market. But there are hurdles to taking advantage of this sort of continuous monitoring program, even for your most critical equipment. One, it’s expensive, whether you do it in-house or outsource. And two, there are data bottlenecks. Condition monitoring data comes is huge volumes and it’s all time-sensitive. Even if you can afford it, you need a data handling network with a lot of capacity. In this workshop, we’ll present a viable technical solution to the data bottleneck problem — based on a solution already proven in financial securities markets — that opens up these possibilities in the realm of plant continuous condition monitoring.