03 Asset Strategy Management

A guide to pump maintenance and how advances in monitoring can enable predictive maintenance to be forecast up to 12 months in advance.             In this guide we explore 3 types of maintenance practice and how users can look to automate inspection, save time unnecessarily dismantling and inspecting pumps, and further explore in detail what features of remote monitoring users should look to utilize for accurate predictive pump maintenance.

How does your organization differentiate between asset management, and maintenance and reliability?

2012 PEM Magazine Maintenance Awardo Plant Engineering & Maintenance magazineo Best use of Technology/Maintenance Innovation categoryo Production Loss Tracing systemo Published March/April 2013 edition of PEM magazine

Asset management processes are becoming more widely adopted across industrial facilities. In this presentation, we’ll examine an essential aspect of the overall process—asset health—to understand the value of using data-centric models and how asset health enables fact-based decision-making at the asset and asset class levels across the enterprise. As a part of Intelligent Operations—a new approach to achieve Operational Excellence—asset health today uses digital transformation to optimize production, minimize equipment downtime, enhance human performance, and manage operational risks. We’ll examine the key asset-related aspects of Intelligent Operations and explore an asset health strategy based on the principles of interoperability and real-time decision support. Outcomes supported include reduced maintenance costs, enhanced asset availability, changes to predictive repair and capital replacement strategies, improved production, and reduced risk.

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

In our increasingly digitized and networked environment, the expectations for excellence in asset management are ever growing. While an abundance of maintenance and sensor data have become available, companies must develop the proper application of the data in their maintenance strategies. In this presentation, we’ll discuss the potential of your operational and maintenance data in the context of asset management, and explore different machine learning (ML) algorithms and how they may be leveraged to unleash hidden patterns in your asset management strategies. We’ll introduce some foundational topics required for ML, such as the taxonomy and data preparation steps critical to all ML approaches, the probability and statistics supporting ML, and how the evaluation of the quality of our models. C-MORE has actively applied machine ML methods to interesting real-world problems, such as the categorization of power generation units according to reliability characteristics, and anomaly detection in linear assets to optimize required maintenance actions. We’ll share a few of our case studies so participants can experience how ML methods can be used in maintenance, reliability and operations.

With many people at home either due to the closure of their operations, self-isolation protocols, or as a proactive measure to reduce non-essential staff on site, some might question how these individuals can be productive, particularly when assets are not operating. However, if employees have access to their CMMS/EAM/ERP data systems from home, here are some value-added activities that employees and employers should consider undertaking given the time they now have. Note these are in no particular order as priorities would be context-specific, and specific procedures are omitted for this same reason. 

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.