Digitalisation |


Predict, prevent, and perform


Asset performance management solutions that incorporate artificial intelligence can now analyse vast datasets from sensors and control systems to predict water and dam asset


failures, optimise maintenance schedules and improve decision-making, as Stacey Jones, Global APM Portfolio Leader Energy Industries, Digital from ABB, explains


ASSET PERFORMANCE MANAGEMENT (APM). The term is now so ubiquitous across multiple industries, particularly those with a high-level value chain like the global water and dam sector, it is easy to forget what it is, why it is critical to modern industrial operations – and how rapidly it is evolving. Despite the shift towards more proactive maintenance


strategies, businesses still face unforeseen errors and mishaps that impact operations and safety. Current volatile economic conditions have limited funding and reduced onsite staff, increasing the need for remote access, automation and efficient risk management. There is also the need to generate more value from existing assets1


.


For water utility managers, dam operators, infrastructure engineers and asset managers, this means avoiding costly downtime and environmental risks, as well as managing increased maintenance and repair costs due to ageing infrastructure; growing regulatory pressures; environmental factors like water scarcity and climate change; and higher energy costs. In this article, we will discuss how AI technologies such as machine learning (ML), predictive analytics and digital twins are being integrated into APM to address these issues, improving the reliability and longevity of assets like turbines, pumps and control systems, and reducing the environmental impact of water and dam operations through optimized resource management and energy consumption.


What is asset performance


management? Put simply, APM is a strategic approach to managing and optimizing assets, helping companies from all


industry sectors to predict process failures in real time. This improves the reliability, availability and maintainability of their critical equipment through predictive rather than reactive maintenance, enabling them to hit production, safety and sustainability KPIs with a higher degree of confidence2


. Fine. However, the current industry standard for


small-to-medium-sized rotating equipment is little to no condition monitoring (CM). Once the values are considered to be within ‘normal’ range3


, what little


information has been gathered is often discarded – meaning industrial operators are getting rid of their most precious asset, data, which can be used to drive uptime, production and profitability. Things are changing, however. The fall in the cost of


wireless technology using connection protocols like Bluetooth or WirelessHart means CAPEX is no longer prohibitively high, meaning the ability to monitor assets across the value chain 24/7 is a cost-effective alternative to manual, infrequent CM4


.


What is more, with data-driven APM for rotating, electrical and instrumentation assets, integrating remote and wireless CM on the Edge into the existing OT landscape is seamless. Once connected, the data can then be analysed, with outputs/alarms made available via email or common dashboards, on- premises or in the cloud5


. AI and digital twins: the advent of


APM 4.0 Hard to believe now, but just a few decades ago the majority of APM solutions were ‘run-to-failure reactive’. Equipment had to be shut down at short notice for unplanned maintenance, which is four to ten times more expensive6


than using quantitative risk analysis


and the current state of machine health to prioritize maintenance, and also avoids both unscheduled downtime and safety incidents. The next evolution was usage or time-based


maintenance, in which a schedule was used to assess when equipment was about to fail, so it could be fixed ahead of time. A drawback of this approach was that it treated/treats all assets as the same, rather than identifying those critical to production and created unmanageable maintenance backlogs. That’s when APM took a major step forward into risk-based maintenance, using failure modes and effects analysis and reliability-centred maintenance to prioritize critical assets. This constituted a vast improvement; however, maintenance was (and still is) based on how an asset has behaved (i.e. failed) in the


22 | November 2024 | www.waterpowermagazine.com


Page 1  |  Page 2  |  Page 3  |  Page 4  |  Page 5  |  Page 6  |  Page 7  |  Page 8  |  Page 9  |  Page 10  |  Page 11  |  Page 12  |  Page 13  |  Page 14  |  Page 15  |  Page 16  |  Page 17  |  Page 18  |  Page 19  |  Page 20  |  Page 21  |  Page 22  |  Page 23  |  Page 24  |  Page 25  |  Page 26  |  Page 27  |  Page 28  |  Page 29  |  Page 30  |  Page 31  |  Page 32  |  Page 33  |  Page 34  |  Page 35  |  Page 36  |  Page 37  |  Page 38  |  Page 39  |  Page 40  |  Page 41  |  Page 42  |  Page 43  |  Page 44  |  Page 45