Asset health is the backbone of a reliable and efficient utility ecosystem. Maintaining the optimal asset health of vital infrastructure components and management plants ensures uninterrupted utility supply, minimizes the risk of sudden failures and extends the lifespan of costly equipment.
AI can leverage a utility’s data assets to dramatically improve asset health through data collection, analysis, and predictive and prescriptive analytics.
Historically, asset maintenance has been reactive, fixing equipment when it breaks, which is not the best option to prolong asset life. Utilities need to find new ways to move beyond reactive asset maintenance. The emergence of AI has provided a solution that enables utilities to adopt a proactive approach to asset health. With AI’s predictive capabilities, this approach better prepares utilities for future challenges, helps with evolving regulatory standards and empowers utilities to continue meeting the demands of growing populations.
Utilities have traditionally taken a reactive maintenance stance, characterized by a “fix-it-when-it-breaks” approach. For many decades, this made sense. When utility infrastructure was relatively new, the reactive mode was the most cost-effective strategy, as incidents were few and far between.
As a result, there has been little investment in predictive maintenance technology for years. But now – with global utility assets and infrastructure aging and urban populations booming – these systems are more prone to unexpected downtimes, inflated repair costs and compromised safety standards. It also places immense stress on maintenance teams and affected populations.
Today, the expense of reactive maintenance has become more significant. The unpredictability can strain budgets and resources, forcing companies into a perpetual cycle of costly emergency fixes. For example, North America’s estimated annual cost of water main breaks is approximately $2.6 billion. In another example from the U.S., approximately 3,000 utility poles catch fire yearly, costing an average of $25,000 each. This amounts to $75 million annually but does not include the cost of homes destroyed and, tragically, lives lost. In 2015, one such fire burned 149,241 acres.
Proactive maintenance is a strategic approach that can transform utilities’ operations. Instead of waiting for equipment to fail and dealing with costly downtime, proactive maintenance involves regular, planned servicing and inspections to catch potential issues before they become significant problems. With the rollout of 5G and advancements in AI, moving to proactive maintenance has become a reality.
This forward-thinking method extends the machinery’s lifespan, enhances efficiency and reduces unexpected repair costs. By investing in proactive maintenance, companies can ensure smoother operations, increase productivity and save significant resources in the long run. This, in turn, translates into substantial cost savings, improved service quality and environmental sustainability.
Proactive maintenance is a strategic approach that can transform utilities’ operations. Instead of waiting for equipment to fail and dealing with costly downtime, proactive maintenance involves regular, planned servicing and inspections to catch potential issues before they become significant problems. With the rollout of 5G and advancements in AI, moving to proactive maintenance has become a reality.
This forward-thinking method extends the machinery’s lifespan, enhances efficiency and reduces unexpected repair costs. By investing in proactive maintenance, companies can ensure smoother operations, increase productivity and save significant resources in the long run. This, in turn, translates into substantial cost savings, improved service quality and environmental sustainability.
Utilities are harnessing the power of advanced algorithms, machine learning and AI to sift through vast datasets collected from sensors and other monitoring tools embedded in the utility infrastructure. This allows for real-time system integrity analysis and establishes a predictive maintenance schedule to address issues before they become major problems.
Traditionally, some sections of utility infrastructure were challenging to monitor. 5G changed this; high-speed data transmission and IoT devices can reach far and wide into the infrastructure.
The result? Enhanced operational efficiency, cost savings and a more sustainable approach to managing utility assets. With AI’s capability to provide actionable insights, utilities can make more informed decisions, safeguard public health and contribute to environmental conservation.
Companies can accurately forecast asset failures by leveraging advanced algorithms. Combining current and historical data volumes, AI can find patterns that suggest pending equipment failure. This proactive approach not only minimizes downtime but also saves significant maintenance costs.
For example, AI-driven leak detection systems have revolutionized how industries manage and prevent costly water and gas leaks. Utilities like Thames Water in the UK have implemented AI to monitor thousands of miles of pipes, enabling early leak detection and significantly reducing water loss.
With predictive analytics, maintenance teams can identify potential failures and perform necessary repairs ahead of time instead of waiting for a machine to break down. This foresight transforms the maintenance strategy from reactive to proactive, ensuring smooth operations and higher productivity. Predictive analytics is not just a tool; it’s a game-changer that empowers businesses to stay one step ahead in managing public utilities.
Predictive and prescriptive analytics are two powerful tools in the AI arsenal, each serving a distinct purpose in data-driven decision-making. Predictive analytics forecasts future events based on analyzing historical data. For example, in the utility sector, predictive analytics can anticipate equipment failures by analyzing patterns in machine performance data, allowing for preemptive maintenance that reduces downtime and repair costs.
Prescriptive analytics goes further — predicting outcomes and recommending actions to achieve desired results. In the same utility scenario, prescriptive analytics could advise on the optimal maintenance schedule and resource allocation to maximize efficiency and minimize disruption. While predictive analytics answers “what might happen,” prescriptive analytics provides “what actions to take” to achieve the best possible outcome.
The power of prescriptive analytics takes proactive maintenance for asset management to another level. It allows utilities to advance to a proactive maintenance approach that reduces maintenance costs, extends the lifespan of critical assets and ensures operations run smoothly and efficiently. As demand for utilities increases, a proactive stance on maintenance will become imperative.
The rise of AI has brought unprecedented opportunities for innovation and efficiency. However, it has also raised concerns regarding the reliability of AI outputs, system and data security and ethical implications of AI.
Despite these challenges, the potential benefits of AI in enhancing asset reliability and reducing maintenance costs make it a compelling option for forward-thinking organizations. The key lies in carefully navigating these risks through robust data management practices, comprehensive security measures and continuous system evaluations.
One of the primary concerns around AI is the reliability of predictions, which can sometimes be skewed by insufficient or poor-quality data. This can lead to inaccurate forecasts and unexpected downtimes, disrupting operations.
The data landscape gets more complex as utilities add unstructured datasets to elevate infrastructure monitoring and maintenance. Data such as video and photos can be powerful in detecting issues.
Utilities should view all data as a strategic asset that improves outcomes and conforms to regulatory requirements. As such, following a data governance and cleansing plan is critical to make all data usable. This process can ensure that AI systems are fed with accurate and unbiased data, as flawed data can lead to skewed results and misguided decisions. It takes a lot of data to provide the most reliable results. Utilities must standardize all historical and new data in their cleansing process.
As AI algorithms become more integrated into our daily lives, safeguarding sensitive information from breaches is more critical than ever. AI systems can be vulnerable to cyber-attacks, potentially compromising sensitive data and operational integrity.
Fortunately, AI can help with data security. AI is highly proficient and accurate in detecting anomalies that could foretell a breach.
Beyond technical considerations, the ethical dimensions of AI must be considered. Questions around transparency, accountability and potential biases must be addressed to build trust and ensure that AI serves the greater good.
Companies should establish a framework that keeps humans in the loop, checking the outputs for accuracy and bias. By navigating these concerns thoughtfully, utilities can harness the full potential of AI while maintaining integrity and trustworthiness.
The average age of water pipes in the United States is 45. In Europe, the average age of electrical grid assets is 40. Aging infrastructure is more prone to failures, making proactive maintenance all the more critical. Utilities worldwide are starting to use AI with impressive results in proactive asset maintenance.
Utilities are heading for a perfect storm – aging assets and growing demand. Population growth will increase water demand by 20% to 50% by 2050. Trends like electric vehicle adoption and AI data center processing will put added pressure on the grid. The International Energy Agency (IEA) predicts that the demand for electricity in data centers alone will more than double from 2022 to 2026.
Old ways of managing assets and infrastructure will likely fail to keep up.
However, AI can scale for increased data collection and analysis, providing real-time information on asset and infrastructure health. Integrating artificial intelligence with its unparalleled speed and accuracy is a strategic necessity that provides previously unattainable insights for the utility sector.
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