As the world becomes increasingly reliant on critical infrastructure services, the importance of maintaining and improving the reliability, efficiency, and safety of these systems has never been more pressing. One key way in which utilities organisations can achieve this is through the adoption of artificial intelligence (AI)-driven predictive maintenance strategies.
Utilities companies are constantly looking to improve the reliability, efficiency, and safety of their critical infrastructure services. By proactively identifying and addressing potential issues, utilities companies can avoid costly outages and downtime, increase efficiency, and reduce the risk of accidents.
Historically this has been achieved through preventative maintenance; the proactive upkeep of equipment and systems before they fail, in order to prevent costly and disruptive outages. This is a manual process, with technicians physically inspecting and servicing equipment on a schedule. However, this approach is costly, time-consuming, and can lead to unexpected disruptions and outages. With the advent of AI, utilities companies can now leverage existing investments in sensor technology to monitor equipment in real-time, detecting and resolving potential issues before they become problematic.
How does AI-driven predictive maintenance work?
AI-driven predictive maintenance uses advanced machine learning algorithms and data analytics to predict when equipment is likely to fail. This allows organisations to carry out targeted, proactive maintenance interventions, rather than reacting to failures after the fact. The result is a more reliable, efficient, and safe delivery of critical infrastructure services to all citizens.
One of the key benefits of AI-driven predictive maintenance is that it can be implemented at minimal cost, leveraging existing sensor and operational technology assets
This approach is especially compelling in situations where plant and equipment are remote, costly or hazardous to inspect, and where failures have a high productivity impact. For example, a remote pumping station in a waste-water treatment facility can cause major disruption on failure, and can be challenging to rectify under emergency conditions. A number of our clients have further extended their AI models to encompass a digital twin representation of their operating environments, where they can explore scenario planning and determine optimal investment in process and equipment improvement. Given that digital twins are a virtual representation of an object or system, it enables operations staff to visualise the system lifecycle under different operating conditions and to plan for adverse events.
Barriers to predictive maintenance
Traditional barriers to entry for effective predictive maintenance have been the cost of implementation, reliability of results and access to suitable skill sets for enablement and ongoing development.
One of the key benefits of AI-driven predictive maintenance is that it can be implemented at minimal cost, leveraging existing sensor and operational technology assets. Data generated by these sensors can be fed into predictive models that can be run on inexpensive cloud computing, or locally on site via edge computing. The mechanisms for achieving reliable results have been well proven at a global scale and the technology is now significantly cheaper to implement than it was only a few years ago.
At my digital tech consultancy firm NCS NEXT, we are encouraging our clients to focus on pragmatic steps to enabling their AI and digital twin objectives. Typically this begins with data management and analytics: one of the key enablers of AI-driven predictive maintenance is the ability to capture and analyse large amounts of data from equipment and systems. Usually this involves establishment of a cloud-based data platform and associated tooling where both functionality and security of data assets can be rapidly enabled. This is followed by integration of existing sensor and meter data as an enabler to training machine learning models and establishing a fledgling digital twin service.
Based on our experience, organisations are more successful in achieving meaningful outcomes when they partner with AI and data analytics experts. Rather than building their own systems from scratch, organisations that leverage the expertise and capabilities of external partners and solution providers, such as data analytics firms and public cloud providers, can reduce the costs and risks associated with implementing AI-driven predictive maintenance.
A phased approach
Adopting a phased approach can help to demonstrate business value whilst constraining cost. Rather than attempting to implement AI-driven predictive maintenance across the entire organisation all at once, utilities can start with a small number of pilot projects or test cases. This allows them to build their knowledge and expertise gradually, and to scale up their efforts as they become more comfortable with the technology.
Looking to the future, investments in AI and digital twin technology can do more than just unlock insights from plant and equipment; they can modernise the role of utilities and accelerate the evolution of smart cities. The ability to partner with government and local agencies in the sharing of critical utility data can lead to broader schemes for improvement of service provision, planning for peak events, and cross-agency collaboration for the accommodation of population growth and increased urban density.
In conclusion, AI-driven predictive maintenance is a key tool for utilities companies looking to improve the reliability, efficiency, and safety of their critical infrastructure services. By proactively identifying and addressing potential issues, organisations can avoid costly outages and downtime, increase operating efficiency, and reduce the risk of accidents. Moreover, existing investments in sensor technology and other infrastructure can be leveraged to enable AI-based predictive maintenance at minimal cost, making it an attractive option for utilities companies of all sizes.