Predictive maintenance allows government entities to more accurately understand conditions and predict maintenance needs of in-service infrastructure and assets. Introducing artificial intelligence (AI) into preventative maintenance programs has reduced costs and contributed to increased reliability, safety, and efficiency. Let’s look at why AI is so beneficial and how government organizations at the federal and local levels are using AI for predictive maintenance.
Why Use AI and Predictive Maintenance for Government Infrastructure and Assets?
The use of AI in predictive maintenance is significantly improving how public assets are managed. It empowers government agencies to transition from passive problem-solving to proactive asset management. Unlike traditional preventative maintenance that prescribes service at regular predetermined intervals, AI-driven predictive maintenance uses a data-centric approach, making continuous recommendations based on the asset’s condition. Here are a few ways AI contributes to preserving functionality and increasing the longevity of public assets.
Improving operational efficiency
Predictive maintenance allows government entities to operate more efficiently, maximizing the use of skilled maintenance staff while reducing the amount of time assets are offline for repairs. With the ability to forecast what type of maintenance will be needed and when, predictive maintenance helps deliver more consistent and dependable services.
With critical information on the health and performance of infrastructure and assets, government entities can ensure they are properly maintained throughout their entire lifespan. Teams can then address maintenance needs in the early stages, extending the useful life of infrastructure and assets.
Improving the safety of operations
Well-maintained infrastructure and assets are safer. AI-enabled predictive maintenance programs can reduce the risk of accidents associated with poorly maintained machinery, vehicles, and infrastructure. Artificial intelligence can also be used to create a virtual model of critical infrastructure that contains all of the components of the real-world system. Using this model, operations and safety personnel can visualize the system under a variety of operating conditions to identify potential points of failure. Armed with this information, these teams can engage in more informed contingency or remediation planning.
Examples of AI in Predictive Maintenance
Here are several specific examples that illustrate how predictive maintenance is helping government agencies maintain assets and infrastructure.
Computer vision, a branch of AI that uses machines to extract information from visual data, can be deployed to identify cracks and potholes in roadways, alerting relevant authorities that repairs are needed. AI can also be used to accelerate the analysis of visual data such as photographs and video gathered during bridge inspections, detecting subtle signs of cracking and other types of wear that may go unnoticed by human inspectors.
AI-driven predictive maintenance tools can be used to continuously monitor the condition of public buildings, such as courthouses, post offices, and schools. Analyzing data transmitted from IoT devices and remote sensors, predictive maintenance tools can identify both impending equipment failures and routine maintenance needs in HVAC systems, elevators, plumbing, and electrical systems, facilitating a reduction in overall maintenance costs while improving the safety and comfort of those who use these buildings.
Many government agencies maintain large fleets of vehicles, including school buses, police cars, and public transportation such as commuter trains and subway cars. Similar to the example above, AI can analyze data gathered from onboard vehicle sensors to predict when maintenance will be required and alert maintenance personnel to acute issues such as engine malfunctions. This proactive approach enables agencies to keep their fleet vehicles in optimal condition and avoid unplanned disruptions in service delivery.
AI-enabled predictive maintenance plays an essential role in national defense, ensuring the operational readiness of ground combat and support systems, ships, and aircraft. Equipment-mounted sensors monitor the status of critical components, alerting maintenance personnel when a piece of hardware requires maintenance or replacement. By analyzing data such as operational conditions and hours of use, predictive maintenance programs determine the optimal time for replacement or service. This approach avoids wasting resources by replacing components too soon while preventing the system failures that occur when maintenance needs go unmet.
By continuous monitoring and analyzing equipment data, AI-powered predictive maintenance solutions can identify anomalies and patterns that indicate potential failures in emergency services equipment. Keeping emergency response systems, vehicles, and other equipment fully operational protects public safety and ensures a swift response to emergencies.
AI can help municipalities monitor a range of environmental quality metrics. One example is water quality. Remote sensors distributed throughout a water system can assist governments to monitor water quality in real time. AI algorithms trained to detect dangerous bacteria and harmful particles suspended in water samples help water systems quickly identify and remediate the sources of contamination. Artificial intelligence can also be used to analyze satellite imagery, allowing governments to spot unregistered businesses that may generate substantial amounts of pollution, such as unregistered large-scale, concentrated animal feeding operations.
Enable Your AI-Driven Predictive Maintenance Solutions with Snowflake
Snowflake is a comprehensive cloud data platform optimized for artificial intelligence workflows. With immediate access to all your maintenance-related data, Snowflake eliminates the need for complex data pipelines, allowing you to incorporate nearly all types of data into your models. Transform data into AI-powered insights using your language of choice, including Python, Java, and Scala, and realize the full potential of AI-enabled predictive maintenance with Snowflake.