Predictive Maintenance: Untapped Potential in Public Sector

Public sector organizations worldwide have responsibility for high-value assets and operations associated with utilities, public venues, roads, bridges, transit and mobility systems, airports, ports, and public health systems. Unexpected downtime can lead to critical outages that cost millions of dollars in lost productivity, but replacing or fixing broken equipment can also cost tens of thousands of dollars in extra expenses. Predictive maintenance analytics capture the state of the equipment, so you can identify potential breakdowns before they impact operations.

With IoT-enabled predictive maintenance solutions, reductions in unplanned downtime can save millions of dollars and keep operations running smoothly. There are two approaches that organizations rely on when it comes to equipment maintenance: preventative maintenance and predictive maintenance.

1. Preventative maintenance follows pre-established guidelines based on timelines and/or schedules to prevent equipment and machinery breakdown.
2. Predictive maintenance relies on the real-time operational condition of equipment to predict when maintenance should be completed. Predictive maintenance provides the additional benefit of improved cost efficiency, since maintenance is only performed when it is actually needed versus based on a pre-determined schedule. Instrumentation of the equipment allows for real-time data capture that is more granular and precise.

Key to a successful predictive maintenance solution is getting access to the right information at the right time. Combining IoT and machine learning can enable real-time predictions, anticipate equipment failure or maintenance scheduling, and achieve higher levels of efficiency. A digital twin, or replica, of physical assets, processes, or systems, can be generated as models to predict preventive maintenance or to optimize output of complex machines or industrial processes. The model can be continuously updated to ‘learn’ in near real-time for any change that may occur. AWS enables customers to build robust, cost-effective, and secure predictive maintenance solutions by leveraging a combination of edge and cloud-based capabilities.

For example, Defense Innovation Unit Experimental (DIUx) is a U.S. Department of Defense (DoD) organization focused on accelerating adoption of innovative commercial technologies such as AI for national defense. DIUx selected C3 IoT to provide an AI and IoT software platform for delivering a new AI-based predictive maintenance solution that increases asset availability and reduces maintenance expenditures associated with DoD’s aircraft platforms. DIUx, the US Air Force, and C3 IoT have prototyped the C3 Predictive Maintenance™ application on the E-3 Sentry (AWACS) aircraft platform. C3 Predictive Maintenance, built on the C3 IoT Platform and run on AWS GovCloud (US), employs AI and machine learning at scale, in near-real time, to predict impending failures at a sub-system level. The ability to predict failure in aircraft that support men and women in uniform will increase platform availability and directly enhance mission effectiveness. See here for more details.

Start Building

For collecting data from equipment and analyzing it in real-time, you should consider the following capabilities.

AWS IoT Core is a managed cloud platform that lets connected sensors, devices, and machines easily and securely interact with cloud applications and other devices. AWS Greengrass is software that lets you run local compute, messaging, data caching, sync, and ML inference capabilities for connected devices in a secure way. ML Inference is a feature of AWS Greengrass that makes it easy to perform machine learning inference locally on Greengrass Core devices using models that are built and trained in the cloud. AWS IoT Analytics is a managed service that makes it easy to run sophisticated analytics on massive volumes of IoT data without having to worry about all the cost and complexity typically required to build your own IoT analytics platform. AWS IoT Analytics enables you to apply machine learning to your IoT data with hosted Jupyter notebooks. You can directly connect your IoT data to the notebook and build, train, and execute models right from the IoT Analytics console without having to manage any of the underlying infrastructure. Using AWS IoT Analytics, you can apply machine-learning algorithms to your device data to produce a health score for each device in your fleet.

Amazon SageMaker is a fully managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Amazon SageMaker includes modules that can be used together or independently to build, train, and deploy your machine learning models. You can configure AWS IoT Rules to route device-generated data directly to machine learning models, enabling you to create machine-learning models without having to learn complex ML algorithms and technology. With Amazon SageMaker integration, you will be able to generate billions of predictions daily and serve those predictions in real-time and at high throughput.

AWS Marketplace features a number of partner solutions. In addition, providers such as C3 IoT, BSquare, Xaqt, Splunk, and Predikto also offer comprehensive capabilities for predictive maintenance while leveraging AWS services.

Learn more at Using AWS IoT for Predictive Maintenance and Machine Learning on AWS. Also, see Predictive Maintenance with AWS IoT and Amazon Machine Learning for a hands-on activity leveraging AWS IoT and Amazon ML services to build a predictive maintenance solution.