Preventing Machine Breakdowns: How Physical AI Predicts Equipment Problems

Physical AI: Intelligence that acts in the real world

Physical AI differs from traditional AI by directly interacting with and manipulating the physical world. While traditional AI processes data and generates text on screens, Physical AI enables robots, self-driving cars, and smart systems to perceive, understand, and act in real multi-dimensional environments.

The key difference: Physical AI understands spatial relationships and physical behavior through training on synthetic and real-world data, bridging the gap between digital intelligence and physical action.

How it works: Highly accurate computer simulations create digital twins of real spaces like factories, city streets etc. where virtual sensors and machines that mirror real world physics are used to train a highly specialized model.

Transforming maintenance

Physical AI shifts maintenance from reactive to autonomous. These systems perceive their environment, understand component relationships, and take preventive actions before problems occur. The automotive Predictive Maintenance (PdM) market will reach $100 billion by 2032, a revolution in vehicle care powered by Physical AI capabilities.

Electric Vehicles (EV) are a great example of where Physical AI can be put into action. They can be designed to constantly learn from their surroundings, make instant decisions to optimize performance, and manage their own health on the go. These systems understand how their parts fit and work together, predict how physical forces will impact different components, and adjust driving patterns to reduce wear and tear.

The same principles behind PdM in cars also show up in other areas. Manufacturing robots now anticipate and prevent equipment failures before they happen. In smart warehouses, systems schedule their own upkeep for maximum efficiency. Healthcare robots keep tabs on their accuracy and recalibrate themselves as needed. Even smart infrastructure can spot its own issues and coordinate repairs automatically.

How does it actually work?

Physical AI systems in modern EVs represent an advanced approach to vehicle monitoring and maintenance through integrated sensor networks that continuously analyze multiple vehicle systems. These systems track battery health, motor performance, brakes, and suspension components while building dynamic models of component interactions. The AI monitors relationships between temperature, vibration, electrical load, and mechanical stress to predict and prevent potential failures. The system takes proactive measures like adjusting charging patterns to reduce battery stress and modifying regenerative braking to minimize wear. This predictive maintenance approach transforms traditional reactive vehicle maintenance into a proactive system that understands and responds to real-world conditions, though specific performance metrics and outcome data would be needed to quantify the benefits.

Overview

In this blog, you will learn the different types of generative AI applications transforming Physical AI-powered PdM and how AWS services enable these innovations.

AWS Internet of Things (IoT), Artificial Intelligence (AI) /Machine Learning (ML), and generative AI have transformed the landscape of connected vehicles and, more specifically, EV’s, by offering innovative solutions for Physical AI-powered PdM. The integration of these advanced technologies has paved the way for a more efficient and effective approach to maintaining EVs, ensuring their optimum performance and longevity through deep understanding of physical systems.

AWS IoT is used by many automotive customers to develop and manage their Physical AI applications (Autonomous driving, predictive maintenance, infotainment etc.). AWS IoT enables EVs to connect to the cloud and transmit real-time data about their condition and performance, including spatial relationships and physical interactions between components. This data is then analyzed using AWS AI/ML services that can identify patterns, detect anomalies, and predict potential issues by understanding the physics of how different systems interact in the real world.

Generative AI in Physical AI-powered PdM operates across four key stages: Machine prioritization uses retrieval-augmented generation (RAG) systems to analyze structured and unstructured maintenance data, determining which equipment requires priority attention. Failure prediction processes machine sensor data through real-time analytics and ML models to predict equipment failures before they occur. Repair plan generation leverages large language models to create comprehensive work orders with instructions and resource allocation by integrating data from multiple sources. Maintenance guidance generation combines service notes and repair plans using generative AI to provide enhanced, actionable guidance for technicians.

This approach allows automotive manufacturers to gather rich data on vehicle performance in real-world physical conditions, improving future vehicle designs by understanding how vehicles interact with their physical environment and making informed decisions about component improvements that account for real-world physics and usage patterns.

Architecture overview

PdM in EVs entails monitoring, analyzing, and acting based on gathered insights. The EVs are equipped with a variety of sensors that gather data on battery health, vehicle location, motor health, brake health, and more. To minimize operating costs, this pattern aims to enhance EV maintenance by utilizing sensor data to create PdM models.

1. Data ingestion and processing

Connected vehicles offer automakers opportunities to boost vehicle quality, safety, and autonomy. However, these advancements come with challenges, particularly in effectively managing and leveraging the significant volumes of data produced by connected vehicles. The task of capturing vehicle data is complicated by the diverse proprietary data formats of electronic control units (ECUs) used by different manufacturers and the substantial costs associated with expanding data collection operations.

AWS IoT FleetWise is a purpose-built service by AWS for the automotive industry. It allows you to easily collect, transform, and transfer vehicle data from various formats present in your vehicles, regardless of make, model, or options. The service standardizes the data format, making it easier for analysis in the cloud without the need for custom data collection systems. With AWS IoT FleetWise, you can efficiently transfer data to the cloud in near-real time using intelligent filtering capabilities. By selecting the data to transfer and defining rules and events based on parameters like weather conditions, location, or vehicle type, you can reduce the amount of data sent to the cloud.

In this section, we will utilize AWS IoT FleetWise to gather and store vehicle data in S3 for the purpose of training machine learning models for predictive analysis.

- Setup AWS IoT FleetWise Edge Agent on the vehicle – Create an Edge Agent for AWS IoT FleetWise to facilitate communication between the vehicle and the cloud. Edge Agent is a fully functional piece of embedded software written in C++ designed for vehicle data collection that can run on most embedded Linux-based platforms. IoT FleetWise controls what data is collected and transferred by the Edge Agent from the vehicle.

- Create signal catalog – Signals structure vehicle data and metadata in distinct types:
  - Sensors capture real-time measurements like temperature, storing each signal’s name, data type, and unit.
  - Attributes contain fixed details such as manufacturer and manufacturing date. Branches create hierarchical organization – Vehicle branches into Powertrain, which contains the combustionEngine sub-branch. Sensor data tracks immediate vehicle status including fluid levels, temperatures, and vibrations.
  - Actuator data controls device states for components like motors and door locks. When you adjust a device – like switching a heater on or off – you update its actuator data.

Signal catalogs streamline vehicle modeling with pre-defined signals. AWS IoT FleetWise integrates Vehicle Signal Specification (VSS), defining standard signals like “vehicle_speed” in kilometers per hour (km/h). This central repository of standard sensors and signals accelerates new vehicle model creation through efficient signal reuse.

- Create a vehicle model – You use signals to establish vehicle models that standardize the format of your vehicles. Vehicle models ensure uniform data across multiple vehicles of the same type, enabling efficient data processing from fleets of vehicles. Vehicles created from the same vehicle model inherit a consistent set of signals.

- Create a decoder manifest – Decoder manifests contain decoding information that AWS IoT FleetWise uses to translate binary vehicle data into easily understandable values. IoT FleetWise supports OBD ||, CAN bus, and vehicle middleware such as ROS2. For instance, if your vehicle utilizes an OBD network interface, the decoder manifest should include signals to associate a message with ID 11 and binary data like 0000×11 with OBDCoolantTemperature.

- Creating vehicles – Vehicles are instances of vehicle models. Vehicles must be created from a vehicle model and associated with a decoder manifest. Vehicles upload one or more data streams to the cloud. For example, a vehicle can send mileage, battery voltage, and state of heater data to the cloud.

- Create and deploy campaign to collect vehicle data – Once the vehicle has been modeled, and the signal catalog has been created, you can now create data collection campaigns using signals created within the model. A campaign is an orchestration of data collection rules. Campaigns give the Edge Agent for AWS IoT FleetWise software instructions on how to select, collect, and transfer data to the cloud.All campaigns are created in the cloud. After the campaigns have been marked as approved by team members, then AWS IoT FleetWise automatically deploys them to vehicles. Automotive teams can choose to deploy a campaign to a specific vehicle or a fleet of vehicles. The Edge Agent software will not start collecting data of the vehicle network until a running campaign is deployed to the vehicle.

- Store vehicle data in S3 – The Edge Agent for AWS IoT FleetWise software transfers selected vehicle data to Amazon Timestream or Amazon Simple Storage Service (Amazon S3). After your data arrives in the data destination, you can use other AWS services to visualize and share it.

2. PdM model training

Machine learning (ML) algorithms are utilized here to perform PdM analytics in order to anticipate equipment failures and optimize maintenance activities. PdM utilizes the real-time data to analyze various factors that are correlated with EV failure, thereby enabling the prediction of potential failure occurrences. This proactive approach can effectively minimize unplanned vehicle breakdowns, prolong the lifespan of EV parts, and reduce overall repair costs.

Once the EV data is brought into the AWS environment, it is stored in an Amazon S3 bucket. The data stored in Amazon S3 is then used to generate real-time predictions from a trained and deployed ML model. These predictions can be further processed and utilized by downstream applications to take necessary actions and initiate PdM activities.The solution is comprised of the following sections:

- Model training and deployment – We utilize the PdM dataset from the Data Repository to train a machine learning model with the XGBoost algorithm using SageMaker. Subsequently, we deploy the trained model to a SageMaker asynchronous inference endpoint.
- Train the model – In order to train our model, we will first store the EV Data in the Amazon S3. This allows us to securely and efficiently store the vast amount of data that we will be working with. Once the data is stored, we can begin the training process using Amazon SageMaker Training. This service is designed to handle the training of various machine learning models at scale. Its capabilities allow us to train our models quickly and accurately, even when dealing with large datasets and we can ensure that our model training is both efficient and effective, leading to high-quality results.
- Near real-time EV data ingestion – The EV data is collected from the vehicle and processed in the AWS environment before being stored in Amazon S3. This data includes important parameters like battery voltage, battery temperature, motor health, location, and etc. Subsequently, an Amazon Lambda function is triggered to invoke an asynchronous Amazon SageMaker endpoint.
- Perform PdM in near real-time – Asynchronous Amazon SageMaker endpoints are utilized to generate inferences from the deployed model for incoming EV data. These endpoints are particularly suitable for PdM workloads, as they support larger payload sizes and can generate inferences within minutes. The inferences generated from the model are stored in Amazon S3. These inferences can be applied for generating dashboards, visualizations, and performing generative AI tasks.

To ensure your Predictive Maintenance solution remains effective at scale, implement a robust training and deployment pipelines by referencing the AWS Well-Architected Framework principles for machine learning[3].

3. Generative AI

- Create the AWS Glue Data Catalog using an AWS Glue crawler (or a different method). Using the Titan-Text-Embeddings model on Amazon Bedrock, convert the metadata into embeddings and store it in an Amazon OpenSearch Serverless vector store, which serves as our knowledge base in our RAG framework. At this stage, the process is ready to receive the query in natural language.
- The user enters their query in natural language. You can use any web application to provide the chat UI. Therefore, we did not cover the UI details in our post.
- The solution applies a RAG framework via similarity search, which adds the extra context from the metadata from the vector database. This table is used for finding the correct table, database, and attributes.
- The model gets the generated SQL query and connects to Athena to validate the syntax.
- Finally, we run the SQL using Athena and generate output. Here, the output is presented to the user. For the sake of architectural simplicity, we did not show this step.

Conclusion

The convergence of Generative AI and Physical AI is fundamentally reshaping condition-based and predictive maintenance across industries. As we’ve explored throughout this discussion, generative AI’s ability to analyze vast datasets, generate synthetic training scenarios, and provide intelligent recommendations is transforming how Physical AI systems monitor, diagnose, and maintain themselves. From EVs that predict battery degradation to industrial robots that schedule their own maintenance, we’re witnessing a paradigm shift where intelligent systems don’t just perform tasks – they actively preserve and optimize their own operational capabilities.

References

About the authors

Ram Gorur is a Senior Solution Architect at AWS, specializing in Agriculture and Consulting Services, with a focus on Edge AI and Connected Products. Based in Virginia, he leverages over 23 years of comprehensive IT experience to help AWS’s enterprise customers implement IoT solutions that span from edge devices to cloud infrastructure. His expertise encompasses designing and deploying connected product solutions across diverse industries, where he develops customized architectural frameworks that bridge edge computing with cloud capabilities. Ram’s combined knowledge of agriculture, IoT, and cloud technologies enables him to create integrated solutions that help businesses modernize their operations through edge-to-cloud connectivity.

Ashish Chaurasia is a Senior Technical Account Manager at AWS who has partnered with enterprise customers since 2020 to align cloud technologies with strategic business outcomes. With over 17 years of software development experience, he specializes in guiding organizations through cloud-native transformation journeys. Ashish is an IoT enthusiast and enjoys building DIY projects to automate day to day tasks.

Channa Samynathan is a Senior Worldwide Specialist Solutions Architect for AWS Edge AI & Advanced Compute. With over 29 years of experience in the technology industry, Channa has held diverse roles including design engineering, system testing, operations, business consulting, and product management. His career spans multiple multinational telecommunication firms, where he has consistently demonstrated expertise in sales, business development, and technical solution design. Channa’s global experience, having worked in over 26 countries, has equipped him with deep technical acumen and the ability to quickly adapt to new technologies. At AWS, he focuses on working with customers, designing edge compute applications from the edge to the cloud, educating customers on AWS’s value proposition, and contributing to customer-facing publications.

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