Collaboration between OEMs and cloud service providers: Driving future innovations

Our  initial article on predictive maintenance covered the definition of such a system, its construction, and the key implementation challenges. In this part, we'll delve into how PdM technology is transforming different facets of the automotive industry and its advantages for OEMs, insurers, car rental companies, and vehicle owners.

Best predictive maintenance techniques and where you can use them

In the first part of the article, we discussed the importance of sensors in a PdM system. These sensors are responsible for collecting data from machines and vehicles, and they can measure various variables like temperature, vibration, pressure, or noise. Proper placement of these sensors on the machines and connecting them to IoT solutions, enables the transfer of data to the central repository of the system. After processing the data, we obtain information about specific machines or their parts that are prone to damage or downtime.

The automotive industry can benefit greatly from implementing these top predictive maintenance techniques.

Vibration analysis

 How does it work?

Machinery used in the automotive industry and car components have a specific frequency of vibration. Deviations from this standard pattern can indicate "fatigue" of the material or interference from a third-party component that may affect the machine's operation. The PdM system enables you to detect these anomalies and alert the machine user before a failure occurs.

 What can be detected?

The technique is mainly applied to high-speed rotating equipment. Vibration and oscillation analysis can detect issues such as bent shafts, loose mechanical components, engine problems, misalignment, and worn bearings or shafts.

Infrared thermography analysis

 How does it work?

The technique involves using infrared cameras to detect thermal anomalies. This technology can identify malfunctioning electrical circuits, sensors or components that are emitting excessive heat due to overheating or operating at increased speeds. With this advanced technology, it's possible to anticipate and prevent such faults, and even create heat maps that can be used in predictive models and maintenance of heating systems.

 What can be detected?

Infrared analysis is a versatile and non-invasive method that can be used on a wide scale. It is suitable for individual components, parts, and entire industrial facilities, and can detect rust, delamination, wear, or heat loss on various types of equipment.

Acoustic analysis monitoring

 How does it work?

Machines produce sound waves while operating, and these waves can indicate equipment failure or an approaching critical point. The amplitude and character of these waves are specific to each machine. Even if the sound is too quiet for humans to hear in the initial phase of malfunction, sensors can detect abnormalities and predict when a failure is likely to occur.

 What can be detected?

This PdM technology is relatively cheaper compared to others, but it does have some limitations in terms of usage. It is widely used in the Gas & Oil industry to detect gas and liquid leaks. In the automotive industry, it is commonly used for detecting vacuum leaks, unwanted friction, and stress on machine parts.

Motor circuit analysis

 How does it work?

The technique works through electronic signature analysis (ESA). It involves measuring the supply voltage and operating current of an electronic engine. It allows locating and identifying problems related to the operation of electric engine components.

 What can be detected?

Motor circuit analysis is a powerful tool that helps identify issues related to various components, such as bearings, rotor, clutch, stator winding, or system load irregularities. The main advantage of this technique is its short testing time and convenience for the operator, as it can be carried out in just two minutes while the machine is running.

PdM oil analysis

 How does it work?

An effective method for Predictive Maintenance is to analyze oil samples from equipment without causing any damage. By analyzing the viscosity and size of the sample, along with detecting the presence or absence of third substances such as water, metals, acids or bases, we can obtain valuable information about mechanical damage, erosion or overheating of specific parts.

 What can be detected?

Detecting anomalies early is crucial for hydraulic systems that consist of rotating and lubricating parts, such as pistons in a vehicle engine. By identifying issues promptly, effective solutions can be developed and potential damage to the equipment or a failure can be prevented.

Computer vision

 How does it work?

Computer vision is revolutionizing the automotive industry by leveraging AI-based technology to enhance predictive maintenance processes. It achieves this by analyzing vast datasets, including real-time sensor data and historical performance records, to rapidly predict equipment wear and tear. By identifying patterns, detecting anomalies, and issuing early warnings for potential equipment issues, computer vision enables proactive maintenance scheduling.

 What can be detected?

In the automotive industry, computer vision technology plays a crucial role in detecting equipment wear and tear patterns to predict maintenance requirements. It can also identify manufacturing defects such as scratches or flaws, welding defects in automotive components, part dimensions and volumes to ensure quality control, surface defects related to painting, tire patterns to match with wheels, and objects for robotic guidance and automation.

Who and how can benefit from predictive maintenance

Smart maintenance systems analyze multiple variables and provide a comprehensive overview, which can benefit several stakeholders in the automotive industry. These stakeholders range from vehicle manufacturing factories and the supply chain to service and dealerships, rental companies, insurance companies, and drivers.

Below, we have outlined the primary benefits that these stakeholders can enjoy. In the OEMs section, we have provided examples of specific implementations and case studies from the market.

Car rentals

 Fleet health monitoring and better prediction of the service time

Managing service and repairs for a large number of vehicles can be costly and time-consuming for rental companies. When vehicles break down or are out of service while in the possession of customers, it can negatively impact the company’s revenue. To prevent this, car rental companies need constant insight into the condition of their vehicles and the ability to predict necessary maintenance. This allows them to manage their service plan more efficiently and minimize the risk of vehicle failure while on the road.

Car dealerships

 Reducing breakdown scenarios

Car dealerships use predictive maintenance primarily to anticipate mechanical issues before they develop into serious problems. This approach helps in ensuring that vehicles sold or serviced by them are in optimal condition, which aids in preventing breakdowns or major faults for the customer down the line. By analyzing data from the vehicle's onboard sensors and historical maintenance records, dealerships can identify patterns that signify potential future failures. Predictive maintenance also benefits dealerships by allowing for proactive communication with vehicle owners, reducing breakdown scenarios, and enhancing customer satisfaction

Vehicle owners

 Peace of mind

Periodic maintenance recommendations for vehicles are traditionally based on analyzing historical data from a large population of vehicle owners. However, each vehicle is used differently and could benefit from a tailored maintenance approach. Vehicles with high mileage or heavy usage should undergo more frequent oil changes than those that are used less frequently. By monitoring the actual vehicle condition and wear, owners can ensure that their vehicles are always at 100% and can better manage and plan for maintenance expenses.

Insurance companies

 Risk & fraud

By using data from smart maintenance systems, insurance companies can enhance their risk modeling. The analysis of this data allows insurers to identify the assets that are at higher risk of requiring maintenance or replacement and adjust their premiums accordingly. In addition, smart maintenance systems can detect any instances of tampering with the equipment or negligence in maintenance. This can aid insurers in recognizing fraudulent claims.

OEMs successful development of PdM systems

BMW Group case study

The German brand implements various predictive maintenance tools and technologies, such as sensors, data analytics, and artificial intelligence, to prevent production downtime, promote sustainability, and ensure efficient resource utilization in its global manufacturing network. These innovative, cloud-based solutions are playing a vital role in enhancing their manufacturing processes and improving overall productivity.

The BMW Group's approach involves:

•     Forecasting phenomena and anomalies using a cloud-based platform.    Individual software modules within the platform can be easily switched on and off if necessary to instantly adapt to changing requirements. The high degree of standardization between individual components allows the system to be globally accessible. Moreover, it is highly scalable and allows new application scenarios to be easily implemented.
•     Optimizing component replacements       (this uses advanced real-time data analytics).  
•     Carrying out maintenance and service work in line with the requirements of the actual status of the system.  
•     Anomaly detection using advanced AI predictive algorithms.  

Meanwhile, it should be taken into account that in BMW's body and paint shop alone, welding guns perform some 15,000 spot welds per day. At the BMW Group's plant in Regensburg, the conveyor systems' control units run 24/7. So any downtime is a huge loss.

→  SOURCE case study.

FORD case study

Predictive vehicle maintenance is one of the benefits offered to drivers and automotive service providers as part of Ford's partnerships with CARUSO and HIGH MOBILITY.  In late 2020, Ford announced two new connected car agreements to potentially enable vehicle owners to benefit from  a personalized third-party offer.

CARUSO and HIGH MOBILITY will function as an  online data platform that is completely independent of Ford and allows  third-party service providers secure and compliant access to vehicle-generated data. This access will, in turn, enable third-party providers to create personalized services for Ford vehicle owners. This will enable drivers to benefit from smarter  insurance, technical maintenance and roadside recovery.

Sharing vehicle data (warning codes, GPS location, etc.) via an  open platform is expected to be a way to maintain competitiveness in the connected mobility market.

→  SOURCE case study.

Predictive maintenance is the future of the automotive market

An effective PdM system means less time spent on equipment maintenance, saving on spare parts, eliminating unplanned downtime and improved management of company resources. And with that comes more efficient production and customers’ and employees’ satisfaction.

As the data shows, organizations that have implemented a PdM system report an average  decrease of 55% in unplanned equipment failures.  Another upside is that, compared to other connected car systems (such as infotainment systems), PdM is relatively easy to monetize. Data here can remain anonymous, and all parties involved in the production and operation of the vehicle reap the benefits.

Organizations have come to recognize the hefty returns on investment provided by predictive maintenance solutions and have thus adopted it on a global scale. According to Market Research Future, the global Predictive Maintenance market is projected to grow to  111.30 billion by 2030 , suggesting that further growth is possible in the future.

Generative AI is quickly emerging as one of the key drivers of automotive innovation. This shift is not just a future possibility; it's already happening. In 2023, the generative AI market in the automotive sector was valued at approximately  USD 387.54 million . Looking ahead, it's projected to surge to about  USD 2,691.92 million by 2032 , demonstrating a robust Compound Annual Growth Rate (CAGR) of 24.03% from 2023 to 2032. Major Original Equipment Manufacturers (OEMs) are integrating sophisticated AI algorithms into various aspects of the industry, from vehicle design to enhancing customer interactions.

The impact of generative AI in the automotive sector is already evident. For instance, NVIDIA's generative AI models empower designers to swiftly  transform 2D sketches into intricate 3D models, significantly speeding up the design process and opening up new avenues for creativity and efficiency​​. Meanwhile, automotive manufacturing companies are exploring collaborations with tech giants to integrate advanced AI language models into their vehicles, enhancing the driving experience​​.

This article will explore how leading automotive players are leveraging generative AI to not only keep pace with the evolving demands of the market but also redefine mobility and automotive excellence.

Software-defined vehicles and generative AI

The introduction of software-defined vehicles (SDVs) represents a significant shift in the automotive industry, moving beyond traditional performance metrics such as horsepower and chassis design to focus on software and digital capabilities. By 2025, it is estimated that vehicles could require as much as  650 million lines of code each. These vehicles heavily rely on software for critical operations such as driving assistance, navigation, and in-car entertainment systems.

The integration of generative AI in this domain further amplifies these capabilities. Generative AI is known for its ability to create and optimize designs and solutions, which is beneficial in improving both the software and hardware aspects of SDVs. It helps in generating efficient algorithms for vehicle control systems, contributes to the development of more effective and adaptive software solutions, and even assists in designing vehicle components for better performance and efficiency.

However, bringing generative AI into this landscape presents both unique opportunities and significant challenges.

The opportunities and challenges

•     Challenges  

Integrating advanced AI systems into modern vehicles is a complex and multifaceted task that demands  technical expertise and careful attention to data security and privacy , particularly with the increasing reliance on data-driven functionalities in vehicles.

The automotive industry is facing a complex regulatory environment. With the growing importance of AI and data in-vehicle systems, it has become crucial to  comply with various international regulations and standards , covering areas such as data protection, safety, and environmental impact.

One significant challenge for OEMs is the  lack of standardization within the software-defined vehicles industry, which can complicate the development and integration of new technologies as there are no universal norms or protocols to guide these processes.

 Internal transformation is also a critical aspect of this integration. OEMs may need to revamp their internal capabilities, processes, and technological infrastructure to use generative AI effectively.

•     Opportunities  

Integrating generative AI technology allows for more  creative and efficient vehicle design , resulting in  quicker prototypes and more innovative models .

It also allows for creating  personalized vehicles that cater to individual user preferences like never before. In manufacturing, generative AI promotes more efficient and  streamlined production processes , which optimizes resources and reduces waste.

Let's explore how automotive manufacturers already use gen AI to boost their operations.

Generative AI applications in the automotive industry

Generative AI's integration into the automotive industry revolutionizes multiple facets of vehicle design, manufacturing, and user experience. Let's explore these areas:

Design and conceptualization

•     Vehicle Design Enhancement    : Artificial Intelligence is revolutionizing the vehicle design process by speeding up the initial phase of the design cycle. Generative design algorithms use parameters such as material properties, cost constraints, and performance requirements to generate optimal design solutions. For example, in vehicle body design, AI can propose multiple design options that optimize for aerodynamics and strength while minimizing weight. This enables quick visualization and modification of ideas.

 Toyota Research Institute has introduced a generative AI technique to optimize the vehicle design process to produce more efficient and innovative vehicles. This approach allows designers to explore a wider range of design possibilities, including aerodynamic shapes and new material compositions.

•     Digital Prototyping    : The use of Generative AI technology makes it possible to create digital prototypes, which can be tested and refined extensively without the need for physical models. This approach is highly beneficial, as it enables designers to detect and correct potential design flaws early in the process.

 BMW's use of NVIDIA Omniverse is a significant step in design improvement. The company uses this platform to create digital twins of their manufacturing facilities, integrating generative AI to enhance production efficiency and design processes.

Manufacturing and production

•     Streamlining Manufacturing Processes    : Generative AI significantly enhances the efficiency of manufacturing processes. Unlike traditional AI or machine learning models, generative AI goes beyond identifying inefficiencies; it actively generates novel manufacturing strategies and solutions. By inputting parameters such as production timelines, material constraints, and cost factors, generative AI algorithms can propose a range of optimized manufacturing workflows and processes.

 BMW has implemented generative AI in a unique way to improve the scheduling of their manufacturing plant. In partnership with Zapata AI, BMW utilized a quantum-inspired generative model to optimize their plant scheduling, resulting in more efficient production. This process, known as Generator-Enhanced Optimization (GEO), has significantly improved BMW's production planning, demonstrating the potential of generative AI in industrial applications.

•     Supply Chain Resilience    : In the context of supply chain management, particularly during challenges like the automotive microchip shortage, generative AI plays a crucial role. Unlike conventional AI, gen AI can do more than just analyze existing supply chain networks; it can creatively generate alternative supply chain models and strategies. The algorithms can propose diverse and robust supplier networks by leveraging data about supplier capabilities, logistics constraints, and market demands.

•     Customized Production    : With generative AI, it is now possible to create personalized vehicles on a large scale, meeting the growing demand for customization in the automotive industry.

Predictive maintenance and modelling

Traditionally, predictive maintenance relies on historical data to forecast equipment failures, but generative AI enhances this process by creating detailed, simulated data environments. This technology generates realistic yet hypothetical scenarios, encompassing a vast array of potential machine failures or system inefficiencies that might not be present in existing data sets.

The generative aspect of this AI technology is particularly valuable in situations where real-world failure data is limited or non-existent. By synthesizing new data points, generative AI models can extrapolate from known conditions to predict how machinery will behave under various untested scenarios.

In modeling, generative AI goes a step further. It not only predicts when and how equipment might fail but also suggests optimal maintenance schedules, anticipates the impact of different environmental conditions, and proposes design improvements.

Customer experience and marketing

One of the challenges in using generative AI, particularly in customer interaction, is the accuracy of AI-generated responses. An example of this was an error by ChatGPT, where it was tricked into suggestion of  buying a Chevy for a dollar . This incident underlines the potential risks of misinformation in AI-driven communication, emphasizing the need for regular updates, accuracy checks, and human oversight in AI systems. Nevertheless, this technology offers many opportunities for improving the customer experience:

•     Personalized User Experiences and Enhanced Interaction    : AI's capability to adapt to individual preferences not only enhances the driving experience but also improves the functionality of vehicle features.

For instance, in collaboration with Microsoft,  General Motors is exploring the use of AI-powered virtual assistants that offer drivers a more interactive and informative experience. These assistants can potentially provide detailed insights into vehicle features and performance metrics and offer personalized recommendations based on driving patterns.

Also,  Mercedes-Benz is exploring the integration of generative AI through voice-activated functionalities in collaboration with Microsoft. This includes leveraging the OpenAI Service plugin ecosystem, which could allow for a range of in-car services like restaurant reservations and movie ticket bookings through natural speech commands.

Example applications

•     Simplified Manuals    : AI technology, enabled by natural language processing, has simplified the interaction between drivers and their vehicles. Beyond just responding to voice commands with pre-existing information, a generative AI system can even create personalized guides or tutorials based on the driver's specific queries and past interactions.

    Grape Up has developed an innovative          voice-driven car manual         that allows drivers to interact with their vehicle manual through voice commands, making it more accessible and user-friendly. With this technology, drivers no longer have to navigate through a traditional manual. Still, they can easily ask questions and receive instant verbal responses, streamlining the process of finding information about their vehicle.  

•     Roadside Assistance:    In this scenario, generative AI can go beyond analyzing situations and suggesting solutions by creating new, context-specific guidance for unique problems. For instance, if a driver is stranded in a rare or complex situation, the AI could generate a step-by-step solution, drawing from a vast database of mechanical knowledge, previous incidents, and environmental factors.

•     Map Generation    : Here, generative AI can be used to not only update maps with real-time data but also to predict and visualize future road conditions or propose optimal routes that don't yet exist. For example, it could generate a route that balances time, fuel efficiency, and scenic value based on the driver's preferences and driving history.

•     Marketing and Sales Innovation    : Generative AI-enabled content engine is transforming the creation of digital advertising for the automotive industry. This content is tailored to meet the unique requirements of automotive brands and their consumers, thereby revolutionizing traditional marketing strategies.

Safety and compliance

•     Enhancing Vehicle Safety    : Generative AI in vehicles goes beyond traditional AI systems by not only assisting drivers but also by creating predictive models that enhance safety features. It processes and interprets data from cameras and sensors to foresee potential road hazards, often employing advanced generative models that simulate and predict various driving scenarios.

•     Regulatory Compliance    : Similarly, gen AI helps automakers comply with safety standards and navigate complex regulation changes by monitoring performance data and comparing it against regulatory benchmarks. This allows automakers to stay ahead of the compliance curve and avoid potential legal and financial repercussions.

Autonomous vehicle development

•     Simulation and Testing    : Generative AI is crucial for developing autonomous vehicle systems. It generates realistic simulations, including edge-case scenarios, to test and improve vehicle safety and performance.

•     Enhancing ADAS Capabilities    : AI technology can improve essential Advanced Driver Assistance Systems (ADAS) features such as adaptive cruise control, lane departure warnings, and automatic emergency braking by analyzing data from various sensors and cameras. Generative AI's strength in this context lies in its ability to not only process existing data but also to generate new data models, which can predict and simulate different driving scenarios. This leads to more advanced, reliable, and safer ADAS functionalities, significantly contributing to the evolution of autonomous and semi-autonomous driving technologies.

Conclusion

As the automotive industry accelerates towards a more AI-integrated future, the role of expert partners like Grape Up becomes increasingly crucial. Our expertise in navigating the intricacies of AI implementation can help automotive companies unlock the full potential of this technology. If you want to stay ahead in this dynamic landscape, now is the time to embrace the power of generative AI. For more information or to collaborate with Grape Up,  contact our experts today.