Predictive Maintenance in the Automotive Industry — An Insider’s Perspective

Leya Lakshmanan
Embitel Technologies
5 min readJul 5, 2021

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As digital transformation has made its mark across various industries, customer demands have also evolved over time. Today, customers seek exceptional service and supreme quality of products. The status quo in the automotive industry is no different.

In the current business landscape, OEMs are not only focused on how good their range of vehicles are. They are also concerned about the rate at which these vehicles roll off the production lines, their after-sales services, feedback from customers, and the extent of vehicle recalls.

Since these aspects of business have gained a lot of importance, vehicle manufacturers are increasingly making use of technology to optimize their operations during the vehicle manufacturing process and post sales. This is where Predictive Analytics gains significance.

Predictive analytics is an impressive manifestation of data science that uses data analytics to make predictions about future outcomes. Predictive analytics systems utilise historical data and machine learning techniques to generate insights into the future with astonishing accuracy.

Predictive maintenance (PdM) in the automotive industry is a great example of predictive analytics. It helps businesses determine when a machine or vehicle part needs servicing, using techniques such as data mining, data preprocessing and employing machine learning algorithms.

Automotive Predictive Maintenance Market — Some Interesting Stats

With so much competition in the market, how can automotive OEMs leverage technology to elevate their services?

A recent market study conducted by Transparency Market Research shows that predictive maintenance is the answer to this question. PdM and AI in the automotive industry can be leveraged to optimize engine performance, transmission function, exhaust systems and structural stability of vehicles.

The report also predicts that between 2019 and 2027, the automotive predictive maintenance market will expand at an impressive CAGR of 28% globally. The demand for connected mobility is the key driver for this growth.

Image Source — Transparency Market Research

Predictive Maintenance in Automotive Manufacturing

In an automotive manufacturing environment, if there is scheduled or unscheduled downtime, the corresponding costs may result in a serious setback.

With predictive maintenance, it is possible to constantly monitor the health of industrial equipment in real time and predict the probability of failures. This improves the efficiency of operations and reduces maintenance cost of equipment.

Emergence of Digital Twin (DT) Technology

The use of sensors and Industrial IoT on the factory floor has enabled organisations to diagnose the health of each piece of machinery in detail. In this context, we will touch upon Digital Twin technology that can be leveraged to garner valuable insights on industrial equipment.

Digital Twin technology includes the usage of simulation techniques to develop a “digital twin” — an elaborate physical and functional description of a physical system with all of its operational data. In other words, a digital twin is a digital model of a physical asset and it accurately represents its physical counterpart’s functionalities, working condition and health.

An interesting fact to note here is that the digital twin actually ages with the physical vehicle, based on the environmental stress that the real vehicle may be subjected to.

Digital twins enable factory administrators to accurately predict and address issues well in advance of prototype testing.

PdM and Vehicle Servicing

Did you know that regular maintenance can actually enhance the life of a vehicle by almost 50%? Yes, it’s true!

When technological advancements such as Over the Air (OTA) update is integrated with vehicles, the car owners do not even have to perform routine car maintenance activities at a service station. They need to visit the service centres only for crucial or emergency servicing activities. Predictive maintenance is highly successful in assuring these comforts to vehicle owners.

A predictive maintenance solution employs machine learning algorithms to proffer intelligent vehicle maintenance recommendations to car owners. The system predicts the possibility of an issue/breakdown based on past occurrence of such events.

For example, the data collected by the sensors may indicate gradual overheating or friction in vehicle parts. This can, over time, result in a complete breakdown of the vehicle or the individual part. The internal machine learning algorithm keeps track of breakdown events in real-time and also analyses the frequency at which they occur. Based on this data, the algorithm then predicts when the next breakdown is expected.

This helps the driver to take necessary precautions in a timely manner. He/she can also get the vehicle serviced or the part inspected by an expert to avoid a breakdown.

Fleet management companies have been leveraging PdM technology to avoid unprecedented failure of their assets. This protects the ROI on each vehicle and boosts the fleet efficiency significantly.

Predictive Maintenance and Vehicle Recalls

As seen from the above examples, predictive maintenance benefits vehicle owners immensely. However, it should be highlighted that automotive OEMs and technology companies also reap benefits from the use of this technology, after the sale of vehicles.

· As the in-vehicle diagnostic systems of connected cars get more sophisticated, the vehicle is able to send signals when the replacement of parts or maintenance is required. Hence, this reduces unanticipated failures and the occurrence of vehicle recalls.

· Another advantage of PdM is that it sends alerts only when a servicing activity is really needed. It reduces over-maintenance and no-fault-found events that can be costly to the owner.

Predictive Analytics Use Cases

Vehicles equipped with Predictive analytics technology have temperature, acoustic, sound, infrared and battery-level sensors that monitor the vehicle conditions continuously. The data collected by these sensors are used to determine the vehicle servicing requirements through predictive maintenance. This data can also be transmitted to the IoT cloud where it is used by analytics and visualization tools to make better decisions.

PdM enables vehicle trouble code analysis, i.e., the inspection of engine diagnostic codes and vehicle health parameters (mileage, fuel level, engine temperature, tire pressure, etc.). This data can be used for the following:

· PdM app for vehicle owners — Using PdM data, it is possible to create vehicle maintenance app for consumers.

· Dealership maintenance packages — Car dealerships can use the PdM data to create customized car maintenance packages for vehicle owners.

· Roadside assistance — PdM data makes it easy to determine when a vehicle needs assistance on the road. Such requests are then serviced in an accelerated manner using the vehicle location data and diagnostic codes.

Adopting PdM — What are the Challenges?

The common challenges that organisations face when adopting predictive maintenance technology are as follows:

1) The need for cutting-edge sensors, smart equipment and advanced business analytics tools

2) Establishing seamless communication between various components of a PdM solution

3) Integration of IoT security in the system

4) The dilemma of high upfront costs

Although these challenges need to be considered carefully when opting for a predictive maintenance solution, the benefits the solution offers down the line will make it well worth the investment!

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Leya Lakshmanan
Embitel Technologies

🚗 Automotive and IoT Enthusiast | 🎯 Head of Marketing at Embitel Technologies | 🏆 CMS Asia Content Marketing Summit Award Winner | 🎨 Artist