Predictive maintenance and its benefits in the automotive industry

Due to fast digital transformation, today customers seek exceptional service and supreme quality products. The automotive industry is no exception.

Manufacturers are increasingly concerned about the rate at which the vehicles roll off the production lines, their after-sales services, feedback from customers, and the extent of vehicle recalls.

Today’s vehicles are much smarter than just several years ago. Thanks to the use of IoT, Big Data, Data Analytics, AI, cloud computing, cars have become safer, more autonomous, and data-driven. Nowadays, vehicles can easily communicate with each other, with pedestrians, other devices, buildings, grid, and even smart homes.

And, in order to optimize all the processes, vehicle manufacturers are adapting numerous technologies. Automotive businesses are increasingly moving from preventive maintenance towards predictive maintenance. Because Predictive Analytics is among the newest and most promising techniques to achieve these goals.

Predictive analytics is a great manifestation of data science. It uses data analytics to make predictions about future outcomes. Its systems utilise historical data paired with machine learning techniques to generate insights into the future with impressive accuracy.

It helps businesses determine when a machine or a part of a vehicle needs to be serviced, using data mining, data preprocessing and employing machine learning algorithms.

A recent market study conducted by Transparency Market Research predicts that between 2019 and 2027, the automotive predictive maintenance market will expand globally at an impressive CAGR of 28%. The demand for connected mobility is the main driver for such growth.

Some of the most popular applications of preventive maintenance in the automotive industry:

• Regular oil change;
• Transmission checkup;
• Belt and tire inspection;
• Coolant change;
• Engine air filter and cabin filter control, etc.

The data collected by the sensors might indicate gradual overheating or friction in some parts. While, if an action is not taken, over time this can result in a complete breakdown of the vehicle or the individual part. The internal machine learning algorithm keeps track of every detail in real-time while also analyzing the frequency at which they occur. This helps the driver to prevent undesirable events timely.

Everything should be checked regularly. And of course, some of these basic controls might be easily done in a traditional way. Based on the instructions from original equipment manufacturers (OEMs) and the history of past failures, drivers can prevent the breakdown or degradation of a component or spare part. They can also identify the frequency of essential controls and schedule a maintenance plan accordingly.

But there is another type of issue that depends mostly on the driver’s behavior, weather and driving conditions. Here, preventive maintenance should often be combined with predictive maintenance since the breakdowns can appear unexpectedly.

With predictive maintenance, machines are serviced only when it is required. This helps reduce over-maintenance and often also large costs.

Note: predictive maintenance is hard to implement when there is no data about previous maintenance activities. In such a case, preventive maintenance is a good start to obtain the necessary info. Automotive predictive and preventive maintenance often go hand in hand.

Investments

With the rise of intelligent technologies, the investments in transportation predictive maintenance are increasing rapidly. Due to the pandemics, many people prefer individual mobility to the use of public transport and other shared mobility services. The key reason for such a shift is the concern over one’s health and safety. The demand for cars is getting higher while vehicle production has slowed down due to massive lockdowns and disrupted supply chains. Used car sales and used car leasing are expected to fill gaps between customer demands and low new vehicle production though. While predictive maintenance can help prolong the lifespan of the used cars and minimize unexpected downtimes.

Fleet management companies have also been leveraging PdM technology to avoid unprecedented failure of their assets. This helps to protect the ROI on each vehicle and boost fleet efficiency.

Other examples of predictive maintenance in automotive industry:

• Trouble code analysis: control over bugs and failures prevention.
• Roadside assistance: finding out where exactly the problem is. Sometimes roadside assistance is not even needed: automatic phone assistance might be enough.
• Analyzing vehicle health indicators.
• New insights and feedback to enhance security, performance, and lifespan of vehicles.

PdM enables vehicle trouble code analysis and vehicle health parameters (miles, fuel level, engine temperature, tire pressure, etc.). This data can be used for the following:

· PdM app for vehicle owners;

· Dealership maintenance packages — customized car maintenance packages for owners;

· Roadside assistance — to determine when a vehicle needs assistance on the road. It is then serviced in an accelerated manner with the help of the vehicle location data and diagnostic codes.

Vehicles equipped with Predictive analytics technology have temperature, acoustic, sound, infrared and battery-level sensors that monitor the vehicle conditions continuously.

There is a number of advantages of predictive maintenance to OEMs, fleet operators, and private users:

• High security;
• Lower maintenance costs;
• Longer vehicle lifespan;
• Less warranty claims;
• Optimized separate parts inventory;

A predictive maintenance solution leverages machine learning algorithms to proffer smart maintenance recommendations to car owners. The system helps to predict or/and avoid an issue/breakdown based on past occurrence of such events.

Adopting PdM. What are the Challenges?

The common challenges that organisations face when starting to adopt predictive maintenance technology are the following:

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

2) Establishing seamless communication between numerous components;

3) Integrating IoT security system;

4) High upfront costs;

These challenges need to be considered carefully when opting for a predictive maintenance solution, but the benefits that it offers make it well worth the investment. Proven.

Conclusions

Predictive maintenance — built on the devices, machine learning, and AI that empowers it — offers numerous potential benefits to vehicle owners and manufacturers.

Utah Tech Labs specializes in providing innovative Automotive & IoT solutions, E-commerce, Cloud and Digital Experience services to its global customer base.

We offer custom made solutions to young start-ups, small, mid-size and large enterprises. To learn more about our automotive practice, contact us here.