The automotive industry is navigating a rapidly changing world. Innovative solutions are constantly emerging in areas such as technological developments, environmental sustainability, security and user experience. In this context, two critical factors stand out: lube oil analysis and data analytics. In this issue, we will examine how these elements intersect and shape the future of automobiles.
- Lube Oil Analysis: The Pulse of Engines
From industrial machinery to the passenger vehicles, from mining sites to construction sites; more than one type of lubricant of different grades and properties is used in each type of engine operating. Each of these oils contains a lot of valuable data about the condition of the engine. These data unite around the same purpose, whether in the automotive industry, mining or construction industry:
- Predictive Maintenance
A maintenance manager, operator or engineer wants to know when to service the engine he uses. Manufacturers generally give an idea about the operating hours of the engine and the operating hours of the oil, but since not every user uses the engine at the same load, speed and under the same environmental conditions, unexpected situations may occur. Lube oil analysis will provide information about this situation.
How It Works: With a 60 ml of oil sample wear levels, coolant, fuel dilution, soot, etc. anomalies in the engine oil can be detected. Within the framework of these results, they can identify potential problems, plan maintenance and take precautions before major malfunctions occur.
Regarding predictive maintenance, I can give an example from Formula 1 races that I follow closely. Each race is held in different climatic conditions. Humidity, temperature, wind and dust conditions in the environment where race tracks are located create serious load and stress on the engines of vehicles moving at an average speed of 225 km/h to 360 km/h. As a result, when an effective oil film cannot form between the piston and cylinder walls and on the bearings, temperature increases, wear, oil thickening and other failure modes may be encountered.
Formula 1's mechanics also check and evaluate both the oil condition and the condition of the engine by performing basic analyzes in mobile oil laboratories with oil samples taken from the engines of the vehicles during each race preparation. Based on the analysis results, improvements are made to the vehicle's performance, tire selections are determined and ultimately the race strategy is created.
- Optimized Oil Change Intervals:
It is useful to follow fixed oil change schedules. Additionally, if oil analysis is performed at each maintenance period, the rate of the benefit increases. You will ask why. Unnecessary replacement of oil that is in good condition and still usable is prevented. No oil manufacturer has produced its oil to be changed only after 10,000 km or 15,000 km. More precisely, the life of the oil is not limited to these distances. If you test it with an oil sample from the first maintenance to the ongoing maintenance, you will witness it. You will see that you have achieved significant cumulative savings in your fleets by preventing unnecessary oil changes.
- Environmental Impact:
Continuing to use a usable engine oil with reliability-oriented oil management approach supports optimizing resource use with sustainable environmental awareness. A lubricant that is classified as waste can be recycled after its group number is determined. Therefore, if you are storing large amounts of waste engine oil or a different type of oil, you can get support from waste companies about what kind of handling process the waste oil should go through.
- Innovation: Where Oil Meets Data
The future of the automotive industry lies at the intersection of lubricant analysis and data analytics. Imagine if an engine could predict not only wear, but also when a particular part would fail. We can achieve this level of precision by combining lubricant analysis data with machine learning algorithms.
How comes? Every piece of equipment in the production process must have a report card, and this includes engines. How many times a year does the engine of a critical vehicle you use in the field fail, what malfunction it has, frequency of failure, cost, etc. You can follow a trend with additional parameters and evaluate this data together with the oil analysis results.
Remember, the raw material of the equipment you use will definitely leave a fingerprint in the oil. This fingerprint will also shed light on problems that may occur in subsequent trends. You can create a maintenance strategy with root cause analysis in the light of this data. Based on your root cause analysis results, you can receive data specific to the type of anomaly with oil sensors that can be integrated into your engine. You can also create early limit / alarm levels with the developed algorithms.
