The topic of predictive maintenance is also receiving increasing attention in the automotive industry - after all, it can help companies avoid expensive recalls and thus avoid straining customer relationships. This article provides an overview of the use of predictive maintenance in the automotive industry and explains how data analyses form the basis for predictive maintenance.
Predictive Maintenance is often compared to looking into a crystal ball. Yet the predictions for impending failures are based on sophisticated analyses and specified Algorithms. With a high-quality database, the right Assessment bases and Approaches downtimes can be predicted, damage can be prevented, warranty costs for maintenance can be reduced and customer loyalty can be strengthened with this model.
Predictive maintenance is especially beneficial for the automotive and logistics industries.
Predictive maintenance is and will be for industries where Machines will be unavoidable, especially in the future - this approach is already very popular in the automotive and logistics sectors. But also in mechanical engineering or in the field of networked production the use of predictive maintenance makes sense.
The automotive industry's uphill battle: high costs for maintenance, warranty and recalls.
The automotive sector in particular has repeatedly had to deal with setbacks and Image loss recalls due to faulty vehicle parts and the emissions scandal are just two examples. Just recently, Toyota had to recall models again due to cracks in an activated carbon filter in the tank and airbag problems. Nevertheless, maintenance is rarely discussed as an approach when discussing causes and solutions.
All in all, the Japanese car manufacturer had to make a decision due to technical deficiencies. 3.4 million cars worldwide to the workshop. Toyota is not an isolated case - besides High costs for maintenance and warranty suffers particularly as a result of this Relationship with the customer.
Data can be used to avoid these costs.
But how can such a source of error be identified, prevented or even foreseen? The answer: with Data. More precisely, with Big Data. The advancing digitalisation and new possibilities, which are also emerging through the Internet of Things, do not stop at the automotive industry. One example of the advancing digitalisation in the automotive industry is the following Connected Cars. The large amounts of data produced by networked cars, for example, initially also cause costs and must therefore be used profitably in meaningful use cases.
Link tip: In our blog articles about the automotive industry, we look at the impact of Artificial Intelligence in the industry.
Customers and vehicle manufacturers alike benefit from predictive maintenance.
Not only customers benefit from the Increasing networking of vehicles- for example, through timely maintenance information or location-based recommendations - the manufacturers themselves also ultimately gain: They gain a deeper understanding of technologies, can advance their product development, reduce costs for warranty, maintenance and guarantee and learn to generate value from Big Data.
Despite a multitude of advantages, predictive maintenance is not yet widely used.
In the (Real-time) vehicle data There is valuable information in the data that needs to be collected and analysed, because it allows predictions about failures to be made and errors to be anticipated. Combining this data with the expertise of the component developers, using Advanced Analytics new approaches to quality assurance - and predictive maintenance - are possible.
According to a Study Despite the numerous advantages that predictive maintenance offers, predictive maintenance solutions are only used in a good quarter of all eligible companies. The main reason for the hesitation of many companies is the expected costs of the introduction and organisational Changes, which were introduced in the course of Data projects become necessary. (Data governance)
Data analyses form the basis for predictive maintenance.
In order to be able to identify or predict possible sources of errors, the Sample defined in order to reliably distinguish a susceptible vehicle from a "healthy" one. This eliminates the unnecessary maintenance of non-defective machines. Information on the length of the distances travelled, the frequency of vehicle use, the average speed or even weather conditions, for example, serve as sources of differentiation or information.
In the next step, the Data for these parameters. This is usually done via a telematics module in the vehicle. For older generations without telematics, the information can be obtained through a Vehicle readout in the workshop can be won.
The manufacturer can determine this data for both intact and defective vehicle models in order to subsequently create a meaningful Data analysis to be able to carry out a comparison. Only through this comparison does the manufacturer not run the risk of considering a general problem of the series, which occurs in both sick and healthy vehicles, as an anomaly. This illustrates that predictive maintenance is much more than a data-based concept of the traditional maintenance approach.
Discrepancies between "healthy" and "sick" vehicles are detected in predictive analytics models.
The discrepancies between the two types of vehicles (sick and healthy) are analysed in predictive analytics.-Model closely examined and identified. At the end of the analysis, there may be different results that need to be evaluated accordingly.
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