Development Group

About KYB

Established in 1919, nowadays the KYB became a world’s largest supplier of shock absorbers and struts for auto manufacturers and the aftermarket. KYB owns 15 production plants in Asia, Europe, and the United States and authorized service stations which provide car services.

Challenges

Nowadays the most common way of diagnosing the working capacity of car’s suspension is to use specialized test stand. The stand allows shaking car's suspension and diagnosing whether it is in the acceptable state. Another way is to use specialized mobile equipment that allows testing the suspension on site. Both solutions are based on expensive equipment, need experience how to use it and how to interpret the results. Currently, there are no solutions to diagnose car’s suspension state without visiting the service station.

Solution description

KYB’s team decided to check whether it is possible to diagnose the state of car’s suspension using smartphone’s built-in gyroscope and accelerometer. The idea is to allow the car owner to diagnose car’s suspension using his own smartphone and to understand whether visiting the service station is needed. Axon has provided research and developed proof of concept of the idea.  To make a scalable solution were used the machine learning technology and neural network.

Achievements after project delivery

Axon successfully developed the working prototype of a mobile application that allowed to diagnose car’s suspension by one person using a smartphone. The prototype demonstrated availability and simplicity of such approach. The user puts his smartphone above the wheel which he is planning to diagnose and shakes the car in the appropriate way. After car’s fluctuations are ended program shows the result for the current wheel.

Additional functionality

Implementation details

Axon developed the iOS application that gathers and saves an oscillation signal tracker and provides an instrument that allows to visualize them and find dependencies. In the current project, we used the machine learning technology that delivered 90% measurement accuracy. The neural network and algorithm itself are written in C++ language to provide maximum calculation speed. The application for iPhone is written on Swift language, CoreMotion, UIKit. The application is supported by iPhone 6 and above.

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