Context
The device under analysis consists of a moving part with a rubber element cleaning a fixed part.
The rubber element bears all the friction and is subject to wear after each movement.
As the level of wear increases, the quality of the cleaning process deteriorates.
Need
The customer wanted to automatically measure the wear of the rubber element, in order to display its wear level to the driver and help plan its replacement.
Solution
DiagFit was used to build a wear indicator based on acceleration data captured from the moving part and by rubber type. The wear indicator was obtained by using data from devices with a new rubber part, learning normal behavior and then detecting devices with wear conditions. The wear indicator can be further refined using historical data for new and old rubber parts.
Results
Using available data, including acceleration data for different parts and rubber types, DiagFit was able to classify worn parts with a 99.3% true-positive rate and 2.8% false-positive rate. 50% of the data was used to train the model.
