Early fault detection
Through analytical pattern recognition in data from heterogeneous sources – from the E/E process through to the entire development cycle – it is possible to create an almost fault-free E/E vehicle. E/E problems that still continue into the field are identified at a very early stage, interpreted and returned to development.
Tool chain optimisation
We know and understand existing tool environments; we can expand and develop these accordingly. We can even identify and implement optimisation potential within tool chains too through the use of appropriate analytical methods.
Using the (diagnostic) data for workshop systems, we can analyse specific driver behaviour and coordinate it with the latest warranty data, including affected systems, and thus draw conclusions about future faults that may occur with the vehicle.
Real-time analysis of test vehicles
Through computing power and analysis-based data reduction built into the vehicle, we can use the ESG Connected Data Recorder (CDR) to transfer compressed analysis data (push/pull) via LTE. The relevant trace data is transferred via a patented ring buffer. It is as though the test vehicle is sitting on the desk of the developer.
The APM software Auklet identifies potential negative user experiences within the IoT environment. Constant performance monitoring helps to identify which functions are causing the problem and to analyse the cause of the fault right down to the individual line of code.