Abstract: A fault detection apparatus and computer-implemented method for providing physically explainable fault information of a bearing built in a machine by a fault detection model is provided, including: obtaining sensor data measured at the bearing as input data relating to an input data domain and the fault detection model, mapping the measured sensor data from the input data domain to a selected data domain resulting in an augmented fault detection model which outputs augmented predicted failure value related to the selected data domain, wherein the selected data domain has a physical meaning to the fault of the bearing, performing a feature attribution on the augmented fault detection model quantifying an importance of at least one individual feature to the augmented failure value related to the selected data domain, and displaying the individual feature and the respective quantified importance in the selected data domain at a user interface.

Abstract: A method for calibrating an electronic assembly during a manufacturing process is provided, including the steps: determining a calibration value for the assembly which for a predefined input value gives a deviation between an actual output value output by the assembly and a predefined desired output value, transmitting the calibration value to the assembly, and storing the calibration value in the assembly, wherein the calibration value of the assembly is determined by a machine learning method executed in a calibration device, and the machine learning method is trained by training data, which include historical calibration values of a plurality of assemblies of the same type and parameters of assemblies of the same type, which are dependent on the manufacturing process and/or express physical properties.