Abstract: An assistance apparatus and computer-implemented method for removing spurious signal components in a measured motor current signal of an electric motor for optimized motor diagnosis is provided, including the steps: receiving the measured motor voltage signal and a measured motor current signal measured at the motor; generating a modelled current signal by a motor model depending on the measured motor voltage signal; generating a reference current signal by the motor model depending on an undisturbed sinusoidal motor voltage signal; adapting a noise removal module to infer optimal denoising parameters by inputting the modelled current signal and the reference current signal into the noise removal module; determining a denoised motor current signal by inputting the measured motor current signal into the adapted noise removal module; and outputting the denoised motor current signal for optimized motor diagnosis.

Abstract: In a method for detecting a fault in a machine, a vibration signal recorded by one or more sensors sensing a vibration of the machine is received. A true fault signal is determined by applying to the vibration signal a neural network which is an unsupervised neural network, and an envelope spectrum analysis is applied to the true fault signal to detect the fault. The unsupervised neural network is designed to determine the true fault signal such that an impulsive component in the true fault signal is maximized, and is designed to determine the true fault signal such that a difference between the true fault signal and the vibration signal is minimized.

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 computer-implemented method for detecting mechanical damage to a machine based on a performance indicator present at a rotation speed of the machine is provided, including the steps of: receiving a number of calibration data points during a calibration period, each comprising the rotation speed of the machine and a performance characteristic value associated with the rotation speed, calibrating an AI-based Gaussian process regressor, by inputting the calibration data points received during the calibration period and outputting an estimated performance curve and an uncertainty range assigned to each value of the performance curve for all rotation speed values, receiving a data point to be evaluated, and comparing the data point to be evaluated with the estimated performance curve, and outputting an anomaly message if the data point to be evaluated is outside a specified limit value, which is dependent on the uncertainty range of the performance characteristic value.

Abstract: A computer-implemented method for detecting mechanical damage to a machine based on a performance indicator present at a rotation speed of the machine is provided, including the steps of: receiving a number of calibration data points during a calibration period, each comprising the rotation speed of the machine and a performance characteristic value associated with the rotation speed, calibrating an AI-based Gaussian process regressor, by inputting the calibration data points received during the calibration period and outputting an estimated performance curve and an uncertainty range assigned to each value of the performance curve for all rotation speed values, receiving a data point to be evaluated, and comparing the data point to be evaluated with the estimated performance curve, and outputting an anomaly message if the data point to be evaluated is outside a specified limit value, which is dependent on the uncertainty range of the performance characteristic value.

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.