Method for analysing the cause of an anomalous state of a machine in a supervision method that is based on a model and that recognizes anomalous states of the machine by determining an anomaly metric, wherein the model models machine states by reconstructing at least two operating parameters of the machine, which serve as input values of the model, as output values, wherein the operating parameters are sensor values that are measured by sensors arranged on the machine or are pre-processed sensor values, wherein the output values of the model exhibit deviations in comparison to the respectively corresponding input values and the model has been trained, through machine learning, to minimize the deviations; wherein, when the supervision method has recognized an anomalous state, the method comprises: determining a reconstruction error for each of the operating parameters based on the input value and the output value, reconstructed by the model, of the respective operating parameter; selecting at least one operating parameter on the basis of the reconstruction errors; and outputting the at least one selected operating parameter; characterized in that the at least one operating parameter is selected on the basis of a size of the reconstruction errors, wherein a specific number of operating parameters containing the largest reconstruction error is selected; and in that the method furthermore comprises: providing and training a submodel (402_n, 402_2, 502, 512) for at least one of the operating parameters, wherein, for the at least one of the operating parameters, the respective submodel comprises, as input values, the output values of the model with the exception of that output value that corresponds to the operating parameter to which the submodel is assigned and comprises, as further input value or further input values, the value of the operating parameter, the value of the operating parameter and the output value, corresponding to the operating parameter, of the model, or comprises the absolute value of the difference between the input value of the operating parameter and the output value, corresponding to the operating parameter, of the model; and in the step of determining the reconstruction errors, determining the reconstruction error of the at least one of the operating parameters as a deviation between input values and output value of this operating parameter in the submodel associated with this operating parameter and outputting the reconstruction errors associated with the selected operating parameters. wherein the model models machine states by reconstructing at least two operating parameters of the machine, which serve as input values of the model, as output values, wherein the operating parameters are sensor values that are measured by sensors arranged on the machine or are pre-processed sensor values, wherein the output values of the model exhibit deviations in comparison to the respectively corresponding input values and the model has been trained, through machine learning, to minimize the deviations; wherein, when the supervision method has recognized an anomalous state, the method comprises: determining a reconstruction error for each of the operating parameters based on the input value and the output value, reconstructed by the model, of the respective operating parameter; selecting at least one operating parameter on the basis of the reconstruction errors; and outputting the at least one selected operating parameter; determining a reconstruction error for each of the operating parameters based on the input value and the output value, reconstructed by the model, of the respective operating parameter; selecting at least one operating parameter on the basis of the reconstruction errors; and outputting the at least one selected operating parameter; providing and training a submodel (402_n, 402_2, 502, 512) for at least one of the operating parameters, wherein, for the at least one of the operating parameters, the respective submodel comprises, as input values, the output values of the model with the exception of that output value that corresponds to the operating parameter to which the submodel is assigned and comprises, as further input value or further input values, the value of the operating parameter, the value of the operating parameter and the output value, corresponding to the operating parameter, of the model, or comprises the absolute value of the difference between the input value of the operating parameter and the output value, corresponding to the operating parameter, of the model; and in the step of determining the reconstruction errors, determining the reconstruction error of the at least one of the operating parameters as a deviation between input values and output value of this operating parameter in the submodel associated with this operating parameter and outputting the reconstruction errors associated with the selected operating parameters.