Computer-implemented method for training a machine learning model to detect installation errors in an elevator, in particular an elevator door, a computer-implemented method for classifying installation errors and a system thereof

A computer-implemented method for detecting installation errors in an elevator, said method comprising:

• detecting a plurality of physical parameters by one or more sensors arranged at the elevator;
• performing a first feature extraction and a second feature extraction from the detected physical parameters;
• feeding the first extracted features and the second extracted features to a machine learning model that is trained that includes a combination of a Set Function model and a Fourier-Transform model to detect installation errors, wherein the machine learning model training comprising:
• obtaining a dataset including at least one time series from values of second physical parameters;
• obtaining a first input layer and a second input layer by extracting features from the dataset;
• and
• feeding the Set Function model with the first input layer and the Fourier-Transform model with the second input layer.

A system for detecting installation errors in an elevator, the system comprising:

• a plurality of sensors arranged at the elevator and configured to detect physical parameters;
• and
• a device that includes instructions that when executed are configured to cause the device to perform operations, the operations including:
• extracting features from the detected physical parameters;
• and
• detecting installation errors of the elevator by applying the extracted features to a machine learning model that includes a combination of a Set Function model and a Fourier-Transform model wherein training the machine learning model to detect installation errors in the elevator comprising:
• obtaining a dataset including at least one time series from values of second physical parameters;
• obtaining a first input layer and a second input layer by extracting features from the dataset;
• and
• feeding the Set Function model with the first input layer and the Fourier-Transform model with the second input layer.