Deep convolutional factor analyzer
AssigneeSiemens Aktiengesellschaft
InventorsChao YUAN, Amit CHAKRABORTY
Life of this patent
Estimating the term…
Abstract
Systems, methods, and computer-readable media are disclosed for generating and training a deep convolutional generative model for multivariate time series modeling and utilizing the model to assess time series data indicative of a machine or machine component's operational state over a period of time to detect and localize potential operational anomalies.
Claims20
A method, comprising:
- receiving training data as input;
- training a machine learning model using the training data, wherein the machine learning model comprises multiple layers and utilizes convolution;
- receiving sensor data as input;
- and
- detecting an anomaly in the sensor data using the trained machine learning model.
A system, comprising:
- at least one memory storing computer-executable instructions;
- and
- at least one processor configured to access the at least one memory and execute the computer-executable instructions to:
- receive training data as input;
- train a machine learning model using the training data, wherein the machine learning model comprises multiple layers and utilizes convolution;
- receive sensor data as input;
- and
- detect an anomaly in the sensor data using the trained machine learning model.
A computer program product comprising a storage medium readable by a processing circuit, the storage medium storing instructions executable by the processing circuit to cause a method to be performed, the method comprising:
- receiving training data as input;
- training a machine learning model using the training data, wherein the machine learning model comprises multiple layers and utilizes convolution;
- receiving sensor data as input;
- and
- detecting an anomaly in the sensor data using the trained machine learning model.
Dependencies are inferred from the claim text.