Fault diagnosis in power electronics using adaptive PCA
First Claim
1. A system for analyzing a health status of a component of vehicles, comprising:
- an electronic device;
a sensor configured to detect sensor data corresponding to at least one performance characteristic of the electronic device;
a memory configured to store a machine learning algorithm; and
a machine learning processor coupled to the sensor and configured to;
receive the sensor data;
generate, using the machine learning algorithm, a model of the electronic device to determine a T squared threshold and a Q threshold;
perform a T squared analysis of the electronic device by comparing a T squared value to the T squared threshold;
perform a Q analysis of the electronic device by comparing a Q value to the Q threshold;
determine that the model of the electronic device is faulty when the T squared value is less than the T squared threshold and the Q value is greater than or equal to the Q threshold; and
generate a new model of the electronic device or update the model of the electronic device when the model of the electronic device is determined to be faulty.
3 Assignments
0 Petitions
Accused Products
Abstract
A system includes an electronic device and a sensor to detect sensor data corresponding to the electronic device. The system also includes a machine learning processor that receives the sensor data and generates a model of the electronic device to determine a T squared threshold and a Q threshold using a machine learning algorithm. The machine learning processor also performs a T squared analysis of the electronic device by comparing a T squared value to the T squared threshold, and a Q analysis of the electronic device by comparing a Q value to the Q threshold. The machine learning processor also determines that the model is faulty when the T squared value is less than the T squared threshold and the Q value is greater than or equal to the Q threshold, and generates a new model or updates the model when the model is determined to be faulty.
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Citations
20 Claims
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1. A system for analyzing a health status of a component of vehicles, comprising:
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an electronic device; a sensor configured to detect sensor data corresponding to at least one performance characteristic of the electronic device; a memory configured to store a machine learning algorithm; and a machine learning processor coupled to the sensor and configured to; receive the sensor data; generate, using the machine learning algorithm, a model of the electronic device to determine a T squared threshold and a Q threshold; perform a T squared analysis of the electronic device by comparing a T squared value to the T squared threshold; perform a Q analysis of the electronic device by comparing a Q value to the Q threshold; determine that the model of the electronic device is faulty when the T squared value is less than the T squared threshold and the Q value is greater than or equal to the Q threshold; and generate a new model of the electronic device or update the model of the electronic device when the model of the electronic device is determined to be faulty. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A system for analyzing a health status of a component of vehicles, comprising:
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an electronic device; a sensor configured to detect sensor data corresponding to at least one performance characteristic of the electronic device; a memory configured to store a machine learning algorithm; and a machine learning processor coupled to the sensor and configured to; receive the sensor data; generate, using the machine learning algorithm, a principal component analysis (PCA) model of the electronic device to determine a reduced data matrix; determine a T squared threshold and a Q threshold based on the reduced data matrix; perform a T squared analysis of the electronic device by comparing a T squared value to the T squared threshold; perform a Q analysis of the electronic device by comparing a Q value to the Q threshold; determine that the PCA model of the electronic device is faulty when the T squared value is less than the T squared threshold and the Q value is greater than or equal to the Q threshold; and generate a new PCA model of the electronic device or update the PCA model of the electronic device when the PCA model of the electronic device is determined to be faulty. - View Dependent Claims (10, 11, 12, 13, 14, 15)
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16. A method for analyzing a health status of a component of vehicles, comprising:
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detecting, by a sensor, sensor data corresponding to at least one performance characteristic of an electronic device of a vehicle; storing, in a memory, a machine learning algorithm; receiving, by a machine learning processor, the sensor data; generating, by the machine learning processor using the machine learning algorithm, a model of the electronic device to determine a T squared threshold and a Q threshold; performing, by the machine learning processor, a T squared analysis of the electronic device by comparing a T squared value to the T squared threshold; performing, by the machine learning processor, a Q analysis of the electronic device by comparing a Q value to the Q threshold; determining, by the machine learning processor, that the model of the electronic device is faulty when the T squared value is less than the T squared threshold and the Q value is greater than or equal to the Q threshold; and at least one of generating, by the machine learning processor, a new model of the electronic device, or updating, by the machine learning processor, the model of the electronic device when the model of the electronic device is determined to be faulty. - View Dependent Claims (17, 18, 19, 20)
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Specification