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INTELLIGENT SYSTEM FOR DIAGNOSING THE STATE OF VEHICLE SYSTEMS AND ASSEMBLIES
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The article considered the possibility of diagnosing the state of systems and assemblies of a car using an artificial neural network. During the experiment, two different states of the engine were identified based on the images of thermograms. ResNet convolutional neural networks were trained for automated classification of thermogram images obtained using a thermal imager. The data obtained was split into training and testing samples. The data was broken down in the ratio: 70% for training and 30% for testing. With sequential partitioning, the training sample was se-lected at the beginning of the recording of each individual experiment, and the test sample was se-lected at the end. The best results were obtained when training the CNN ResNet18. The accuracy of testing with a random method of data partitioning was 100%.
Title: INTELLIGENT SYSTEM FOR DIAGNOSING THE STATE OF VEHICLE SYSTEMS AND ASSEMBLIES
Description:
The article considered the possibility of diagnosing the state of systems and assemblies of a car using an artificial neural network.
During the experiment, two different states of the engine were identified based on the images of thermograms.
ResNet convolutional neural networks were trained for automated classification of thermogram images obtained using a thermal imager.
The data obtained was split into training and testing samples.
The data was broken down in the ratio: 70% for training and 30% for testing.
With sequential partitioning, the training sample was se-lected at the beginning of the recording of each individual experiment, and the test sample was se-lected at the end.
The best results were obtained when training the CNN ResNet18.
The accuracy of testing with a random method of data partitioning was 100%.
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