An unsupervised industrial system anomaly detection method based on deep transfer learning

An industrial system, transfer learning technology, applied in the fields of data mining, deep learning and neural networks

Active Publication Date: 2019-05-03
GUANGDONG UNIV OF TECH
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  • Abstract
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Therefore, using traditional deep learning techniques...

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  • An unsupervised industrial system anomaly detection method based on deep transfer learning
  • An unsupervised industrial system anomaly detection method based on deep transfer learning
  • An unsupervised industrial system anomaly detection method based on deep transfer learning

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Embodiment Construction

[0017] The unsupervised industrial system anomaly detection method based on deep transfer learning of the present invention comprises the following steps:

[0018] 1) Collect sensor data;

[0019] 2) Data preprocessing;

[0020] 3) Divide the dataset,

[0021] 4) Model building;

[0022] 5) Model training;

[0023] 6) Model testing;

[0024] 7) Determine whether there is a fault.

[0025] When a new machine is added to the industrial system, repeat steps 1) to 6) to get a new model, and then redeploy the model.

[0026] The above step 2) data preprocessing divides sensor data into three categories, the first category has a certain range of data; the second category is data that increases with time; the third category is data with only fixed state quantities; The data in a certain range of change is processed by normalization, which compresses the range of the training set data to [0,1]; the data that increases with time adopts differential processing, and the differentia...

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Abstract

The invention discloses an unsupervised industrial system anomaly detection method based on deep transfer learning. According to the invention, labeled machine sensor sequence data from a migration source and unlabeled sensor sequence data from a migration target are utilized to train an industrial system abnormality detection model with good generalization ability, and the industrial system abnormality detection model is trained and tested to finally generate a trained industrial system abnormality discrimination model. By means of the model, the received machine sensor sequence data can be analyzed, and whether the machine is abnormal or not can be judged.

Description

technical field [0001] The invention relates to the fields of data mining, deep learning, neural network and the like, and in particular to an unsupervised industrial system anomaly detection method based on deep transfer learning. Background technique [0002] In recent years, with the rapid development of deep learning technology, deep learning has blossomed in all walks of life. One of the applications in industrial systems is anomaly detection. For example, in a power plant, by collecting data from sensors such as temperature and air pressure on the boiler, an abnormality discrimination model is used to determine whether there is an abnormality; Whether there is any abnormality; in the telecommunications marketing system, by collecting server hardware load and network traffic data, it is judged whether there is any abnormality through the abnormality discrimination model. The examples mentioned above all use industrial sensors to receive data. Among them, we call the la...

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Application Information

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IPC IPC(8): G06F16/245G06N3/04
CPCG05B23/024G05B23/0221G06N3/084G06N3/045G05B19/4065G06N3/088
Inventor 蔡瑞初李梓健温雯郝志峰王丽娟陈炳丰许柏炎李俊峰
Owner GUANGDONG UNIV OF TECH
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