Time series data intelligent fault diagnosis method based on deep learning

A technology of time series and deep learning, applied in the field of fault diagnosis, can solve problems such as insufficient deep feature extraction and data mining capabilities of SVM, long test time, and multiple computing resources in the fully connected layer, so as to achieve intelligent, convenient and fast fault diagnosis and improve diagnosis Accuracy, the effect of reducing the amount of training parameters

CN112067916AInactive Publication Date: 2020-12-11WUHAN UNIV OF TECH
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Publication Date
2020-12-11
Estimated Expiration
Not applicable · inactive patent

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Abstract

The invention discloses a time series data intelligent fault diagnosis method based on deep learning. The method comprises the following steps: 1) collecting one-dimensional time series health state data of electromechanical equipment, wherein fault data is the fault data arranged at a preset measuring point of the electromechanical equipment; 2) constructing a one-dimensional time series originalfault data set according to the acquired N health state data; 3) preprocessing the data, wherein the preprocessing includes normalization and data truncation; 4) constructing a two-dimensional feature map fault data set; 5) carrying out data set division; 6) constructing a deep learning fault diagnosis model; 7) training the deep learning fault diagnosis model to obtain parameters; and 8) performing fault diagnosis on the input to-be-diagnosed sample data by using the deep learning fault diagnosis model, and outputting a final fault diagnosis result. By constructing the two-dimensional feature map and improving a traditional convolutional neural network model structure, a fault diagnosis speed and fault diagnosis accuracy are improved.
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Description

technical field

[0001] The present invention relates to fault diagnosis technology, in particular to an intelligent fault diagnosis method for time series data based on deep learning. Background technique

[0002] With the advent of the intelligent era, more and more electromechanical equipment products are developing in the direction of intelligence, automation, multi-function and precision. Today, the complexity of the application environment of electromechanical equipment products is gradually increasing. Under such circumstances, the long-term continuous operation of electromechanical equipment is prone to various faults. If the fault cannot be diagnosed and eliminated in time, once the fault hazard spreads, it may cause heavy losses. Therefore, it is extremely necessary to provide effective intelligent fault diagnosis methods for electromechanical equipment; Nowadays, with the wide application of "Internet +", Internet of Things and advanced smart sensor technology in e...

Examples

Embodiment Construction

[0044] In order to make the object, technical solution and advantages of the present invention more clear, the present invention will be further described in detail below in conjunction with the examples. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0045] Such as figure 1 As shown, a time series data intelligent fault diagnosis method based on deep learning, which includes the following steps:

[0046] Step (1), collecting the one-dimensional time-series fault data of the monitoring object; arranging sensors on the monitoring object, using the sensors to collect the one-dimensional time-series data monitoring signals generated when the monitoring object is running in various health states, the The number of measuring points arranged by the sensor on the monitoring object is set to T (that is, T channels, T greater than or equal to 1, generally multiple channels), use...