Equipment maintenance time prediction method based on deep learning
A technology of deep learning and maintenance timing, applied in forecasting, data processing applications, instruments, etc., can solve problems such as difficult maintenance work, low efficiency of equipment use, inaccurate equipment repair timing, etc., and achieve the effect of improving prediction accuracy
Active Publication Date: 2021-09-17
ARMOR ACADEMY OF CHINESE PEOPLES LIBERATION ARMY
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Problems solved by technology
In the actual application of maintenance support, it is difficult to carry out maintenance work according to the actual environment and conditions of the equipment to be maintained, resulting in inaccurate repair timing of equipment and low efficiency of equipment use
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[0020] Below in conjunction with accompanying drawing, further describe the present invention through embodiment, but do not limit the scope of the present invention in any way.
[0021] The present invention provides a method for predicting equipment maintenance time intervals based on deep learning. By establishing an equipment maintenance opportunity prediction model with a mixed structure of cyclic neural network RNN and convolutional neural network CNN, the equipment maintenance time interval is predicted to obtain accurate equipment maintenance time intervals. Time to fix. image 3 Shown is the flow of the prediction method provided by the present invention, and the specific implementation of deep learning-based equipment maintenance time interval prediction for armored equipment includes the following steps:
[0022] Taking the prediction of the time interval of minor repairs of armored equipment as an example, the data is preprocessed. Extract the unit number from t...
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The invention discloses an equipment maintenance time prediction method based on deep learning, and the method can predict an equipment repair time interval by building an equipment maintenance time prediction model of a mixed structure of a recurrent neural network (RNN) and a convolutional neural network (CNN), so as to obtain the precise maintenance time of equipment. The method comprises the following steps: extracting attribute information related to maintenance from equipment maintenance service data; extracting equipment maintenance service attribute information in a same equipment maintenance information, and processing the equipment maintenance service attribute information to obtain a plurality of training sample data and labels; establishing an equipment maintenance time prediction model of a mixed structure of a recurrent neural network (RNN) and a convolutional neural network (CNN); training to obtain a well trained equipment maintenance time prediction model; inputting data of to-be-predicted equipment into the trained equipment maintenance time prediction model to realize deep learning-based equipment maintenance time interval prediction.
Description
technical field [0001] The invention belongs to the technical field of equipment maintenance, and relates to a prediction technology for equipment maintenance time intervals, in particular to the design and implementation of a method for predicting equipment maintenance timing based on deep learning. Background technique [0002] In the actual maintenance support of equipment, the existing technology mainly uses the unified equipment maintenance interval standard to formulate equipment maintenance plan. In the actual application of maintenance support, it is difficult to carry out maintenance work according to the actual environment and conditions of the equipment to be maintained, resulting in inaccurate repair timing of equipment and low efficiency of equipment use. Contents of the invention [0003] In order to overcome the shortcomings of the above-mentioned prior art, the present invention provides a method for predicting equipment maintenance time intervals based on ...
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IPC IPC(8): G06Q10/04G06Q10/00G06N3/04
CPCG06Q10/04G06Q10/20G06N3/045
Inventor 罗晓玲王维锋张惠民陈财森向阳霞金传洋王子强张晶晶陈颂郑斯文马杰毕建权莫伟锋屈强胡海荣
Owner ARMOR ACADEMY OF CHINESE PEOPLES LIBERATION ARMY