Equipment residual life prediction method and device, computer equipment and medium
A life prediction and equipment technology, which is applied in the field of computer equipment and media, and equipment remaining life prediction, can solve the problems of low model accuracy, time-consuming training, poor training effect, etc., and achieve the goal of reducing computational complexity and improving efficiency Effect
Pending Publication Date: 2021-10-15
ENNEW DIGITAL TECH CO LTD
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[0003] In view of this, the disclosed embodiments of the present invention provide a method, device, computer equipment, and medium for predicting the remaining life of equipment to solve the problem in the prior art that due to the huge amount of data, the time-consuming training is very large, resulting in poor training effect. With a small amount of data, the accuracy of the trained model is extremely low
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[0015] In the following description, for the purpose of illustration rather than limitation, specific details such as specific system structures and techniques are set forth in order to provide a thorough understanding of the disclosed embodiments of the present invention. It will be apparent, however, to one skilled in the art that the present disclosure may be practiced in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present disclosure with unnecessary detail.
[0016] The disclosure of the present invention will be described in detail below with reference to the accompanying drawings and examples.
[0017] figure 1 It is a schematic diagram of the application scene of the disclosed embodiment of the present invention. The application scenario may include terminal devices 1 , 2 and 3 , a server 4 and a network 5 .
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The invention relates to the technical field of energy equipment, and provides an equipment residual life prediction method and device, computer equipment and a medium. The method comprises the following steps: dividing an obtained original time sequence set based on a preset division index to obtain original time sequence subsets; processing each original time sequence subset in the original time sequence subsets based on a preset processing strategy, generating feature data, and obtaining a feature data set; training the original processing model by using the feature data set to obtain a target processing model; and generating target prediction data. According to the embodiment of the invention, each original time sequence subset in the at least one original time sequence subset is processed to generate the feature data, and each original time sequence subset can be converted into one feature data, so that under the condition that each time sequence data feature in the original time sequence subset is not lost, the operation complexity during model training is greatly reduced, and the model training efficiency is improved.
Description
technical field [0001] The disclosure of the present invention relates to the technical field of energy equipment, and in particular to a method, a device, a computer equipment and a medium for predicting the remaining life of equipment. Background technique [0002] Equipment health management is an important scientific research field, which can be widely used in comprehensive energy and industrial fields. Its focus is to calculate the degradation state of equipment or systems and estimate the remaining service life of the system. The existing technology is generally based on each time-series data in the time-series data set of the device for model training. Due to the huge amount of data, the training time is very large, resulting in extremely low training efficiency; if a small amount of data is used, the accuracy of the model after training extremely low. Contents of the invention [0003] In view of this, the disclosed embodiments of the present invention provide a m...
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IPC IPC(8): G06F30/27G06N3/08G06F119/04
CPCG06F30/27G06N3/08G06F2119/04
Inventor 张燧徐少龙
Owner ENNEW DIGITAL TECH CO LTD