Electric steering engine residual life prediction method and system based on DBN and multi-layer fuzzy LSTM

A technology of electric steering gear and life prediction, applied in the direction of neural learning method, computer aided design, biological neural network model, etc., can solve the problems of low efficiency, failure to guarantee the safety and reliability of steering gear, etc., to reduce the amount of calculation, Improve training effect and prediction accuracy, improve the effect of precision

Pending Publication Date: 2020-12-11
SHANDONG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] Existing technologies generally use post-event diagnosis or offline monitoring methods to monitor the faults of steering gears. This method is inefficient and cannot guarantee the safety and reliability of steering gears during operation.

Method used

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  • Electric steering engine residual life prediction method and system based on DBN and multi-layer fuzzy LSTM
  • Electric steering engine residual life prediction method and system based on DBN and multi-layer fuzzy LSTM
  • Electric steering engine residual life prediction method and system based on DBN and multi-layer fuzzy LSTM

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Embodiment 1

[0030] In one or more embodiments, a method for predicting the remaining life of electric steering gear based on DBN and multi-layer fuzzy LSTM is disclosed, refer to figure 1 , including the following steps:

[0031] Step (1): Layout sensors to obtain real-time monitoring data of electric steering gear;

[0032] The main faults of electric steering gear include transmission mechanism faults, motor faults and sensor faults. Current, RPM and vibration signals are readily available and contain a wealth of steering gear status information. Therefore, install two current sensors to monitor the current of motor 1 and motor 2; install four vibration sensors to monitor the vibration signals of motor 1, motor 2, transmission mechanism and housing; install three speed sensors to monitor motor 1, motor 2 and the rotational speed of the output shaft; install four temperature sensors to monitor the temperature of motor 1, motor 2, transmission mechanism and housing.

[0033] Step (2): ...

Embodiment 2

[0088] In one or more embodiments, a DBN and multi-layer fuzzy LSTM based electric steering gear remaining life prediction system is disclosed, including:

[0089] The data acquisition module is used to obtain the real-time monitoring data of the electric steering gear;

[0090] The data preprocessing module is used to preprocess the acquired real-time monitoring data;

[0091] The remaining life prediction module is used to input the preprocessed data into the trained steering gear state degradation model, and output the predicted remaining life of the electric steering gear;

[0092] Wherein, the steering gear state degradation model extracts the feature law through the deep belief network for the preprocessed data, reduces the feature dimension of the data at the same time, and then extracts the time feature in the data sequence through the multi-layer fuzzy LSTM network; based on the feature law and time features, the predicted remaining life of the electric steering gear...

Embodiment 3

[0099] In one or more embodiments, a terminal device is disclosed, including a server, the server includes a memory, a processor, and a computer program stored on the memory and operable on the processor, and the processor executes the The program implements the method for predicting the remaining life of the electric steering gear based on DBN and multi-layer fuzzy LSTM in the first embodiment. For the sake of brevity, details are not repeated here.

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Abstract

The invention discloses an electric steering engine residual life prediction method and system based on DBN and multi-layer fuzzy LSTM. The method comprises the steps: obtaining real-time monitoring data of an electric steering engine; preprocessing the obtained real-time monitoring data; inputting the preprocessed data into a trained steering engine state degradation model, and outputting the predicted residual life of the electric steering engine, wherein the steering engine state degradation model is used for extracting a feature rule from preprocessed data through a deep belief network, and then extracting the ime feature in a data sequence through a multi-layer fuzzy LSTM network; and obtaining the predicted residual life of the electric steering engine based on the feature rule and the time feature. According to the method, the deep learning network model based on the DBN and the multi-layer fuzzy LSTM is adopted to predict the residual life of the electric steering engine so that feature rules and time features of a sequence in multi-dimensional electric steering engine sensor monitoring data can be effectively extracted, and the precision of residual life prediction is improved; and the safety and the reliability of the steering engine during operation are improved.

Description

technical field [0001] The invention relates to the technical field of equipment remaining life prediction, in particular to a method and system for predicting the remaining life of an electric steering gear based on DBN and multi-layer fuzzy LSTM. Background technique [0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art. [0003] As the core equipment of advanced aircraft self-driving systems such as helicopters and drones, electric steering gear is an important control component in aircraft. If the electric steering gear fails, especially the failure of the main control steering gear, such as rudder, elevator and aileron, etc., it will cause the aircraft to be in a state of loss of control, and if it is serious, it will lead to catastrophic consequences of aircraft crash and death, which seriously restricts the flight of the aircraft. The safety and reliability of electri...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F30/27G06N3/04G06N3/08
CPCG06F30/27G06N3/049G06N3/08G06F2119/04G06N3/045
Inventor 张法业李新龙姜明顺张雷隋青美贾磊
Owner SHANDONG UNIV
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