CN111177975A - Aviation equipment availability prediction method based on artificial intelligence - Google Patents
Aviation equipment availability prediction method based on artificial intelligence Download PDFInfo
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Abstract
The invention belongs to the aviation maintenance support technology, and relates to an aviation equipment availability prediction method based on artificial intelligence. The method comprises the following steps: acquiring various input data of the aviation equipment to be tested, wherein the input data comprises equipment mean fault interval time, spare part turnover time, mean repair time, regular inspection period and overhaul period; inputting various input data of the aviation equipment to be tested into the three-layer artificial neural network model to obtain the availability index of the aviation equipment; the three-layer artificial neural network model is trained according to training sample data of the conventional aviation equipment.
Description
Technical Field
The invention belongs to the aviation maintenance support technology, and relates to an aviation equipment availability prediction method based on artificial intelligence.
Background
In the process of developing aviation equipment, the availability index of the equipment needs to be subjected to predictive analysis so as to develop design and development in a targeted manner, wherein the availability index of the equipment represents the probability that the equipment is in an intact and available state at any moment. However, the traditional availability calculation model is rough, the needed parameters are mostly statistical data in equipment use, and in the early development stage of novel aviation equipment, a corresponding equipment availability prediction method is lacked to predict equipment availability and optimize equipment design.
Disclosure of Invention
The purpose of the invention is as follows: the aviation equipment availability prediction method based on artificial intelligence is provided, and prediction analysis of equipment availability indexes is achieved.
The invention provides an aviation equipment availability prediction method based on artificial intelligence, which comprises the following steps:
acquiring various input data of the aviation equipment to be tested, wherein the input data comprises equipment mean fault interval time, spare part turnover time, mean repair time, regular inspection period and overhaul period;
inputting various input data of the aviation equipment to be tested into the three-layer artificial neural network model to obtain the availability index of the aviation equipment; the three-layer artificial neural network model is trained according to training sample data of the conventional aviation equipment.
Further, the artificial neural network model comprises an input layer, a hidden layer and an output layer, wherein the number of input nodes of the input layer is 5, and the number of nodes of the hidden layer is 3.
Further, the method further comprises:
collecting and counting average fault interval time, spare part turnover time, average repair time, regular inspection period and overhaul period of the conventional aviation equipment as training sample data, and using availability data as a training result sample;
training an initial artificial neural network model through training sample data to train an available artificial neural network.
Further, training the initial artificial neural network model by training sample data to train an available artificial neural network, including:
dividing training sample data and corresponding training sample results into n groups;
correcting the initial artificial neural network model according to each set of training sample data and corresponding training sample results in sequence by taking a group as a unit to obtain a first weight correction factor from a hidden layer to an output layer and a second weight correction factor from an input layer to the hidden layer;
according to the weight, the first weight correction factor and the second weight correction factor, correcting the parameters of the corresponding layer of the artificial neural network model; wherein the weight is composed of a group number and a group number used for training.
The invention provides an aviation equipment availability prediction device based on artificial intelligence, which comprises:
the acquisition module is used for acquiring various input data of the aviation equipment to be detected, wherein the input data comprises equipment average fault interval time, spare part turnover time, average repair time, regular inspection period and overhaul period;
the input module is used for inputting various input data of the aviation equipment to be tested into the three-layer artificial neural network model to obtain the availability index of the aviation equipment; the three-layer artificial neural network model is trained according to training sample data of the conventional aviation equipment.
Further, the artificial neural network model comprises an input layer, a hidden layer and an output layer, wherein the number of input nodes of the input layer is 5, and the number of nodes of the hidden layer is 3.
Further, the apparatus further comprises:
the collection module is used for collecting and counting average fault interval time, spare part turnover time, average repair time, regular inspection period and overhaul period of the conventional aviation equipment as training sample data, and using availability data as training result samples;
and the training module is used for training the initial artificial neural network model through training sample data to train an available artificial neural network.
Further, the training module comprises:
the grouping unit is used for dividing the training sample data and the corresponding training sample results into n groups;
the correction unit is used for correcting the initial artificial neural network model according to each group of training sample data and the corresponding training sample result in sequence by taking a group as a unit to obtain a first weight correction factor from the hidden layer to the output layer and a second weight correction factor from the input layer to the hidden layer; according to the weight, the first weight correction factor and the second weight correction factor, correcting the parameters of the corresponding layer of the artificial neural network model; the weight is composed of a group number and a group number used for training.
The invention has the advantages that: the method can accurately predict and analyze the availability index of the equipment in the early stage of development of novel aviation equipment, and supports development of equipment index demonstration and design optimization.
Drawings
FIG. 1 is a flow chart of an artificial intelligence based aviation equipment availability prediction method.
FIG. 2 is a schematic diagram of a three-layer artificial neural network model.
Detailed description of the invention
Artificial neural networks are artificial intelligence techniques developed in recent years that simulate biological processes of the nervous system. The method is characterized in that a complex nonlinear system is formed by interconnection of a large number of simple-structured neurons, and the nonlinear mapping capability is strong. The availability of the aviation equipment is a nonlinear model, so that the artificial neural network has a wide application prospect in the field of equipment availability prediction. The input signal and the expected output can be continuously provided to the network in the training process of the neural network, and the network is considered to complete the learning when the neural network can generate the actual output which is similar to the expected output for various input signals provided by learning the statistical data of the previous model equipment, so that the specific work task can be executed, and the usability prediction of the model equipment in research can be carried out. The neural network algorithm has the advantages of no need of complex nonlinear modeling work, high calculation speed and strong adaptability and stability.
The invention provides an aviation equipment availability prediction method based on artificial intelligence, which comprises the following steps of:
the method comprises the following steps: and establishing a three-layer artificial neural network model as shown in figure 2. Wherein xi(i 1, 2.. n.) is the input to the network, yj(j ═ 1, 2.. times, p) is the output of the hidden layer, and o is the output of the output layer, which is equipment availability. v. ofijRepresenting the weight between the ith node of the input layer and the jth node of the hidden layer, wjRepresenting the jth node and output layer of the hidden layerWeight between nodes. ThetajRepresents the threshold of the jth neuron node of the hidden layer, and gamma represents the threshold of the output node.
This patent is selected and is equipped mean fault interval time, spare part turnover time, mean repair time, regular maintenance cycle, overhaul period totally 5 parameters as the input, sets for input number n promptly and equals 5, sets for implicit layer node number p and equals 3.
Step two: collecting and counting average fault interval time, spare part turnover time, average repair time, regular inspection period, overhaul period and availability data of past aviation equipment as training sample data.
initializing each weight value of the neural network as a random number, setting a learning rate η as a decimal between 0 and 1, and determining a threshold theta of each neuron node of the hidden layerj(j ═ 1, 2,. p), an output layer node threshold γ is set.
Step four: inputting training sample data of past equipment, wherein d represents the availability value of the past equipment. And calculating the output of each layer of the neural network according to a formula.
And calculating the output of each node of the hidden layer:
calculating output layer node output:
calculating a weight correction factor from a hidden layer to an output layer:
Δwf＝η(d-o)o(1-α)yi；
calculating a weight correction factor from an input layer to a hidden layer:
Δvij＝η(d-o)o(1-o)wfyj(1-yj)xi；
step five: and updating all weights according to the weight correction factors:
step six: and (4) checking whether all training samples are trained, if so, executing the step seven, and if not, selecting untrained samples and returning to the step four.
Step seven: inputting various input data of the aviation equipment needing availability prediction, and calculating through a neural network to obtain an output value, namely the predicted availability value of the aviation equipment.
Claims (8)
1. An aviation equipment availability prediction method based on artificial intelligence is characterized by comprising the following steps:
acquiring various input data of the aviation equipment to be tested, wherein the input data comprises equipment mean fault interval time, spare part turnover time, mean repair time, regular inspection period and overhaul period;
inputting various input data of the aviation equipment to be tested into the three-layer artificial neural network model to obtain the availability index of the aviation equipment; the three-layer artificial neural network model is trained according to training sample data of the conventional aviation equipment.
2. The method of claim 1, wherein the artificial neural network model comprises an input layer, a hidden layer and an output layer, wherein the number of inputs of the input layer is 5, and the number of nodes of the hidden layer is 3.
3. The method of claim 1, further comprising:
collecting and counting average fault interval time, spare part turnover time, average repair time, regular inspection period and overhaul period of the conventional aviation equipment as training sample data, and using availability data as training result samples;
training an initial artificial neural network model through training sample data to train an available artificial neural network.
4. The method of claim 3, wherein training the initial artificial neural network model with training sample data to train an available artificial neural network comprises:
dividing training sample data and corresponding training sample results into n groups;
correcting the initial artificial neural network model according to each set of training sample data and corresponding training sample results in sequence by taking a group as a unit to obtain a first weight correction factor from a hidden layer to an output layer and a second weight correction factor from an input layer to the hidden layer;
according to the weight, the first weight correction factor and the second weight correction factor, correcting the parameters of the corresponding layer of the artificial neural network model; wherein the weight is composed of a group number and a group number used for training.
5. An artificial intelligence based aviation equipment availability prediction device, comprising:
the acquisition module is used for acquiring various input data of the aviation equipment to be detected, wherein the input data comprises equipment average fault interval time, spare part turnover time, average repair time, regular inspection period and overhaul period;
the input module is used for inputting various input data of the aviation equipment to be tested into the three-layer artificial neural network model to obtain the availability index of the aviation equipment; the three-layer artificial neural network model is trained according to training sample data of the conventional aviation equipment.
6. The apparatus of claim 5, wherein the artificial neural network model comprises an input layer, a hidden layer and an output layer, wherein the number of inputs of the input layer is 5, and the number of nodes of the hidden layer is 3.
7. The apparatus of claim 5, further comprising:
the collection module is used for collecting and counting average fault interval time, spare part turnover time, average repair time, regular inspection period and overhaul period of the conventional aviation equipment as training sample data, and using the availability data as a training result sample;
and the training module is used for training the initial artificial neural network model through training sample data to train an available artificial neural network.
8. The apparatus of claim 7, wherein the training module comprises:
the grouping unit is used for dividing the training sample data and the corresponding training sample results into n groups;
the correction unit is used for correcting the initial artificial neural network model according to each set of training sample data and the corresponding training sample result in sequence by taking a set as a unit to obtain a first weight correction factor from the hidden layer to the output layer and a second weight correction factor from the input layer to the hidden layer; according to the weight, the first weight correction factor and the second weight correction factor, correcting the parameters of the corresponding layer of the artificial neural network model; wherein the weight is composed of a group number and a group number used for training.
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| CN112347560A (en) * | 2020-11-09 | 2021-02-09 | 张铮 | Aviation equipment availability prediction method and device based on artificial intelligence |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102937784A (en) * | 2012-10-30 | 2013-02-20 | 中冶南方工程技术有限公司 | Artificial neural network based method for controlling online prediction of casting billet quality |
| CN108918137A (en) * | 2018-06-08 | 2018-11-30 | 华北水利水电大学 | Fault Diagnosis of Gear Case devices and methods therefor based on improved WPA-BP neural network |
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Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102937784A (en) * | 2012-10-30 | 2013-02-20 | 中冶南方工程技术有限公司 | Artificial neural network based method for controlling online prediction of casting billet quality |
| CN108918137A (en) * | 2018-06-08 | 2018-11-30 | 华北水利水电大学 | Fault Diagnosis of Gear Case devices and methods therefor based on improved WPA-BP neural network |
Non-Patent Citations (2)
| Title |
|---|
| 于宗艳等: "基于人工神经网络的航空发动机故障诊断方法", 《现代电子技术》, vol. 36, no. 2, 15 January 2013 (2013-01-15), pages 65 - 67 * |
| 左力: "基于CPNN的航空装备维修保障能力评估研究", 《电子设计工程》, vol. 24, no. 23, 5 December 2016 (2016-12-05), pages 7 - 9 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112347560A (en) * | 2020-11-09 | 2021-02-09 | 张铮 | Aviation equipment availability prediction method and device based on artificial intelligence |
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