Methods and systems for analyzing engine unbalance conditions
원문보기
IPC분류정보
국가/구분
United States(US) Patent
등록
국제특허분류(IPC7판)
G01M-001/38
G01M-001/00
출원번호
US-0708208
(2007-02-20)
등록번호
US-7400943
(2008-07-15)
발명자
/ 주소
Vian,John L.
Travis,Matt H.
출원인 / 주소
The Boeing Company
대리인 / 주소
Richardson, P.S.,Robert R.
인용정보
피인용 횟수 :
8인용 특허 :
12
초록▼
Methods and systems for analyzing engine unbalance conditions are disclosed. In one embodiment, a method includes receiving vibrational data from a plurality of locations distributed over an engine and a surrounding engine support structure, and inputting the vibrational data into a neural network i
Methods and systems for analyzing engine unbalance conditions are disclosed. In one embodiment, a method includes receiving vibrational data from a plurality of locations distributed over an engine and a surrounding engine support structure, and inputting the vibrational data into a neural network inverse model. The neural network inverse model establishes a relationship between the vibrational data and an unbalance condition of the engine, and outputs diagnostic information indicating the unbalance condition of the engine. In a further embodiment, a method further includes subjecting the vibrational data to a Fast Fourier Transformation to extract a desired once per revolution vibrational data prior to input to the neural network inverse model.
대표청구항▼
What is claimed is: 1. A system for analyzing an engine unbalance condition, comprising: a control component; an input/output device adapted to receive vibrational data; and a processor arranged to analyze the vibrational data, the processor including: a first portion adapted to receive vibrational
What is claimed is: 1. A system for analyzing an engine unbalance condition, comprising: a control component; an input/output device adapted to receive vibrational data; and a processor arranged to analyze the vibrational data, the processor including: a first portion adapted to receive vibrational data from a plurality of locations distributed over at least one of an engine and surrounding engine support structure; a second portion adapted to input the vibrational data into a neural network inverse model; a third portion adapted to establish a relationship between the vibrational data from the plurality of locations and the engine unbalance condition using the neural network inverse model; and a fourth portion adapted to output diagnostic information from the neural network inverse model, wherein the diagnostic information indicates at least one of the engine unbalance condition and information indicating the quantity and position of corrective engine balance weights needed to achieve desirable vibrational characteristics at the plurality of locations, and wherein the diagnostic information includes an unbalance magnitude and an angular location as a function of a rotational frequency of the engine. 2. The system of claim 1, wherein the second portion is adapted to input the vibrational data in a time domain format into a neural network inverse model. 3. The system of claim 1, wherein the second portion is adapted to input the vibrational data in a complex domain format into a neural network inverse model. 4. The system of claim 1, wherein at least one of the first, second, and third portions is adapted to subject the vibrational data to a Fast Fourier Transformation. 5. The system of claim 1, wherein at least one of the first, second, and third portions is adapted to extract a desired once per revolution vibrational data. 6. The system of claim 1, wherein at least one of the first, second, and third portions is adapted to subject the vibrational data to a Wavelet Transformation. 7. The system of claim 1, wherein the third portion is adapted to establish a relationship between the vibrational data from the plurality of locations and an unbalance condition of the engine using at least one of a multilayer perceptron neural network model, and a support vector machine neural network model. 8. The system of claim 1, wherein the third portion is adapted to establish a relationship between the vibrational data from a plurality of locations within one defined area to that of a plurality of locations within another defined area using at least one of a multilayer perceptron neural network model and a support vector machine neural network model. 9. The system of claim 1, wherein the third portion is adapted to be trained including adjusting model parameters such that application of a set of inputs and outputs reaches a desired state of definition defined by acceptable error tolerances. 10. The system of claim 1, wherein the third portion is adapted to be trained including using vibrational data generated using an engine that is subject to at least one of residual unbalances and applied trial weight unbalances. 11. The system of claim 1, wherein the third portion is adapted to be trained including scaling the vibrational training data prior to inputting into the neural network inverse model. 12. The system of claim 1, further including a memory component operatively coupled to at least one of the control component, the input/output device, and the processor. 13. The system of claim 1, further including a data acquisition component operatively coupled to at least one of the control component, the input/output device, and the processor. 14. The system of claim 13, wherein the data acquisition component includes a plurality of data acquisition sensors.
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