CN117574077A - Helicopter health monitoring data acquisition processing method and storage medium - Google Patents
Helicopter health monitoring data acquisition processing method and storage medium Download PDFInfo
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- CN117574077A CN117574077A CN202311486501.0A CN202311486501A CN117574077A CN 117574077 A CN117574077 A CN 117574077A CN 202311486501 A CN202311486501 A CN 202311486501A CN 117574077 A CN117574077 A CN 117574077A
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- 230000036541 health Effects 0.000 title claims abstract description 21
- 238000012544 monitoring process Methods 0.000 title claims abstract description 15
- 238000003672 processing method Methods 0.000 title claims abstract description 10
- 230000005540 biological transmission Effects 0.000 claims abstract description 20
- 238000007781 pre-processing Methods 0.000 claims abstract description 8
- 238000000034 method Methods 0.000 claims description 37
- 230000008569 process Effects 0.000 claims description 16
- 238000012935 Averaging Methods 0.000 claims description 13
- 230000001360 synchronised effect Effects 0.000 claims description 11
- 238000005070 sampling Methods 0.000 claims description 9
- 238000012952 Resampling Methods 0.000 claims description 6
- 238000012545 processing Methods 0.000 claims description 6
- 230000006798 recombination Effects 0.000 claims description 4
- 238000005215 recombination Methods 0.000 claims description 4
- 230000011218 segmentation Effects 0.000 claims description 3
- 230000001133 acceleration Effects 0.000 claims description 2
- 230000009194 climbing Effects 0.000 claims description 2
- 238000004590 computer program Methods 0.000 claims description 2
- 230000000630 rising effect Effects 0.000 claims description 2
- 230000003862 health status Effects 0.000 abstract description 2
- 238000013528 artificial neural network Methods 0.000 description 12
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- 238000004364 calculation method Methods 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 239000010687 lubricating oil Substances 0.000 description 2
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Classifications
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B64—AIRCRAFT; AVIATION; COSMONAUTICS
- B64D—EQUIPMENT FOR FITTING IN OR TO AIRCRAFT; FLIGHT SUITS; PARACHUTES; ARRANGEMENTS OR MOUNTING OF POWER PLANTS OR PROPULSION TRANSMISSIONS IN AIRCRAFT
- B64D45/00—Aircraft indicators or protectors not otherwise provided for
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B64—AIRCRAFT; AVIATION; COSMONAUTICS
- B64D—EQUIPMENT FOR FITTING IN OR TO AIRCRAFT; FLIGHT SUITS; PARACHUTES; ARRANGEMENTS OR MOUNTING OF POWER PLANTS OR PROPULSION TRANSMISSIONS IN AIRCRAFT
- B64D45/00—Aircraft indicators or protectors not otherwise provided for
- B64D2045/0085—Devices for aircraft health monitoring, e.g. monitoring flutter or vibration
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
Abstract
The invention belongs to the technical field of helicopter health status and use management systems, and relates to a helicopter health monitoring data acquisition and processing method, which comprises the following steps: step one: identifying the flight state of the helicopter; step two: when the helicopter enters any flight state, starting data acquisition; step three: the data acquisition comprises the following steps: main rotor speed, tail rotor speed, main rotor vibration value, tail rotor vibration value and transmission system vibration value; step four: preprocessing the collected data and storing the preprocessed data.
Description
Technical Field
The invention belongs to the technical field of helicopter health status and use management systems, and relates to a helicopter health monitoring data acquisition and processing method and a storage medium.
Background
From the last 80 th century, the state of the United states, yingmei, has actively developed HUMS to perform health monitoring, fault diagnosis and prediction on key components such as gear boxes, rotors and engines. In order to realize health monitoring of the helicopter, health management data of the helicopter need to be collected and processed. McFadden proposes a time-synchronized averaging technique to process vibration data of a helicopter transmission system to reduce the effects of background noise and other components. Marcos e.orcard et al uses a particle filter method to propose a method that can be used for on-line fault diagnosis and prediction, and they proposed a method that verifies the fault experiment on the UH-60 helicopter planetary gear.
At present, the research on the fault diagnosis of the helicopter transmission mechanism at home and abroad still adopts data collected in the whole process of the helicopter flight, the data is mixed with the background noise of each part on the helicopter, and the useful signals are greatly interfered. In addition, data are collected in the whole process in the flight process, the data size is extremely large, redundant invalid data are large, too much manpower and material resources are needed to process the data, and the efficiency is low.
Disclosure of Invention
The invention aims to provide a helicopter health monitoring data acquisition processing method, which can greatly reduce invalid data acquisition and improve the utilization rate of data by identifying the flight state of a helicopter and carrying out data acquisition in an effective period. And simultaneously, the method for synchronously averaging the angular frequency domain of the rotating component is combined to process the data, so that the signal to noise ratio of the signal can be obviously improved, and the signal characteristics of the helicopter component are enhanced.
The technical scheme is as follows:
a helicopter health monitoring data acquisition and processing method comprises the following steps:
step one: identifying the flight state of the helicopter;
step two: when the helicopter enters any flight state, starting data acquisition;
step three: the data acquisition comprises the following steps: main rotor speed, tail rotor speed, main rotor vibration value, tail rotor vibration value and transmission system vibration value;
step four: preprocessing the collected data and storing the preprocessed data.
Further, in the first step, the helicopter flight state is identified based on the radial basis function neural network.
Further, in the first step, the identifying the input parameters of the helicopter flight state includes:
further, in the first step, the helicopter flight state includes: ground operation, vertical take-off, acceleration, climbing, flat flight and landing.
In the third step, the output signals of the main rotor rotating speed sensor and the tail rotor rotating speed sensor are square wave signals;
the main rotor rotation speed and tail rotor rotation speed acquisition process is as follows:
counting clock pulses at the rising edge of the square wave signal, and stopping counting at the falling edge of the square wave signal;
the rotating speed value is obtained according to the counting value and the clock pulse period, and the formula is as follows:
rotational speed= (clock frequency/count value) ×60, unit r/min.
Further, in the third step, the main rotor vibration value and the tail rotor vibration value are acquired as follows:
and vibration data of X1 points are collected through a vibration sensor every time the main rotor or the tail rotor rotates, the data of 128 circles of main rotor rotation are taken as a data set, and X1 changes along with the rotating speed of the main rotor or the tail rotor.
Further, the sampling rate of the vibration value of each part of the transmission system is in direct proportion to the rotating speed of each part of the transmission system; each part rotates for one circle to collect vibration data of X2 points, and X2 changes along with the rotation speed of each part of the transmission system; one component rotates the vibration data for 128 turns as one data set.
Further, in the fourth step, the preprocessing refers to preprocessing the vibration value of the main rotor wing, the vibration value of the tail rotor and the vibration value of the transmission system, and the process is as follows:
data segmentation: dividing each group of vibration data into 128 blocks according to pulses output by the rotation speed sensor when each component rotates for one circle;
resampling: resampling the 128 blocks of data entirely to 256 points per block of vibration data and recombining;
angular domain averaging: and carrying out average processing on each group of recombined data, wherein the formula is as follows:
wherein x is TSA (t) is an angular frequency domain synchronous average signal, x (t) is a vibration signal after recombination, N is the number of data groups, L is the number of data points contained in each rotation, after recombination, the number is not 256, T s For sampling intervals, period T of vibration signal x (T) 0 ＝LT s 。
A computer readable storage medium having stored thereon a computer program which, when executed by a processor of a computer, causes the computer to perform the helicopter health monitoring data acquisition processing method.
The beneficial effects are that:
the invention provides a helicopter health monitoring data acquisition and processing method, which can effectively reduce the number of acquired data, improve the duty ratio of effective data, improve the signal to noise ratio of the data, save the processing time of the data and provide a good technical route for acquiring the data of the helicopter health monitoring.
Drawings
FIG. 1 is a schematic diagram of a rotational speed signal acquisition;
FIG. 2 is a schematic diagram of a rotation angle frequency domain synchronous average;
FIG. 3 is a schematic diagram of the flight parameters employed;
FIG. 4 is a schematic diagram of a flight status recognition result;
FIG. 5 is a schematic diagram of an acquired vibration signal;
fig. 6 is a schematic diagram of a spectrum of a vibration signal after angular domain averaging.
Detailed Description
In the flight process of the helicopter, due to different flight tasks, the loaded cargo has different weights and the like, and different flight states exist in different flight phases. The values of the data of the rotational speed, vibration, lubricating oil pressure, temperature, etc. of the various parts of the helicopter are also varied under different flight conditions. The health state of the helicopter is monitored, and various parameters are required to be collected for analysis. The same parameter also has larger variation in different flight states, and the combination analysis with the flight state of the helicopter is needed.
The health management data acquisition method provided by the invention comprises the steps of firstly, correctly identifying the flight state of the helicopter after the helicopter performs a certain flight state; after the flight state is stable, starting a health management system to acquire vibration data and rotation speed data in the flight state, and acquiring the acquired data by adopting different acquisition frequencies according to different data sources; preprocessing the collected rotation speed data and vibration data by adopting a rotation angle frequency domain synchronous averaging technology, reducing vibration data noise and improving signal to noise ratio; and finally, storing the processed rotating speed data and vibration data into a data recording control box.
1. Flight status identification
During the flight of a helicopter, there are many flight parameters such as altitude, speed, engine torque, yaw angle, sideslip angle, etc. These flight parameters are constantly changing. The flight state of the helicopter is defined by the parameter values, and the defined flight state generally comprises various flight states such as ground running, running and taking off, effective hovering, non-effective hovering, flat flight, side flight and the like. When a helicopter enters a certain flight state, the value of its flight parameters is relatively stable. In the flight process of the helicopter, a method for identifying the flight state of the helicopter by adopting a Radial Basis Function (RBF) neural network is adopted. The input parameters of the neural network method are shown in table 1.
TABLE 1 flight parameters relating to the identification of flight status
The values of the parameters in table 1 were collected as inputs to the neural network. Judging which cluster the input parameters belong to, establishing a neural network for each cluster, calling out the neural network data, initializing the neural network, then according to the difference of the characteristics of each subclass, extracting the data representing the difference of the internal states of each subclass, removing the wild points, smoothing, and the data (such as yaw angle) still need to be derived (judging whether to turn). After this has been done, the data is normalized and then helicopter flight status identification is performed.
The neural network is designed according to the following rules:
1) The dimension of the neural network entry vector is the number of the characteristic parameters of the subclass;
2) The dimension of the neural network exit vector is the number of flight states of the subclass;
3) The number of hidden layer nodes of the neural network is about 3 times of the number of the characteristic parameters of the subclass;
4) The outlet vector of the neural network is given according to 0 and 1 during training, the corresponding states are given 1, and the rest are given 0;
5) In the identification, the outlet vector is identified by judging which dimension is close to 1 and which dimension is closest to 1, and the data is in a state corresponding to the dimension.
After calculation, it is determined which flight state the helicopter is in, see in particular table 2. When the helicopter is in a certain flight state for more than 1 second, the data acquisition function of the health management system is started.
Table 2 list of flight status of helicopters
2. Data acquisition
The health management system automatically starts the data acquisition function when the helicopter enters a certain flight state and is stable. The main data that gathers have main rotor rotational speed, tail rotor rotational speed, main rotor vibration value, tail rotor vibration value and transmission system's vibration value.
(1) Data acquisition of rotational speed
The health management system collects rotational speed data by adopting frequency of 51.2kHz
As shown in the figure, the output of the rotation speed sensor signal is in the form of square wave after being modulated, and is counted by a fixed clock pulse far greater than the frequency of the sensor signal, so as to obtain the period:
and reading the clock pulse number, removing the abnormal value, averaging, and replacing the abnormal value with the average value.
The rotation speed calculation formula: rotational speed= (50×10) 6 Number of clock pulses) ×60;
50×10 6 a clock pulse of 50MHz, a clock for counting sensor pulse signals;
60: the rotation speed unit r/s is converted into r/min.
The main rotor speed and the tail rotor speed are obtained through the above form.
(2) Main rotor vibration value and tail rotor vibration value acquisition
According to the rotational speed of main rotor wing rotational speed sensor survey, main rotor wing rotates every round, gathers 256 vibration data of some points. Vibration data of 128 rotations of the main rotor is used as a data set.
And according to the rotating speed of the tail rotor measured by the tail rotor rotating speed sensor, collecting vibration data of 256 points every time the tail rotor rotates. Vibration data of 128 turns of the tail rotor is used as a data set.
The main rotor vibration value and the tail rotor vibration value are mainly used for calculating a vibration root mean square value (RMS) and a first-order vibration amplitude (unit: ips), and a first-order vibration phase.
(3) Rotation speed and vibration value acquisition of transmission system
Monitoring content according to vibration of a transmission system: the shaft, the bearing and the gear are main monitoring objects. The main acquisition signals are: the rotation speed of the tail transmission shaft, the vibration value of a flange plate, the vibration value of a planetary gear, the vibration value of a driving shaft of a lubricating oil fan, the vibration value of an accessory of an alternating current motor and the vibration value of a planetary gear bearing are transmitted from the tail.
When the helicopter enters a stable flight state, data acquisition is automatically performed. Because the transmission system rotates more parts, the sampling frequency of each part is different. The sampling rate is 25.6kHz-102.4kHz, and different sampling frequencies are selected according to different rotating parts.
And converting according to the transmission ratio by taking the rotation speed of the tail transmission shaft as a reference to calculate the rotation speed of the corresponding rotating component. The vibration data of 256 points are collected for each rotation of the rotating member, and the vibration data of 128 rotations of the member are used as a data set.
The gear vibration value is used for calculating vibration amplitude values of first-order and second-order meshing frequencies of the rotating speed, and left and right sideband values of the first-order and second-order meshing frequency vibration signals;
the vibration value of the transmission shaft is used for calculating the vibration total effective value, the vibration residual effective value, the vibration total kurtosis and the vibration residual kurtosis, and the vibration amplitude of the first-order and second-order rotating shaft frequency.
3. Data preprocessing
The vibration data collected by the helicopter in the flight process is mixed with stronger background noise, and the collected data needs to be preprocessed before the data is stored. Because the helicopter has more rotating parts, the rotating speeds of the parts are inconsistent, and the rotating speeds of the same rotating part at different moments also change, in order to remove the background noise of vibration data, the vibration data is subjected to filtering pretreatment by adopting a method of synchronous averaging of rotation angle frequency domains.
The rotation angle frequency domain synchronous averaging technique mainly comprises three steps: and (5) data segmentation, resampling and average processing. The working principle is shown in figure 3.
In the rotation angle frequency domain synchronous averaging technique, an original vibration signal x (t) is first divided according to a rotation speed pulse signal to obtain vibration signals of each rotation period. And (3) due to the reasons of working condition change, environmental factors and the like, the number of the vibration signal data points in each rotation period is different, resampling is needed to be carried out on each piece of data, and then the angle frequency domain synchronous average signal can be obtained through average processing. The averaging process can be represented by the formula (3.1)
In which x is TSA (T) is an angular frequency domain synchronous average signal, x (T) is an original vibration signal, N is the number of data groups, namely the average times, L is the number of data points contained in each turn, T s For sampling intervals, period T of vibration signal x (T) 0 ＝LT s 。
If the signal x (T) is defined by period T 0 The periodic signal y (t) and the white noise signal n (t), the signal x (t) can be expressed as
x(t)＝y(t)+n(t) (3.2)
The original vibration signal x (T) is processed according to the period T of the periodic signal y (T) 0 Dividing to obtain N segments, and averaging the corresponding points of each data segment to obtain
The averaging process exploits the uncorrelation of white noise. Noise output at this timeThe sound being in the original signal x (t)The signal to noise ratio is improved.
The rotation angle frequency domain synchronous average technology can remove signal components which are irrelevant to a given frequency (such as the rotation speed frequency of a transmission shaft), including noise and irrelevant periodic signals, extract the periodic signals which are relevant to the given frequency, and improve the signal-to-noise ratio.
4. Data storage
And storing the preprocessed vibration data and the preprocessed rotating speed data into a data recording control box.
The helicopter health management data acquisition method provided by the invention is based on the stable flight state of the helicopter for data acquisition, and has more pertinence; different sampling rates are adopted to collect data from different sources, so that the effectiveness of the data is improved; and the rotation angle frequency domain synchronous average technology is adopted to preprocess the data process, so that the signal to noise ratio of the data is improved. The method can effectively reduce the number of acquired data, improve the duty ratio of effective data, improve the signal-to-noise ratio of the data and save the processing time of the data.
The above description of the preferred embodiments of the present invention is merely for illustration of the present invention, and it should be noted that it is possible for those skilled in the art to make several improvements and modifications without departing from the original scope of the present invention, and these improvements and modifications should also be considered as the scope of the present invention. The inventive embodiments of the present application and features in the embodiments may be combined with each other arbitrarily without conflict.
Claims (10)
1. The helicopter health monitoring data acquisition and processing method is characterized by comprising the following steps of:
step one: identifying the flight state of the helicopter;
step two: when the helicopter enters any flight state, starting data acquisition;
step three: the data acquisition comprises the following steps: main rotor speed, tail rotor speed, main rotor vibration value, tail rotor vibration value and transmission system vibration value;
step four: preprocessing the collected data and storing the preprocessed data.
2. The method of claim 1, wherein in step one, the helicopter flight status is identified based on a radial basis function network.
3. The method of claim 2, wherein in the first step, the identifying the input parameters of the helicopter flight status comprises:
4. a method according to claim 3, wherein in step one, the helicopter flight conditions comprise: ground operation, vertical take-off, acceleration, climbing, flat flight and landing.
5. The method of claim 4, wherein in step three, the main rotor speed and tail rotor speed sensor output signals are square wave signals;
the main rotor rotation speed and tail rotor rotation speed acquisition process is as follows:
counting clock pulses at the rising edge of the square wave signal, and stopping counting at the falling edge of the square wave signal;
the rotating speed value is obtained according to the counting value and the clock pulse period, and the formula is as follows:
rotational speed= (clock frequency/count value) ×60, unit r/min.
6. The method according to claim 5, wherein in the third step, the main rotor vibration value and the tail rotor vibration value are acquired as follows:
and vibration data of X1 points are collected through a vibration sensor every time the main rotor or the tail rotor rotates, the data of 128 circles of main rotor rotation are taken as a data set, and X1 changes along with the rotating speed of the main rotor or the tail rotor.
7. The method of claim 6, wherein the sampling rate at which vibration values of each component of the drive train are acquired is proportional to the rotational speed of each component of the drive train; each part rotates for one circle to collect vibration data of X2 points, and X2 changes along with the rotation speed of each part of the transmission system; one component rotates the vibration data for 128 turns as one data set.
8. The method according to claim 7, wherein in the fourth step, the preprocessing is performed on the vibration value of the main rotor, the vibration value of the tail rotor and the vibration value of the transmission system, and the process is as follows:
data segmentation: dividing each group of vibration data into 128 blocks according to pulses output by the rotation speed sensor when each component rotates for one circle;
resampling: resampling the 128 blocks of data entirely to 256 points per block of vibration data and recombining;
angular domain averaging: and carrying out average processing on each group of recombined data, wherein the formula is as follows:
wherein x is TSA (t) is an angular frequency domain synchronous average signal, x (t) is a vibration signal after recombination, N is the number of data groups, L is the number of data points contained in each rotation, after recombination, the number is not 256, T s For sampling intervals, period T of vibration signal x (T) 0 ＝LT s 。
9. The method according to claim 1, wherein in the second step, the data acquisition is started after the helicopter enters any flight state T time.
10. A computer-readable storage medium, characterized by: a computer program stored thereon, which, when executed by a processor of a computer, causes the computer to perform the helicopter health monitoring data acquisition processing method of any of claims 1-9.
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