CN110844111B - Multi-characteristic index bevel gear health state assessment method - Google Patents

Multi-characteristic index bevel gear health state assessment method Download PDF

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CN110844111B
CN110844111B CN201910965903.6A CN201910965903A CN110844111B CN 110844111 B CN110844111 B CN 110844111B CN 201910965903 A CN201910965903 A CN 201910965903A CN 110844111 B CN110844111 B CN 110844111B
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熊天旸
李新民
金小强
张先辉
孙伟
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China Helicopter Research and Development Institute
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B64—AIRCRAFT; AVIATION; COSMONAUTICS
    • B64F—GROUND OR AIRCRAFT-CARRIER-DECK INSTALLATIONS SPECIALLY ADAPTED FOR USE IN CONNECTION WITH AIRCRAFT; DESIGNING, MANUFACTURING, ASSEMBLING, CLEANING, MAINTAINING OR REPAIRING AIRCRAFT, NOT OTHERWISE PROVIDED FOR; HANDLING, TRANSPORTING, TESTING OR INSPECTING AIRCRAFT COMPONENTS, NOT OTHERWISE PROVIDED FOR
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Abstract

The invention belongs to the technical field of helicopter health monitoring and fault diagnosis, and discloses a multi-characteristic index bevel gear health state assessment method, which comprises the following steps: acquiring first vibration test data of a normal gear and second vibration test data of a specific fault gear; calculating the root mean square of the test data; calculating skewness of the test data; calculating a state index of the test data based on the side band analysis; calculating the state index of the differential signal of the test data; acquiring the sensitivity of a root mean square, a skewness, a state index based on side band analysis and a state index of a differential signal to a specific fault respectively; and (4) evaluating the health state of the bevel gear by using the index with high sensitivity as the evaluation index of the specific fault.

Description

Multi-characteristic index bevel gear health state assessment method
Technical Field
The invention belongs to the technical field of helicopter health monitoring and fault diagnosis, and particularly relates to a multi-characteristic-index bevel gear health state assessment method.
Background
Statistically, of the helicopter flight accidents caused by mechanical failures, 68% of the accidents can be attributed to failures of the power plant and the transmission system. The helicopter tail transmission system is used as an important component of a transmission system, has a single-channel complex structure without redundancy, has serious fault consequences, is high in complexity and integration level, difficult to maintain and guarantee and high in maintenance cost, and is very necessary for fault diagnosis.
At present, the research on gears at home and abroad is mainly developed from time domains, frequency domains and time-frequency domains, specifically, the meshing frequency and harmonic components thereof, the rotating speed frequency and harmonic components thereof of the gears are obtained by related methods, the amplitudes corresponding to the corresponding frequencies of the normal state and the fault state are analyzed, and the effectiveness of the method is verified. Three people such as stuart, mafaden and zak Lei Ji respectively publish three classic works in the related field; the methods for identifying the health state of gears are summarized by Denposi et al, and the application of these methods to FAA and American land military helicopters is discussed with emphasis, and vibration-based gear fault detection is currently improved abroad, but the empirical data for checking fault detection algorithms is very small.
Disclosure of Invention
Aiming at the problems, the invention aims to provide a multi-characteristic index bevel gear health state assessment method, which removes the influence of environmental noise by a time domain synchronous average preprocessing method; different characteristic indexes are calculated, and the difference of the index values in the healthy state and the fault state is compared, so that the purpose of evaluating the healthy state of the bevel gear is achieved.
In order to achieve the purpose, the invention is realized by adopting the following technical scheme.
A multi-feature index bevel gear state of health assessment method comprises the following steps:
acquiring first vibration test data of a normal gear and second vibration test data of a specific fault gear;
calculating the root mean square of the first vibration test data and the root mean square of the second vibration test data;
calculating the skewness of the first vibration test data and the skewness of the second vibration test data;
calculating a state index of the first vibration test data based on the sideband analysis and a state index of the second vibration test data based on the sideband analysis;
calculating a state index of a differential signal of the first vibration test data and a state index of a differential signal of the second vibration test data;
acquiring the sensitivity of a root mean square, a skewness, a state index based on side band analysis and a state index of a differential signal to a specific fault respectively;
and (4) evaluating the health state of the bevel gear by using the index with high sensitivity as the evaluation index of the specific fault.
The technical scheme of the invention has the characteristics and further improvements that:
(1) After first vibration test data of a normal gear and second vibration test data of a specific fault gear are obtained, time domain synchronous average (TSA) preprocessing is carried out on the first vibration test data and the second vibration test data to obtain N groups of data of the normal gear and N groups of data of the specific fault gear.
(2) Calculating the root mean square of the first vibration test data and the root mean square of the second vibration test data, wherein the root mean square is specifically calculated as follows:
Figure BDA0002229781390000021
in the formula, x is a data sequence acquired once, i is a data acquisition batch, and N is the number of times of data acquisition once.
(3) Calculating the skewness of the first vibration test data and the skewness of the second vibration test data, wherein the skewness is specifically calculated as follows:
Figure BDA0002229781390000031
in the formula, x is a data sequence acquired once, i is a data acquisition batch, and N is the number of times of data acquisition once.
Figure BDA0002229781390000032
Is the mean of the data sequence x.
(4) Calculating the state index based on the sideband analysis of the first vibration test data and the state index based on the sideband analysis of the second vibration test data, wherein the state index based on the sideband analysis is the 1 st order index DA of the data algorithm 1 ，DA 1 = RMS (TSA-mean (TSA)), TSA represents TSA-processed data, mean () represents averaging, and RMS () represents root mean square.
(5) Calculating the state index of the differential signal of the first vibration test data and the state index of the differential signal of the second vibration test data, wherein the state index of the differential signal comprises FM 4 M6A, M A, the differential signal is obtained by filtering out the first-order side band of the meshing frequency and the harmonic component after TSA processing of the original signal,
Figure BDA0002229781390000033
wherein d is i For the ith data in the differential signal,
Figure BDA0002229781390000034
the mean value of the differential signal is shown, and N is the number of data acquisition.
(6) The method comprises the following steps of obtaining the sensitivity of a root mean square, a skewness, a state index based on side band analysis and a state index of a differential signal to a specific fault respectively, specifically:
drawing numerical curves of root mean square, skewness, state indexes based on side frequency band analysis and state indexes of differential signals corresponding to multiple groups of data of the normal gears and multiple groups of data of the specific fault gears;
and if the numerical curve under the specific fault gear corresponding to a certain index does not have an intersection point with the numerical curve under the normal gear, the index is considered to have high sensitivity to the specific fault.
(7) The specific fault at least comprises: the gear comprises a crack fault gear with the depth of 25%, a crack fault gear with the depth of 50%, a tooth surface denudation fault gear with the depth of 5%, a tooth surface denudation fault gear with the depth of 10%, a tooth-profile-lack 1/4 broken-tooth fault gear and a tooth-profile-lack 1/2 broken-tooth fault gear.
The invention has the beneficial effects that: removing the influence of environmental noise by a time domain synchronous average preprocessing method; different characteristic indexes are calculated, and the difference between the index values in the healthy state and the fault state is compared, so that the purpose of evaluating the healthy state of the bevel gear is achieved; finally, the method is verified by implanting a set of fault implant "blind" data.
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Fig. 1 is a first schematic flow chart of a multi-characteristic index bevel gear health state assessment method according to an embodiment of the present invention;
fig. 2 is a schematic flow chart diagram of a health state evaluation method for a multi-characteristic index bevel gear according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of RMS index curves for normal and various fault conditions in accordance with an embodiment of the present invention;
FIG. 4 is a schematic diagram of SKEW index curves for normal and various fault states according to an embodiment of the present invention;
FIG. 5 is a diagram illustrating an FM4 index curve for a normal state and various fault states according to an embodiment of the present invention;
fig. 6 is a schematic diagram of an M6A index curve in a normal state and various fault states according to an embodiment of the present invention;
FIG. 7 is a schematic diagram of an M8A index curve for a normal state and various fault states according to an embodiment of the present invention;
FIG. 8 is a diagram illustrating DA1 index curves for normal and various fault states according to an embodiment of the present invention;
FIG. 9 is a schematic diagram of an RMS index curve for a normal state and a blind state according to an embodiment of the invention;
FIG. 10 is a schematic diagram of SKEW index curves for normal and various fault states according to an embodiment of the present invention;
fig. 11 is a schematic diagram of an FM4 index curve in a normal state and a blind state according to an embodiment of the present invention;
fig. 12 is a schematic diagram of an M6A index curve in a normal state and a blind state according to an embodiment of the present invention;
fig. 13 is a schematic diagram of an M8A index curve in a normal state and a blind state according to an embodiment of the present invention;
fig. 14 is a diagram illustrating a DA1 index curve in a normal state and a blind state according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Due to the structural characteristics of the tail reducer of the helicopter, the sensor cannot be directly installed at a point to be measured and is installed on the shell of the reducer, so that the early fault characteristics are weak, the sensor is difficult to accurately extract the tail reducer from the strong noise background of the tail reducer, meanwhile, vibration information measured by the sensor on the shell of the tail reducer is interfered and overlapped mutually, so that information of each part of the tail reducer is difficult to separate, and for the information, a common wavelet packet analysis or time domain synchronization preprocessing method is generally used. However, compared with the practical application in wavelet transformation, which needs to judge and select a proper basis function according to experience, the time domain synchronous averaging (TSA) technology can more simply and effectively extract periodic signals from signals mixed with noise interference, and improve the signal-to-noise ratio.
The method utilizes the multi-characteristic indexes to evaluate the health state of the bevel gear. Firstly, TSA processing is carried out on an original signal to extract a periodic signal from a signal mixed with noise interference, and the signal-to-noise ratio is improved. And then, processing and analyzing the signals according to the indexes of the health states to obtain the sensitivity of the indexes to the fault states. And finally, verifying the validity of the result through further experimental data analysis.
The bevel gear of the tail transmission system is affected by factors such as the installation position and the structure of the bevel gear, so that faults such as gear corrosion, cracks, tooth breakage and the like often occur.
The present invention is described in further detail below.
The gear fault implantation test is carried out on a tail transmission fault implantation test bed under the state of a 90-degree included angle, the rotating speed is controlled to be 4000rpm, the torque of an output end is respectively 0, 25%, 50%, 75% and 100% of the total output torque, the output end gear test pieces are respectively 7 test pieces including a normal gear, a depth of 25%, a depth of 50% of a crack fault gear, a 5% tooth surface denudation fault gear, a 10% tooth surface denudation fault gear, a tooth-profile-lack 1/4 tooth-broken fault gear and a tooth-profile lack 1/2 tooth-broken fault gear, 35 test states are adopted, 6-minute data are collected under each stable state, and the sampling rate is 25K/s. In order to ensure the accuracy of the data, each state has one repeated test.
The vertical vibration data at the output gear is exemplified by a 75% torque. And (3) preprocessing the test data, resampling to 256 points per circle by adopting 1 minute data of each state, and performing time domain synchronous averaging (TSA) on a group of 30 circles to obtain 10 groups of data of each state. The TSA processed data removes background noise and has more regularity. The specific process refers to fig. 2.
Using a state indicator, root Mean Square (RMS) (the formula is:
Figure BDA0002229781390000061
where x is a data sequence acquired once, i is a data acquisition batch, and N is the number of times of data acquisition once) respectively obtaining the RMS of 10 groups of data in different states, plotting, and analyzing the difference between the RMS values in the fault state and the RMS value in the normal state to obtain the sensitivity of the RMS to each fault state.
Using the state index Skewness (SKEW) (the formula is:
Figure BDA0002229781390000062
) And respectively obtaining SKEW of 10 groups of data in different states, drawing, and analyzing the difference and the sameness of SKEW values in the fault state and the normal state to obtain the sensitivity degree of SKEW to each fault state.
The data algorithm 1 order index (DA 1) which is a state index based on the side band analysis is utilized (the formula is DA 1) 1 Where TSA represents data processed by TSA), 10 sets of data DA1 in different states are obtained, plotted, and the difference between the values of DA1 in the failure state and the values of DA1 in the normal state is analyzed to obtain the sensitivity of DA1 to each failure state.
After the original signal is processed by TSA, the first order side band of the meshing frequency and the harmonic component is filtered to obtain a differential signal. The state index of the differential signal, FM4 (the formula is:
Figure BDA0002229781390000071
di is the ith data in the differential signal and represents the mean value of the differential signal, N is the total point number of the differential signal time record) respectively obtain 10 groups of data FM4 of different states, drawing, analyzing the difference and sameness of the FM4 values in the fault state and the normal state, and obtaining the sensitivity degree of the FM4 to each fault state.
Using the state index of the differential signal, M6A (the formula is:
Figure BDA0002229781390000072
di is the ith data in the differential signal and represents the mean value of the differential signal, N is the total number of points recorded in time of the differential signal) to respectively obtain M6A of 10 groups of data in different states, drawing, and analyzing the difference and sameness of the M6A values in the fault state and the normal state to obtain the sensitivity degree of the M6A to each fault state.
Using the state index of the differential signal, M8A (the formula is:
Figure BDA0002229781390000073
di is the ith data in the differential signal and represents the mean value of the differential signal, N is the total number of points recorded in time of the differential signal) to respectively obtain M8A of 10 groups of data in different states, drawing, and analyzing the difference and sameness of the M8A values in the fault state and the normal state to obtain the sensitivity degree of the M8A to each fault state.
The obtained M6A, M A is sensitive to the denudation fault; RMS and DA1 are sensitive to the crack fault of the gear; FM4 is sensitive to tooth breakage failures.
Based on the research on the health state evaluation method of the tail bevel gear, the health state of the bevel gear corresponding to the blind data can be judged by analyzing the blind data (the blind data refers to a group of data of which the health state is unknown in advance), and the blind data is compared with the real situation to verify the effectiveness of the evaluation method. As can be seen from fig. 2, the normal state of the 10 groups of data intersects with the RMS value range of the ablation and tooth breakage fault states, and there is no obvious intersection with the crack fault, that is, the RMS is very sensitive to the crack fault of the gear, and the larger the crack degree is, the more sensitive it is. The same can be derived, fig. 3: SKEW is insensitive to the corrosion, crack and tooth breakage of the gear; FIG. 4 is a schematic view of: FM4 is sensitive to tooth breakage faults and insensitive to denudation and crack faults; FIG. 5 is a schematic view of: M6A is more sensitive to the denudation fault and is not sensitive to the fault of broken teeth and cracks; FIG. 6: M8A is more sensitive to the denudation fault and is not sensitive to the fault of broken teeth and cracks; FIG. 7: DA1 is not sensitive to stripping and broken tooth faults and is sensitive to crack faults.
Fig. 8-9 are the results of analysis of "blind" data: it can be seen from the figure that the values of M6A, SKEW and M8A, FM in the normal state and the "blind" state are not very different, but the values of RMS and DA1 are very different, and the "blind" state can be determined to be the crack fault state of the bevel gear through the above research, and the actual state is the fault state of 50% of cracks, and the determined state is consistent with the actual state.
The embodiment of the invention provides a method for evaluating the health state of a bevel gear by utilizing multi-feature indexes, which removes the influence of environmental noise by a time domain synchronous average preprocessing method; different characteristic indexes are calculated, and the difference between the index values in the healthy state and the fault state is compared, so that the purpose of evaluating the healthy state of the bevel gear is achieved; finally, the method is verified by implanting a set of fault implant "blind" data.
The foregoing is illustrative of the present invention and is not to be construed as limiting thereof. The scope of the present invention is not limited thereto, and any changes or substitutions that can be easily made by those skilled in the art within the technical scope of the present invention will be covered by the scope of the present invention. The protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (6)

1. A multi-characteristic index bevel gear health state assessment method is characterized by comprising the following steps:
acquiring first vibration test data of a normal gear and second vibration test data of a specific fault gear; the first vibration test data and the second vibration test data are data subjected to time domain synchronous average TSA preprocessing;
calculating the root mean square of the first vibration test data and the root mean square of the second vibration test data;
calculating the skewness of the first vibration test data and the skewness of the second vibration test data;
calculating a state index of the first vibration test data based on the sideband analysis and a state index of the second vibration test data based on the sideband analysis;
calculating a state index of a differential signal of the first vibration test data and a state index of a differential signal of the second vibration test data;
the method comprises the following steps of obtaining the sensitivity of a root mean square, a skewness, a state index based on side band analysis and a state index of a differential signal to a specific fault respectively, specifically:
drawing numerical curves of root mean square, skewness, state indexes based on side frequency band analysis and state indexes of differential signals corresponding to multiple groups of data of the normal gears and multiple groups of data of the specific fault gears;
if the numerical curve under the specific fault gear corresponding to a certain index does not have an intersection point with the numerical curve under the normal gear, the index is considered to have high sensitivity to the specific fault;
and (4) evaluating the health state of the bevel gear by using the index with high sensitivity as the evaluation index of the specific fault.
2. The method for evaluating the health state of the multi-characteristic index bevel gear according to claim 1, wherein the root mean square of the first vibration test data and the root mean square of the second vibration test data are calculated, and the root mean square is specifically calculated as follows:
Figure FDA0003712309070000021
in the formula, x is a data sequence acquired once, i is a data acquisition batch, and N is the number of times of data acquisition once.
3. The method for evaluating the health state of the bevel gear with the multiple characteristic indexes according to claim 1, wherein the skewness of the first vibration test data and the skewness of the second vibration test data are calculated, and the skewness is specifically calculated as follows:
Figure FDA0003712309070000022
wherein x is a data sequence acquired once, i is a data acquisition batch, N is the number of times of data acquisition once,
Figure FDA0003712309070000023
is the mean of the data sequence x.
4. The method for evaluating the health status of bevel gears with multiple characteristic indexes according to claim 1, wherein the state index based on the sideband analysis of the first vibration test data and the state index based on the sideband analysis of the second vibration test data are calculated as the 1 st order index DA of the data algorithm 1 ，DA 1 = RMS (TSA-mean (TSA)), TSA denotes data after TSA processing, mean () denotes averaging, RMS (.) denotes root mean square.
5. The method of claim 1, wherein the state index of the differential signal of the first vibration test data and the state index of the differential signal of the second vibration test data are calculated, and the state index of the differential signal comprises FM 4 M6A, M A, the differential signal is obtained by filtering out the first-order side band of the meshing frequency and the harmonic component after TSA processing of the original signal,
Figure FDA0003712309070000024
wherein, d i For the ith data in the differential signal,
Figure FDA0003712309070000025
the mean value of the differential signal is shown, and N is the number of data acquisition.
6. The method for evaluating the health status of a multi-characteristic index bevel gear according to claim 1, wherein the specific fault at least comprises: the gear comprises a crack fault gear with the depth of 25%, a crack fault gear with the depth of 50%, a tooth surface denudation fault gear with the depth of 5%, a tooth surface denudation fault gear with the depth of 10%, a tooth-profile-lack 1/4 broken-tooth fault gear and a tooth-profile-lack 1/2 broken-tooth fault gear.
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