CN112268719A - Remote fault diagnosis method for header of combine harvester - Google Patents
Remote fault diagnosis method for header of combine harvester Download PDFInfo
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Abstract
A remote fault diagnosis method for a header of a combine harvester is characterized in that a data acquisition module of a vehicle-mounted monitoring platform acquires vibration information of the header of the combine harvester through a combined sensor module, a data processing module of the vehicle-mounted monitoring platform firstly performs noise reduction processing on the vibration information to acquire more accurate information, and finally performs fault diagnosis through a data diagnosis module of the vehicle-mounted monitoring platform; the method for fault diagnosis of the vibration signal comprises the following steps: and denoising the fault signal by adopting a singular value decomposition method, carrying out information fusion, and carrying out fault diagnosis on the fused information. The invention can comprehensively collect information for the header of the combine harvester, and greatly improve the characteristic extraction effect of weak signals by using a multi-sensor fusion technology.
Description
Technical Field
The invention belongs to the technical field of combine harvesters, and particularly relates to a remote fault diagnosis system for a header of a combine harvester.
Background
The combine harvester has the advantages that the operation environment is complex, the operation failure rate is high, the combine harvester header is of great importance to the efficiency of the combine harvester, the structure of the combine harvester header is more complex compared with other agricultural machinery structures, the combine harvester header is composed of a cutter, a reel, a conveyor and a divider, vibration among all parts is influenced mutually, the height of the combine harvester needs to be adjusted continuously to adapt to the height of crops in the working state, vibration and noise can be generated while the height of the header is adjusted, the header vibration directly influences the harvesting loss rate, and the accuracy and the harvesting efficiency of the combine harvester are influenced. Remote fault diagnosis of combine headers is becoming very important.
At present, signals acquired by a single sensor are generally preprocessed for fault feature extraction of a vibration signal of a header of a combine harvester, and then a signal processing method is adopted for extracting fault features, but the header of the combine harvester is influenced by uncertain factors in the working process, and the obtained signals are weak and aliasing. Weak information in the signal, which may be critical for fault diagnosis, may be attenuated or lost during signal preprocessing. At present, no good method is available for accurately and stably diagnosing faults of a header of a combine harvester. Therefore, the method for remotely diagnosing the faults of the header of the combine harvester, which can extract weak signals, has important significance.
Disclosure of Invention
In view of the above, in order to solve the above-mentioned deficiencies of the prior art, an object of the present invention is to provide a remote fault diagnosis system for a header of a combine harvester, which can perform remote fault diagnosis on the header of the combine harvester while retaining weak information.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
a remote fault diagnosis method for a header of a combine harvester is characterized in that a data acquisition module of a vehicle-mounted monitoring platform acquires vibration information of the header of the combine harvester through a combined sensor module, a data processing module of the vehicle-mounted monitoring platform firstly performs noise reduction processing on the vibration information to acquire more accurate information, and finally performs fault diagnosis through a data diagnosis module of the vehicle-mounted monitoring platform;
the fault diagnosis is carried out on the vibration signal, and the fault diagnosis method comprises the following steps:
s1: denoising the fault signal by adopting a singular value decomposition method;
s2: the information fusion is adopted to carry out fusion processing on the vibration signal information obtained by each vibration sensor, so that a more accurate and reliable measurement result is obtained; the process of information fusion comprises the following steps:
s21: a first vibration sensor of the combined sensor module is arranged at a reel pin shaft, a second vibration sensor is arranged at an angle adjusting part of a cutting table auger, a third vibration sensor is arranged at a connecting part of a conveying groove, and a fourth vibration sensor is arranged at an end cover of a power input shaft of the cutting table; measured values thereof are X respectively1、X2、X3And X4The corresponding weighting factor weights are respectively: w1、W2、W3And W4(ii) a X represents the true value of the system,
and
representing the variance value, n is the number of sensors,
is the value after data fusion; under the condition of minimum total mean square error, the optimal weighting factors corresponding to the four sensors are searched in a self-adaptive mode according to the signals obtained by the sensorsSo as to be fused
The value is optimal:
the condition that each weighting factor satisfies:
total variance of true value X for the corresponding system:
wherein:
for the square of the weighting factor of each sensor,
variance for each sensor;
s22: according to the Lagrange multiplier extreme value theory, the corresponding weighting factor when the total mean square error is minimum is calculated as follows:
the actual value X in the fusion algorithm is obtained through average processing, the external environment is complex during measurement, and large deviation can be generated, and the actual value X is optimized in the invention;
firstly, collected measured values X of four vibration sensors1、X2、X3、X4Arranged from small to large, for the maximum value AmaxAnd a minimum value AminAnd (3) averaging:
mixing X0As a reference standard, the values of the four vibration sensors are compared with each other by a ratio X0Large classification as D1Is greater than X0Classification of small or equal to D2：
Respectively averaging the two classified values to obtain E [ D ]1]And E [ D ]2]Calculating the average value of the two values as the median value of the next calculation until X is obtained for the last timekAt this time, XkThe real value X of the additive fusion algorithm is obtained;
s3: and carrying out fault diagnosis on the fused information.
Further, the step S3 is specifically: and the data diagnosis module of the vehicle-mounted monitoring platform diagnoses the signals of the data processing module after data noise reduction and information fusion, and the diagnosis method is Variational Modal Decomposition (VMD).
Further, the wireless transmission module sends the diagnosis result obtained by the data diagnosis module to the monitoring terminal, so that the fault diagnosis is carried out remotely.
Furthermore, the combined sensor module comprises a first vibration sensor, a second vibration sensor, a third vibration sensor and a fourth vibration sensor, the vehicle-mounted monitoring platform comprises a data acquisition module, a data processing module, a data diagnosis module and a wireless sending module, and the monitoring terminal comprises a wireless receiving module and a data display module.
The invention has the beneficial effects that:
the remote fault diagnosis system for the header of the combine harvester can carry out remote fault diagnosis on the header of the combine harvester under the condition of keeping weak information;
the invention provides a method for monitoring a reel pin shaft position, a cutting table screw feeder angle adjusting position, a conveying groove connecting position and a cutting table power input shaft end cover position of a cutting table by a plurality of vibration sensors, comprehensively acquiring information of the cutting table of a combine harvester, applying a multi-sensor fusion technology, greatly improving the characteristic extraction effect of weak signals, and effectively acquiring the fault information of the combine harvester in real time and making diagnosis and timely taking rescue measures according to a GPS module by arranging a combined sensor module, a vehicle-mounted terminal platform, a wireless transmission module, the GPS module and a monitoring terminal.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic flow diagram of a method for diagnosing faults of a header of a combine harvester according to the present invention;
FIG. 2 is a block diagram of a multiple vibration sensor data fusion analysis;
FIG. 3 is a block diagram of the system architecture of the present invention;
fig. 4 is a schematic view of the mounting position of a plurality of vibration sensors on the header;
the labels in the figure are: 1. the system comprises a combined sensor module, 2, a vehicle-mounted monitoring platform, 3, a wireless transmission module, 4, a monitoring terminal, 5, a GPS module, 6, a data acquisition module, 7, a data processing module, 8, a data diagnosis module, 9, a wireless sending module, 10, a wireless receiving module, 11, a data display module, 12, a first vibration sensor, 13, a second vibration sensor, 14, a third vibration sensor, 15 and a fourth vibration sensor.
Detailed Description
The following specific examples are given to further clarify, complete and detailed the technical solution of the present invention. The present embodiment is a preferred embodiment based on the technical solution of the present invention, but the scope of the present invention is not limited to the following embodiments.
A remote fault diagnosis method for a header of a combine harvester is characterized in that a data acquisition module 6 of a vehicle-mounted monitoring platform 2 acquires vibration information of the header of the combine harvester through a combined sensor module 1, a data processing module 7 of the vehicle-mounted monitoring platform 2 firstly performs noise reduction processing on the vibration information to acquire more accurate information, and finally performs fault diagnosis through a data diagnosis module 8 of the vehicle-mounted monitoring platform 2, a wireless sending module 9 of the vehicle-mounted monitoring platform 2 transmits a fault signal to a wireless transmission module 3, the fault information is sent to a monitoring terminal 4 through 5G communication through the wireless transmission module 3 to perform remote control, and a GPS module 5 positions the combine harvester;
the fault diagnosis is carried out on the vibration signal, and the fault diagnosis method comprises the following steps:
s1: denoising the fault signal by adopting a singular value decomposition method;
s2: the information fusion is adopted to carry out fusion processing on the vibration signal information obtained by each vibration sensor, so that a more accurate and reliable measurement result is obtained; the process of information fusion comprises the following steps:
s21: a first vibration sensor 12 of the combined sensor module 1 is arranged at a reel pin shaft, a second vibration sensor 13 is arranged at a cutting table auger angle adjusting position, a third vibration sensor 14 is arranged at a conveying groove connecting position, and a fourth vibration sensor 15 is arranged at a cutting table power input shaft end cover; measured values thereof are X respectively1、X2、X3And X4The corresponding weighting factor weights are respectively: w1、W2、W3And W4(ii) a X represents the true value of the system,
and
representing the variance value, n is the number of sensors,
is data fusionThe latter value; under the condition of minimum total mean square error, the optimal weighting factors corresponding to the four sensors are searched in a self-adaptive mode according to the signals obtained by the sensors, so that the fused signals
The value is optimal:
the condition that each weighting factor satisfies:
total variance of true value X for the corresponding system:
wherein:
for the square of the weighting factor of each sensor,
variance for each sensor;
s22: according to the Lagrange multiplier extreme value theory, the corresponding weighting factor when the total mean square error is minimum is calculated as follows:
the actual value X in the fusion algorithm is obtained through average processing, the external environment is complex during measurement, and large deviation can be generated, and the actual value X is optimized in the invention;
firstly, the collected measurements of four vibration sensorsMagnitude X1、X2、X3、X4Arranged from small to large, for the maximum value AmaxAnd a minimum value AminAnd (3) averaging:
mixing X0As a reference standard, the values of the four vibration sensors are compared with each other by a ratio X0Large classification as D1Is greater than X0Classification of small or equal to D2：
Respectively averaging the two classified values to obtain E [ D ]1]And E [ D ]2]Calculating the average value of the two values as the median value of the next calculation until X is obtained for the last timekAt this time, XkThe real value X of the additive fusion algorithm is obtained;
s3: and carrying out fault diagnosis on the fused information.
Further, the step S3 is specifically: and a data diagnosis module 8 of the vehicle-mounted monitoring platform 2 diagnoses signals after data noise reduction and information fusion of the data processing module 7, and the diagnosis method is a variational modal decomposition VMD.
Further, the wireless transmission module 3 transmits the diagnosis result obtained by the data diagnosis module 8 to the monitoring terminal 4, so as to perform fault diagnosis remotely.
Further, the combined sensor module 1 comprises a first vibration sensor 12, a second vibration sensor 13, a third vibration sensor 14 and a fourth vibration sensor 15, the first vibration sensor 12 is installed at a reel pin shaft, the second vibration sensor 13 is installed at a cutting table auger angle adjusting position, the third vibration sensor 14 is installed at a conveying groove connecting position, and the fourth vibration sensor 15 is installed at a cutting table power input shaft end cover. The comprehensive acquisition of the header vibration signals is realized.
The vehicle-mounted monitoring platform 2 comprises a data acquisition module 6, a data processing module 7, a data diagnosis module 8 and a wireless sending module 9, and the monitoring terminal 4 comprises a wireless receiving module 10 and a data display module 11.
Further, the combined sensor module 1 is used for comprehensively acquiring a header vibration signal; the vehicle-mounted monitoring platform 2 is used for carrying out data acquisition, processing, signal noise reduction, signal fusion and fault diagnosis on working signals of a header of the combine harvester; the wireless transmission module 3 is used for sending the header fault diagnosis result data to the monitoring terminal 4 for displaying and processing; the monitoring terminal 4 is used for diagnosing faults of the header under a complex condition; the GPS module 5 is installed on the combine harvester, and the GPS module 5 is used for acquiring the position information of the combine harvester and positioning the combine harvester.
Further, the data acquisition module 6 is configured to acquire header vibration information; the data processing module 7 is used for carrying out noise reduction and information fusion on the acquired vibration information, and the data processing module 7 carries out fusion processing on the information obtained by the four vibration sensors by adopting information fusion to obtain a more accurate and reliable measurement result; the data processing module 7 adopts a singular value decomposition method to reduce noise of the fault signal, and the singular value decomposition noise reduction method has better noise immunity and higher frequency domain resolution and can extract weak signal components; the data diagnosis module 8 is used for carrying out fault diagnosis on the fused data; the wireless sending module 9 is used for transmitting the fault diagnosis result to the wireless transmission module 3.
Further, the wireless receiving module 10 is configured to receive the fault information transmitted by the wireless transmitting module 3, and the data displaying module 11 is configured to display the fault information.
In conclusion, the remote fault diagnosis method for the header of the combine harvester can be used for remotely diagnosing the header of the combine harvester under the condition of keeping weak information; the invention provides a method for monitoring a reel pin shaft position, a cutting table screw feeder angle adjusting position, a conveying groove connecting position and a cutting table power input shaft end cover position of a cutting table by a plurality of vibration sensors, comprehensively acquiring information of the cutting table of a combine harvester, applying a multi-sensor fusion technology, greatly improving the characteristic extraction effect of weak signals, and effectively acquiring the fault information of the combine harvester in real time and making diagnosis and timely taking rescue measures according to a GPS module by arranging a combined sensor module, a vehicle-mounted terminal platform, a wireless transmission module, the GPS module and a monitoring terminal.
The principal features, principles and advantages of the invention have been shown and described above. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to explain the principles of the invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the invention as expressed in the following claims. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (4)
1. A remote fault diagnosis method for a header of a combine harvester is characterized by comprising the following steps: the method comprises the following steps that a data acquisition module (6) of a vehicle-mounted monitoring platform (2) acquires vibration information of a header of the combine harvester through a combined sensor module (1), a data processing module (7) of the vehicle-mounted monitoring platform (2) firstly performs noise reduction processing on the vibration information to obtain more accurate information, and finally performs fault diagnosis through a data diagnosis module (8) of the vehicle-mounted monitoring platform (2), a wireless sending module (9) of the vehicle-mounted monitoring platform (2) transmits a fault signal to a wireless transmission module (3), the fault information is sent to a monitoring terminal (4) through the wireless transmission module (3) to perform remote control, and a GPS module (5) positions the combine harvester;
the fault diagnosis is carried out on the vibration signal, and the fault diagnosis method comprises the following steps:
s1: denoising the fault signal by adopting a singular value decomposition method;
s2: the information fusion is adopted to carry out fusion processing on the vibration signal information obtained by each vibration sensor, so that a more accurate and reliable measurement result is obtained; the process of information fusion comprises the following steps:
s21: first vibration of a combination sensor module (1)A moving sensor (12) is arranged at a reel pin shaft, a second vibration sensor (13) is arranged at the cutting table auger angle adjusting position, a third vibration sensor (14) is arranged at the conveying groove connecting position, and a fourth vibration sensor (15) is arranged at the cutting table power input shaft end cover; measured values thereof are X respectively1、X2、X3And X4The corresponding weighting factor weights are respectively: w1、W2、W3And W4；
X represents the true value of the system,
and
representing the variance value, n is the number of sensors,
is the value after data fusion; under the condition of minimum total mean square error, the optimal weighting factors corresponding to the four sensors are searched in a self-adaptive mode according to the signals obtained by the sensors, so that the fused signals
The value is optimal:
the condition that each weighting factor satisfies:
total variance of true value X for the corresponding system:
wherein:
for the square of the weighting factor of each sensor,
variance for each sensor;
s22: according to the Lagrange multiplier extreme value theory, the corresponding weighting factor when the total mean square error is minimum is calculated as follows:
the actual value X in the fusion algorithm is obtained through average processing, the external environment is complex during measurement, and large deviation can be generated, and the actual value X is optimized in the invention;
firstly, collected measured values X of four vibration sensors1、X2、X3、X4Arranged from small to large, for the maximum value AmaxAnd a minimum value AminAnd (3) averaging:
mixing X0As a reference standard, the values of the four vibration sensors are compared with each other by a ratio X0Large classification as D1Is greater than X0Classification of small or equal to D2：
Respectively averaging the two classified values to obtain E [ D ]1]And E [ D ]2]Calculating the average value of the two values as the median value of the next calculation until X is obtained for the last timekAt this time, XkThe real value X of the additive fusion algorithm is obtained;
s3: and carrying out fault diagnosis on the fused information.
2. A method of remote fault diagnosis of a combine harvester header according to claim 1, characterized in that: the step S3 specifically includes: and a data diagnosis module (8) of the vehicle-mounted monitoring platform (2) diagnoses signals of the data processing module (7) after data noise reduction and information fusion, and the diagnosis method is a variational modal decomposition VMD.
3. A method of remote fault diagnosis of a combine harvester header according to claim 1, characterized in that: the wireless transmission module (3) sends the diagnosis result obtained by the data diagnosis module (8) to the monitoring terminal (4), so that the fault diagnosis is carried out remotely.
4. A method of remote fault diagnosis of a combine harvester header according to claim 1, characterized in that: the combined sensor module (1) comprises a first vibration sensor (12), a second vibration sensor (13), a third vibration sensor (14) and a fourth vibration sensor (15), the vehicle-mounted monitoring platform (2) comprises a data acquisition module (6), a data processing module (7), a data diagnosis module (8) and a wireless sending module (9), and the monitoring terminal (4) comprises a wireless receiving module (10) and a data display module (11).
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Application publication date: 20210126 |
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