CN104345680A - FNN (Fuzzy Neural Network)-based fault diagnosis method and device of tangential-longitudinal flow combined harvester - Google Patents
FNN (Fuzzy Neural Network)-based fault diagnosis method and device of tangential-longitudinal flow combined harvester Download PDFInfo
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Abstract
The invention discloses an FNN (Fuzzy Neural Network)-based diagnosis method and device of a tangential-longitudinal flow combined harvester. The method and the device are used for monitoring the working conditions of the combined harvester in real time and making a pre-alarm on a fault. The device comprises a signal acquisition module, a PLC (Programmable Logic Controller), a display module and an audible and visual alarm module. According to the method and the device, a rotating speed sensor is used for acquiring rotating speed signals of a header auger, a conveying trough, a tangential flow roller, a longitudinal axial flow roller and a grain conveying auger, a signal conditioning circuit is used for transmitting the rotating speed signals to the PLC, the PLC is used for analyzing the signals by an FNN-based fault diagnosis algorithm to obtain a fault analysis result which is then displayed in a liquid crystal display module, and in addition, an audible and visual pre-alarm signal is sent in time in case of a fault. According to the method and the device disclosed by the invention, the working conditions of the tangential-longitudinal flow combined harvester can be automatically monitored, the fault situations of the combined harvester can be reflected in real time, the labor force, the materials and the fund are effectively saved and the mechanical operation reliability is improved.
Description
Technical Field
The invention relates to the field of agricultural machinery, in particular to a fault diagnosis method and a fault diagnosis device for a tangential-longitudinal flow combine harvester.
Background
When the tangential-longitudinal flow combine harvester works in the field, the feeding amount is too large due to too high speed, so that rotating parts such as a header auger, a conveying groove, a tangential flow roller, a longitudinal-longitudinal flow roller, a grain conveying auger and the like are blocked, and the working quality of the combine harvester is influenced, so that the fault diagnosis research on the combine harvester is particularly necessary. Scholars at home and abroad make more researches on the aspects of intelligent load monitoring and fault diagnosis of the combine harvester, and although certain results are obtained, the effect is not ideal because the nonlinear characteristic of the combine harvester is not considered; for example, in the research of a blockage fault monitoring system of a combine harvester, a first-order difference, a second-order difference, a relative speed ratio, a slip ratio, a difference type integral and 5 parameters are selected as characteristic vectors to monitor the aspects of a rotating shaft rotating speed continuous change process, a rotating speed gradient continuous change process, a dynamic rotating speed continuous change process and the like of the combine harvester, although the early warning effect on faults is good, the nonlinear problem of system input is not solved. Based on the method, a Fuzzy Neural Network (FNN) is applied to a fault diagnosis clock of the combine harvester, the nonlinear mapping relation between various fault symptoms and fault types of the blockage fault diagnosis of the combine harvester is established, and the nonlinear problem of a blockage fault diagnosis system of the combine harvester is solved.
Disclosure of Invention
The invention aims to provide a FNN algorithm-based fault diagnosis method and device for a tangential-longitudinal flow combine harvester, which can send out early warning signals before faults occur, improve the reliability of the combine harvester and promote the conversion from passive maintenance to fault prevention concept.
The intelligent fault diagnosis of the tangential-longitudinal flow combine harvester is a complex process, and in order to overcome the problem that the input and output nonlinear characteristics of a fault diagnosis model are poor, the invention provides a data processing method based on an FNN algorithm, which can effectively improve the nonlinearity of the system. And carrying out fuzzy neural network algorithm processing on the data in the PLC by collecting the rotating speed value within a certain time to obtain a system fault diagnosis result.
When the tangential-longitudinal flow combine harvester operates in the field, the rotating parts such as the header auger, the conveying groove, the tangential flow roller, the longitudinal flow roller, the grain conveying auger and the like are blocked due to overlarge feeding amount, so that the rotating speed is reduced, and the operating quality of the harvester is influenced when the rotating speed is reduced to a certain degree. The invention takes the header auger, the conveying trough, the tangential axial flow roller, the longitudinal axial flow roller and the grain conveying auger as high-failure-rate components of the harvester to carry out failure analysis research.
The invention adopts a harvester fault diagnosis method based on FNN algorithm, which comprises the following steps:
1) the rotating speed sensor acquires rotating speed values of the header auger, the conveying groove, the tangential flow roller, the longitudinal axial flow roller and the grain conveying auger;
2) transmitting the measured rotating speed signal into a PLC (programmable logic controller), and analyzing and processing the signal by the PLC according to an FNN (fuzzy neural network) -based method to obtain a fault diagnosis result; the method adopted by the invention comprises the following steps: the invention determines five quantities of the rotating speed of a header auger, a conveying groove, a tangential flow roller, a longitudinal axis axial flow roller and a grain conveying auger as the input quantity of the system, and records the system input as x1、x2、x3、x4And x5The input vector is X ═ X1,x2,...,xn) (ii) a The system failure diagnosis result Y ═ Y1,y2,y3) As an output quantity (y)1Representing a normal result, y2Representing a resultant anomaly, y3Representative of a failure of the result); the fuzzy algorithm formulates an input and output quantization table in advance according to a formulaThe degree of suitability α of the input for each rule can be foundi(i-1, 2,3,4,5) (wherein,is xiThe value of the language variable of (a),is thatDegree of membership of) according to the degree of suitability αiThe input quantity X ═ X can be obtained1,x2,...,xn) The fuzzy quantization value of (1); by fuzzy reasoning, according to formulae <math><mrow>
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</mrow></math> The degree of membership of the output quantity can be obtained(whereinIs yγThe value of the language variable of (a),is thatMembership function) of the output quantity, a weighted average method is adopted to obtain the output quantity value as follows:(wherein r is 1,2,3,is thatCentral value of (i.e. <math><mrow>
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After the input and output quantity is subjected to fuzzy quantization through the steps, the fuzzy value of the input quantity is used as the input value of the neural network, and the fuzzy output is performedAs an output value of the neural network, offline training is carried out on a neural network algorithm in MATLAB by means of mathematical tool software, a fuzzy neural diagnosis rule table can be made by analyzing and inducing an offline training result, the fuzzy neural diagnosis rule table only needs to be stored in a PLC in practical application, and a fault diagnosis result can be rapidly obtained by directly using a table look-up method.
3) Transmitting the fault diagnosis result into a liquid crystal display module for displaying;
4) when a fault occurs, the PLC sends out an acousto-optic early warning signal in time, and the acousto-optic warning module displays the acousto-optic early warning signal.
The fault diagnosis device of the tangential-longitudinal flow combine harvester comprises a signal acquisition module, a PLC, a liquid crystal display module and an acousto-optic alarm module; the signal acquisition module comprises a rotating speed sensor and a signal conditioning circuit; the rotating speed sensors are respectively arranged on rotating speed output shafts of a header auger, a conveying groove, a tangential flow roller, a longitudinal flow roller and a grain conveying auger of the tangential-longitudinal flow combine harvester; the rotation speed sensor acquires rotation speed signals of the header auger, the conveying groove, the tangential flow roller, the longitudinal axial flow roller and the grain conveying auger and transmits the rotation speed signals to the PLC through the signal conditioning circuit, the PLC processes data through the FNN-based fault diagnosis algorithm, and the obtained result is transmitted to the liquid crystal display module to be displayed; meanwhile, the PLC sends out acousto-optic early warning signals in time, and the acousto-optic early warning signals are displayed in the acousto-optic warning module.
The invention can monitor the working state of the tangential-longitudinal flow combine harvester and display the working state of the tangential-longitudinal flow combine harvester through the display module; when a fault occurs, the fault is reminded in time, and an audible and visual alarm signal is sent out in time; the invention can realize the automatic monitoring of the working condition of the tangential-longitudinal flow combine harvester, well solve the nonlinear problem of the system and reflect the fault condition of the combine harvester in real time.
Drawings
Fig. 1 is a block diagram of a tangential-longitudinal flow combine fault diagnosis system.
FIG. 2 is a graph of input membership functions.
FIG. 3 is a graph of output membership function.
FIG. 4 is a flow chart of the FNN algorithm; .
Detailed Description
The details and operation of the particular apparatus proposed by the present invention will now be described in detail with reference to the accompanying drawings.
The system determines five rotating speeds of the cutting table auger, the conveying groove, the tangential flow roller, the longitudinal axis axial flow roller and the grain conveying auger as input quantities of the system, and records the system input as the input quantity of the system、、、And(ii) a The fault diagnosis result of the system is taken as an output quantity, and the output quantity of the system is recorded as。
Taking the tangential flow roller of the combine harvester as an example, the division of the membership function of the system input quantity is explained. The rotation speed range of the tangential flow roller of the Taihu star TH988 tangential longitudinal flow combine harvester is 0-800r/minThe normal working rotating speed range is 700-800r/minRated speed of 750r/minLeft and right. For the fine description of variables, the basic discourse domain is divided into 13 grades to obtain the rotating speed of the tangential flow rollerThe fuzzy subset domain of (a) { -6, -5, -4, -3, -2, -1, 0, 1,2,3,4,5, 6 }. The deviation variation range for each grade is shown in the table below.
| Quantization levels | Variation Range (r/min) | Quantization levels | Variation Range (r/min) |
| -6 | <560 | 6 | 780～800 |
| -5 | 560～580 | 5 | 760～780 |
| -4 | 580～600 | 4 | 740～760 |
| -3 | 600～620 | 5 | 720～740 |
| -2 | 620～640 | 2 | 700～720 |
| -1 | 640～660 | 1 | 680～700 |
| 0 | 660～680 |
The corresponding fuzzy language variable set is seven words of { big negative, middle negative, small negative, zero positive, small middle positive, big positive }, which are abbreviated as { NB, NM, NS, ZO, PS, PM, PB } by English prefix.If the corresponding language variable is positive, the operation of the tangential flow roller is normal; if the current is negative, the current is abnormal when the current cutting roller works; the membership function curve of the input function is shown in fig. 3.
Obtaining system fuzzy output by dividing system output membership function: output when the input part is working normally= 1; system output to prevent jamming failure when input components have a tendency to jam= 2; system output when a fault has occurredAnd = 3. Then the basic domain of discourse of y is (1-3) and the fuzzy subset domain of discourse is {0, 1,2 }. To pair
The fuzzy language variable set is three words of { zero, small, large }, and is abbreviated as { NB, NS, ZO } by English prefix.If the corresponding language variable is positive, the operation is normal;if the corresponding language variable is negative, the harvester is indicated to be abnormal in operation. The output value of the system can be obtained by assigning the output fuzzy linguistic variable by using the triangular distribution membership function shown in fig. 4.
After the input and output quantity is subjected to fuzzy quantization through the steps, the fuzzy value of the input quantity is used as the input value of the neural network, and the fuzzy output is performedAs an output value of the neural network, offline training is carried out on a neural network algorithm in MATLAB by means of mathematical tool software, a fuzzy neural diagnosis rule table can be made by analyzing and inducing an offline training result, the fuzzy neural diagnosis rule table only needs to be stored in a PLC in practical application, and a fault diagnosis result can be rapidly obtained by directly using a table look-up method.
MATLAB is a powerful mathematical tool software, which involves various network models and provides powerful support for the design and implementation of FNN. The off-line training of the neural network in MATLAB can be realized by directly calling related functions, so that the training period of the application program is greatly shortened, and the reliability can be enhanced. The FNN-based fault diagnosis system is trained off-line by programming, and the training program is as follows:
clear all
X=[148 310 746 1078 490;147 311 745 1080 492;149 309 736 1042 472;143 297 729 1011 484;136 306 739 978 469;146 301 701 997 452;131 298 684 971 482;148 294 639 981 463;147 261 685 958 448;129 268 642 942 431;146 290 694 1075 483;130 303 738 1020 479;125 267 638 1075 479;145 263 659 952 443;139 274 703 917 417;132 291 720 928 459;129 294 743 957 421;146 259 629 1082 420;132 301 653 969 486;117 273 736 923 441]T
Y=[1;1;1;1;2;2;2;2;2;2;2;2;3;3;3;3;3;3;3;3]T
net_1=newff(minmax(X),[20,20],{'tan-sig','purelin'},'traingdm')
net_1.trainParam.show=50;
net_1.trainParam.lr=0.05;
net_1.trainParam.mc=0.9;
net_1.trainParam.epochs=100000;
net_1.trainParam.goal=le-3;
[net_1,tr]=train(net_1,X，Y)；
A=sim(net_1,X);
E=Y-A;
MSE=mse(E)；
obtaining fuzzy values of corresponding input quantities according to an input and output quantization table and a membership function diagram of a combine harvester fault diagnosis system, performing offline training by using MATLAB, and making the following 20 control rules through analysis and induction, wherein the established FNN diagnosis rules are shown in the following table.
| x1 | x2 | x3 | x4 | x5 | y |
| PB | PB | PB | PM | PB | ZO |
| PM | PB | PB | PM | PM | ZO |
| PB | PB | PM | PS | PS | ZO |
| PM | PM | PM | ZO | PM | ZO |
| ZO | PM | PB | NS | PM | NS |
| PM | PM | PS | NS | ZO | NS |
| NM | PS | PM | NS | PM | NS |
| PB | PS | PM | NS | ZO | NS |
| PM | NB | ZO | NM | NS | NS |
| NB | NB | NS | NB | NM | NS |
| PM | PS | NS | PM | PM | NS |
| NS | PS | PS | PS | PS | NS |
| NS | NB | PM | PM | PS | NB |
| PM | NB | NM | NM | NS | NB |
| PS | NS | PS | NB | NB | NB |
| PM | PS | PM | NB | ZO | NB |
| NB | PS | PB | NM | NB | NB |
| PM | NB | NB | PM | NB | NB |
| NM | PM | NM | NM | PM | NB |
| NB | NM | NB | NB | NS | NB |
In the FNN diagnostic rules table: if the rotating speed of the longitudinal axis flow drum is far lower than the rated value when the rotating speed of the header auger is PM, the rotating speed of the conveying chute is PM, the rotating speed of the tangential flow drum is PS, the rotating speed of the grain conveying auger is ZO and the rotating speed of the longitudinal axis flow drum is NS, the output is realizedIf the rotating speed of the cutting table auger is PM, the rotating speed of the conveying groove is PS, the rotating speed of the longitudinal axial flow roller is PM, the rotating speed of the grain conveying auger is PM and the rotating speed of the tangential flow roller is NS,the rotating speed of the tangential flow roller is far lower than the rated rotating speed value, the blockage situation occurs, and therefore the output is realizedIf the rotating speed of the header auger is PB, the rotating speed of the conveying groove is PB, the rotating speed of the tangential flow roller is PB, the rotating speed of the longitudinal axis flow roller is PM and the rotating speed of the grain conveying auger is PM, the working state of the combine harvester is normal, and therefore the output quantity is NBThe output is normal, namely ZO.
When the fault diagnosis algorithm based on the FNN is realized by utilizing the PLC, a quantization table of input output quantity and a FNN diagnosis rule query table must be established in the PLC firstly. For specific input, the corresponding quantization grade is obtained by checking the quantization table of the input quantity to obtain the corresponding fuzzy quantity, then the quantization grade of the system output quantity is obtained by checking the table according to the FNN rule, and finally the result of system fault diagnosis is obtained by the quantization table of the output quantity, and the FNN algorithm flow chart is shown in figure 2.
The specific working process of the invention is as follows: the PLC analyzes and processes the signals according to the FNN-based fault diagnosis method provided by the invention to obtain a fault analysis result, and displays the result in the liquid crystal display; when a fault occurs, the PLC sends out an acousto-optic early warning signal in time, and the acousto-optic warning module displays the acousto-optic early warning signal. The final decision obtained by the invention can be divided into three stages: normal, early warning and alarming. If the judgment result is normal, a green light indicator lamp in the sound-light alarm module is lightened, and the combine harvester continues to work in the current working state; if the result obtained by judgment is early warning, the situation that the harvester is abnormal at once is shown, the advancing speed needs to be reduced immediately, the harvester is maintained in a normal working range, a yellow early warning indicator lamp in the sound-light alarm module is turned on and accompanied with a buzzing sound, and a driver is reminded that the advancing speed needs to be reduced. If the result obtained by the judgment is alarm, the operation of the harvester is indicated to be failed, the operation needs to be stopped for maintenance, and the red light of the alarm indicator lamp in the audible and visual alarm module is lightened along with a buzzing sound to remind a driver to stop the operation for maintenance.
Claims (3)
1. A FNN-based tangential-longitudinal flow combine harvester fault diagnosis method comprises the following steps:
a, a rotating speed sensor acquires rotating speed values of a header auger, a conveying groove, a tangential flow roller, a longitudinal axial flow roller and a grain conveying auger;
b, transmitting the signals measured by the sensors in the step A into a PLC (programmable logic controller), and analyzing and processing the signals of the sensors by the PLC based on an FNN (fuzzy neural network) algorithm to obtain three diagnosis results of normal diagnosis, early warning and alarming;
the PLC transmits the diagnosis result to a liquid crystal display module for displaying based on the FNN algorithm;
and D, when the diagnosis result is early warning or alarming, the PLC sends out early warning signals in time and transmits the early warning signals into the sound-light alarming module to remind the sound-light early warning signals.
2. The FNN-based tangential longitudinal flow combine harvester fault diagnosis method according to claim 1, wherein the fault diagnosis method of the PLC based on FNN algorithm comprises the following specific steps:
e, determining five rotation speed quantities of the header auger, the conveying groove, the tangential flow roller, the longitudinal axis axial flow roller and the grain conveying auger as input quantities of the PLC system, and recording the system input as x1、x2、x3、x4And x5The input vector is X ═ X1,x2,...,xn) (ii) a The system failure diagnosis result Y ═ Y1,y2,y3) As an output, wherein y1Representing a normal result, y2Representing a resultant anomaly, y3Representing a failure of the result; the fuzzy algorithm formulates an input and output quantization table in advance according to a formulaThe degree of suitability α of the input for each rule can be foundi(i-1, 2,3,4,5), wherein,is xiThe value of the language variable of (a),is thatDegree of membership of, according to degree of suitability αiThe input quantity X ═ X can be obtained1,x2,...,xn) The fuzzy quantization value of (1); by fuzzy reasoning, according to formulaeThe degree of membership of the output quantity can be obtainedWherein,is yγThe value of the language variable of (a),is thatThe membership function of (2) is obtained by a weighted average clarification method, and the output quantity value is obtained as follows:wherein r is 1,2,3,is thatCentral value of (i.e.
F, after the input and output quantity of the PLC is subjected to fuzzy quantization through the step E, the input quantity fuzzy value is used as the input value of the neural network, and the fuzzy output y is outputγThe method comprises the steps of performing off-line training on a neural network algorithm in MATLAB by means of mathematical tool software as an output value of the neural network, analyzing an off-line training result, inducing to prepare a fuzzy neural diagnosis rule table, storing the fuzzy neural diagnosis rule table into a PLC, and directly and quickly obtaining a fault diagnosis result by a table look-up method.
3. The utility model provides a surely indulge and flow combine fault diagnosis device based on FNN which characterized in that: the device comprises a signal acquisition module, a PLC, a liquid crystal display module and an audible and visual alarm module; the signal acquisition module comprises a rotating speed sensor and a signal conditioning circuit; the rotating speed sensors are respectively arranged on rotating speed output shafts of a header auger, a conveying groove, a tangential flow roller, a longitudinal flow roller and a grain conveying auger of the tangential-longitudinal flow combine harvester; the rotating speed sensor acquires rotating speed signals of the header auger, the conveying groove, the tangential flow roller, the longitudinal axial flow roller and the grain conveying auger and transmits five rotating speed signals to the PLC through the signal conditioning circuit, the PLC processes data through the FNN-based fault diagnosis algorithm, and an obtained result is transmitted to the liquid crystal display module to be displayed; meanwhile, the PLC is connected with the sound-light alarming module, when the early-warning alarming signal appears, the PLC transmits the early-warning alarming signal to the sound-light early-warning alarming module, and the sound-light early-warning alarming module sends out sound-light early-warning alarm.
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