CN115390550A - Fault diagnosis method and system of combine harvester, controller and combine harvester - Google Patents
Fault diagnosis method and system of combine harvester, controller and combine harvester Download PDFInfo
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- CN115390550A CN115390550A CN202211322984.6A CN202211322984A CN115390550A CN 115390550 A CN115390550 A CN 115390550A CN 202211322984 A CN202211322984 A CN 202211322984A CN 115390550 A CN115390550 A CN 115390550A
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
- G05B23/0243—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults model based detection method, e.g. first-principles knowledge model
- G05B23/0245—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults model based detection method, e.g. first-principles knowledge model based on a qualitative model, e.g. rule based; if-then decisions
- G05B23/0248—Causal models, e.g. fault tree; digraphs; qualitative physics
Abstract
The invention relates to the technical field of combine harvesters, in particular to a method, a system, a controller and a combine harvester for diagnosing faults of the combine harvester, wherein the method comprises the following steps: carrying out hierarchical classification on each fault of a preset component corresponding to a fault tree model by using any fault tree model to obtain a minimum cut set corresponding to each fault, forming a structural body by using any minimum cut set and the upper-level category of the minimum cut set to obtain a plurality of structural bodies, and calibrating a threshold condition corresponding to each structural body; actual data corresponding to each structural body is obtained from data monitored in real time of the combine harvester, and is compared with the threshold value condition of each structural body respectively, the minimum cut set of the structural bodies which do not meet the threshold value condition is determined as the fault reason of the combine harvester, the working parts of the combine harvester and related electric control functions can be analyzed and diagnosed in time, and the efficiency is high.
Description
Technical Field
The invention relates to the technical field of combine harvesters, in particular to a fault diagnosis method, a fault diagnosis system, a fault diagnosis controller and a combine harvester of the combine harvester.
Background
At present, the trend of large-scale grain planting is strengthened year by year, a miniaturized combine harvester cannot meet the requirement of harvesting operation of a large land, and the requirement of medium-sized and large-sized grain combine harvesters is increased year by year. The whole size of the combine harvester is huge, the internal structure is complex, the electric control function of the whole combine harvester is continuously increased along with the improvement of the intelligent and automatic degree of the combine harvester, the difficulty in designing the wire harness is increased, the troubleshooting and positioning are difficult, the operation environment is severe, the effect of the electric control function is easily influenced, and various electrical appliance faults are easily caused. Due to the fact that an electric control system of the combine harvester is complex, field personnel cannot timely and accurately position a fault occurrence position and troubleshoot the fault occurrence position due to the occurrence of the fault. Currently, fault location is often performed by:
1) In the invention patent with the publication number of CN109673217A and the subject name of 'vegetable drill operation fault diagnosis system and method', the sensor data of each detection unit is comprehensively judged by a control unit to make corresponding judgment, the scheme mainly aims at fault diagnosis of the miss-seeding detection function to obtain the type, reason and position of the miss-seeding. The scheme is only used for analyzing the working condition of the seeding machine in the seeding process and is not used for analyzing the complex working condition in the harvesting process of the combine harvester.
2) In the text of the fault diagnosis technology of the combine harvester based on the random forest, fault data characteristics of main parts of the combine harvester are collected and extracted, the data are classified into normal and abnormal data, a plurality of CART decision trees are established, a random forest diagnosis model is formed, and rapid fault diagnosis of the combine harvester is realized. According to the technical scheme, the cylinder blockage fault is judged by analyzing the blockage condition of the cylinder aiming at the threshing cylinder fault of the combine harvester, the reason of the cylinder rotating speed fault is explained in detail, the fault is single, and the fault analysis is not carried out aiming at other electric control functions of the whole harvester.
3) In the text of the remote fault diagnosis system research of the combine harvester based on the fuzzy neural network, aiming at the rice combine harvester, a vehicle-mounted data monitoring module is built, a sensor is used for monitoring the working data of relevant working parts, and the data are transmitted to a remote service terminal for storage in real time. A fault diagnosis model is established by adopting a fuzzy neural network diagnosis and early warning algorithm, so that the fault diagnosis of a working site is realized.
4) In the study of a combine harvester fault monitoring and diagnosing system, a related sensor detecting system is built by designing hardware and software of the combine harvester monitoring system. A T-S fuzzy model is built in a built system, and whether the combine harvester is in a normal working state or not is judged by monitoring eight rotating speeds such as an input screw, a vibrating screen, a sundry screw, a grain conveying screw, a fan wheel, a conveyor belt wheel, a threshing cylinder, a reel, cutting knife frequency and the like.
The technical schemes only analyze faults of certain specific parts and do not analyze and diagnose faults of working parts of the whole vehicle and related electric control functions.
Disclosure of Invention
The invention aims to solve the technical problem of the prior art and provides a method, a system, a controller and a combine harvester for diagnosing the fault of the combine harvester.
The technical scheme of the fault diagnosis method of the combine harvester is as follows:
establishing a fault tree model of each fault type corresponding to each preset component of the combine harvester;
carrying out hierarchical classification on each fault of a preset component corresponding to a fault tree model by using any fault tree model to obtain a minimum cut set corresponding to each fault, forming a structural body by using any minimum cut set and the upper-level category of the minimum cut set to obtain a plurality of structural bodies, and calibrating a threshold condition corresponding to each structural body;
and acquiring actual data corresponding to each structural body from the data for monitoring the combine harvester in real time, comparing the actual data with the threshold condition of each structural body respectively, and determining the minimum cut set of the structural bodies which do not meet the threshold condition as the fault cause of the combine harvester.
The method for diagnosing the fault of the combine harvester has the following beneficial effects:
the method can quickly analyze and diagnose the faults of the working parts and the related electric control functions of the combine harvester, and is high in efficiency.
The technical scheme of the fault diagnosis system of the combine harvester is as follows:
the system comprises an establishing module, a classification calibration module and a logic judgment module;
the establishing module is used for: establishing a fault tree model of each fault type corresponding to each preset component of the combine harvester;
the classification calibration module is used for: carrying out hierarchical classification on each fault of a preset component corresponding to a fault tree model by using any fault tree model to obtain a minimum cut set corresponding to each fault, forming a structural body by using any minimum cut set and the upper-level category of the minimum cut set to obtain a plurality of structural bodies, and calibrating a threshold condition corresponding to each structural body;
the logic judgment module is used for: and acquiring actual data corresponding to each structural body from the data for monitoring the combine harvester in real time, comparing the actual data with the threshold condition of each structural body respectively, and determining the minimum cut set of the structural bodies which do not meet the threshold condition as the fault cause of the combine harvester.
The fault diagnosis system of the combine harvester has the following beneficial effects:
the method can quickly analyze and diagnose the faults of the working parts and the related electric control functions of the combine harvester, and is high in efficiency.
The controller comprises a control chip, wherein the control chip is used for executing the fault diagnosis method of the combine harvester.
The invention provides a combine harvester, which comprises the controller.
Drawings
Fig. 1 is a schematic flow chart of a method for diagnosing a fault of a combine harvester according to an embodiment of the present invention;
FIG. 2 is a schematic view of a failure cause of a drum spin-down failure;
FIG. 3 is a schematic diagram of a fault tree model corresponding to a drum tune-up fault;
FIG. 4 is a schematic diagram of a fault tree model in the form of letters;
FIG. 5 is a schematic view showing a flow of determining whether the drum rotation speed is within a normal range;
FIG. 6 is a schematic flow chart of a belt failure determination routine;
fig. 7 is a schematic flow chart of drum failure determination;
FIG. 8 is a schematic flow chart of monitoring data of a combine harvester;
fig. 9 is a schematic structural diagram of a fault diagnosis system of a combine harvester according to an embodiment of the invention.
Detailed Description
As shown in fig. 1, a method for diagnosing a fault of a combine harvester according to an embodiment of the present invention includes the steps of:
s1, establishing a fault tree model of each fault type corresponding to each preset component of the combine harvester;
wherein, a plurality of parts of predetermineeing of combine include header, cylinder, unload grain section of thick bamboo, fan etc. summarize every trouble and reason of every part of predetermineeing, form the trouble tree model that every part of predetermineeing corresponds, take the cylinder as the example and explain:
first, the failure type and the failure cause of the drum are summarized, and the description will be continued by taking the failure type as a drum-down failure as an example, and the failure cause of the drum-down failure is shown in fig. 2. According to the figure 2, establishing a fault tree model corresponding to the roller rotation failure;
s2, carrying out hierarchical classification on each fault of a preset component corresponding to a fault tree model by using any fault tree model to obtain a minimum cut set corresponding to each fault, forming a structural body by using any minimum cut set and the upper-level category of the minimum cut set to obtain a plurality of structural bodies, and calibrating a threshold condition corresponding to each structural body;
taking a fault tree model corresponding to a roller rotation failure as an example, description is given to layer classification, the classification of a first level corresponding to the roller rotation failure is 'adjustment non-automatic recovery' and 'adjustment automatic recovery', 'the classification of a next level corresponding to the adjustment non-automatic recovery' is 'belt slip', 'electromagnetic valve failure', 'engine speed is low', 'the classification of a next level corresponding to the electromagnetic valve failure' is 'electrical fault' and 'hydraulic lock failure', 'the classification of a next level corresponding to the engine speed is low' is 'accelerator failure', 'ECU failure' and 'naphtha system failure', 'the classification of a next level corresponding to the electrical fault' is 'line failure' and 'electromagnetic valve coil failure', 'adjustment automatic recovery', 'the classification of a next level corresponding to the roller blockage', 'belt slip', 'throttle is excessively large', 'feeding speed is slightly small', 'the feeding belt is' and 'the classification of a next level corresponding grain is' oil pollution belt ',' belt is seriously insufficient 'and' severely worn out ', and' the classification of a gravure plate, and 'the classification of a lower level corresponding to the speed is high speed gap', and the classification of a lower vehicle speed is reduced;
in order to facilitate the subsequent simplification of the fault tree and the calculation of the minimum cut set in the later period, the scheme adopts letter representation. As shown in table 1, a fault tree model in the form of letters is thus obtained, as shown in fig. 4.
Table 1:
obtaining a minimum cut set corresponding to each fault as follows: { X1}, { X2}, { X3}, { X4, X8}, { X4, X9}, { X5}, { X6}, { X7}, { X10}, { X11}, { X12}, { X13}, { X14}, { X15, X16, X17}, { X15, X16, X18};
for example: { X1} with the upper class C constitutes a structure, X2} with the upper class C constitutes a structure, and X5} with the upper class D constitutes a structure.
The method for calibrating the threshold value corresponding to each structural body specifically comprises the following steps:
for example, the threshold condition for a structure consisting of { X1} and C is: the minimum value of the rotating speed of the roller is 100rpm, the maximum value is 2000rpm, and the bearing temperature of the roller is 90 ℃;
the threshold conditions for the structure composed of { X4} and D are: the minimum value of the rotating speed of the roller is 10rpm, and the maximum value is 9000rpm;
the threshold conditions for the structure composed of { X6} and E are: the engine speed of the combine harvester is 2000rpm;
by analogy, according to the actual situation, the threshold condition of each structural body is set.
And S3, obtaining actual data corresponding to each structural body from the data for monitoring the combine harvester in real time, comparing the actual data with the threshold condition of each structural body respectively, and determining the minimum cut set of the structural bodies which do not meet the threshold condition as the fault reason of the combine harvester.
Wherein, the data to combine real-time supervision include: header operating data, roller operating data, unloading cylinder state data and fan state data. Specifically, the method comprises the following steps:
1) Header operational data includes: the height of the cutting table, the profiling height of the cutting table and the like;
2) The drum operation data includes: the rotation speed of the roller, the bearing temperature of the roller and the like;
3) The grain unloading barrel state data comprises: angle of grain unloading cylinder, etc.;
4) The fan status data includes: fan speed, etc.; the process of determining the cause of a malfunction of a combine harvester is described by the following cases:
1) Taking a structure composed of { X1} and C as an example for explanation, acquiring the actual rotating speed of the roller and the actual temperature of a bearing of the roller from the roller operation data, comparing the actual rotating speed of the roller and the actual temperature with a threshold condition corresponding to the structure composed of { X1} and C, and determining the minimum cut set { X1} as a fault cause of the combine harvester if the threshold condition is not met;
2) Taking a structural body composed of { X4} and D as an example for explanation, acquiring the actual rotating speed of the roller from the roller operation data, and determining the minimal cut set { X4} as the fault cause of the combine harvester if the threshold condition is not met;
3) Taking a structural body composed of { X6} and E as an example for explanation, acquiring the actual rotating speed of an engine of the combine harvester, and if the threshold condition is not met, determining the minimal cut set { X6} as the fault cause of the combine harvester;
by analogy, the fault reason of the combine harvester can be determined in time so as to facilitate subsequent treatment.
Optionally, in the above technical solution, the cause of the failure is displayed.
Taking preset parts as rollers as an example for explanation, specifically:
s10, building a fault tree model corresponding to the drum, first summarizing the fault type and the fault cause of the drum, and continuing the description by taking the fault type as the drum turning fault as an example, where the fault cause of the drum turning fault is shown in fig. 2.
According to fig. 2, a fault tree model corresponding to the drum turning fault is established, as shown in fig. 3.
In order to facilitate subsequent simplification of the fault tree and solving of a minimum cut set in the later period, letters are adopted in the scheme to represent each fault event.
The names of the faults represented by the letters are shown in table 2, and the fault tree model in the form of the letters is shown in fig. 4.
Table 2:
s11, solving a minimum cut set:
and according to the establishment of the fault tree model, minimum cut sets are obtained, the importance of each minimum cut set is obtained by the following formula, and data support is provided for automatic troubleshooting and positioning of the system by using the obtained importance. The minimum cut set of the fault tree for the roller turning is as follows: { X1}, { X2}, { X3}, { X4, X8}, { X4, X9}, { X5}, { X6}, { X7}, { X10}, { X11}, { X12}, { X13}, { X14}, { X15, X16, X17}, { X15, X16, X18}
In the formula:
is a probability density function of the fault;
taking a fault probability density function of 1 for the ith bottom event;
a failure probability density function of 0 is taken for the ith bottom event.
The minimum cut set is obtained by a different method, but the final result is the minimum cut set, and the method is not limited to the method according to the present embodiment.
And forming a structure body by the minimum cut set and the event, calculating the related probability by using a related formula, calibrating related threshold data, and judging the fault according to the data monitored in real time.
(6) Drum rotation speed determination
In any fault determination, a determination flow or a determination method for a certain fault is necessary, and related threshold data can be changed and need to be calibrated at any time, and is not limited to the data listed in the present embodiment. In order to ensure the normal rotation speed of the drum, the scheme judges whether the rotation speed of the drum is in a normal range, and the judging flow is shown in fig. 5.
According to the roller turning fault tree model listed in fig. 3, belt faults have influence on automatic and non-automatic turning recovery, and in order to realize correct fault pre-judgment, a belt fault logic flow is analyzed, and fig. 6 is a belt fault judgment program flow, wherein T is the temperature of a bearing of a four-belt input shaft of an engine, generally 90 ℃, and the slip ratio is x.
According to the above-mentioned drum turning fault and belt fault determination process, the present solution needs to perform comprehensive processing on the above-mentioned determination to realize the completed drum fault determination, and the flow thereof is shown in fig. 7.
In the process of monitoring the data of the combine harvester, as shown in fig. 8, the sensing processing unit in fig. 8 is used for collecting data of various sensing units, and the header height detection device is used for detecting the height of the header from the ground to realize the self-adaptive control of the header; the header profiling detection device is used for realizing profiling harvesting; the roller rotating speed detection device is used for detecting the rotating speed of the axial flow roller and providing a rotating speed basis for regulating and controlling the axial flow roller; the main clutch state detection device is used for detecting whether the main clutch is opened or not in the operation process; the grain unloading barrel state detection device is used for detecting whether the grain unloading barrel is in a normal operation state or not; the grain unloading clutch state detection device is used for detecting whether the grain unloading clutch can be normally started or not; the device for detecting the speed regulation state of the reel is used for detecting the rotating speed value of the reel in the operation process and providing data basis for the rotating speed regulation of the reel; the fan detection device detects the running state of a fan in the cleaning loss process; the cleaning loss detection device is used for realizing cleaning detection of grains in the harvesting process; the yield measuring device is used for realizing yield detection in the grain harvesting process; the running speed detection device is used for acquiring real-time vehicle speed; the light state detection device is used for detecting whether the light state on the whole vehicle can normally run or not; the oil temperature detection device is used for detecting the temperature states of hydraulic oil, engine oil and the like.
In the above embodiments, although the steps are numbered as S1, S2, etc., but only the specific embodiments are given in the present application, and a person skilled in the art may adjust the execution sequence of S1, S2, etc. according to the actual situation, which is also within the protection scope of the present invention, it is understood that some embodiments may include some or all of the above embodiments.
As shown in fig. 9, the system for diagnosing faults of a combine harvester according to the embodiment of the present invention includes an establishing module, a classification calibration module and a logic judgment module;
the establishing module is used for: establishing a fault tree model of each fault type corresponding to each preset component of the combine harvester;
the classification calibration module is used for: carrying out hierarchical classification on each fault of a preset component corresponding to a fault tree model by using any fault tree model to obtain a minimum cut set corresponding to each fault, forming a structural body by using any minimum cut set and the upper-level category of the minimum cut set to obtain a plurality of structural bodies, and calibrating a threshold condition corresponding to each structural body;
the logic judgment module is used for: and obtaining actual data corresponding to each structural body from the data for monitoring the combine harvester in real time, comparing the actual data with the threshold condition of each structural body respectively, and determining the minimum cut set of the structural bodies which do not meet the threshold condition as the fault reason of the combine harvester.
The method can be used for rapidly analyzing and diagnosing the faults of the working parts and the related electric control functions of the combine harvester, and is high in efficiency.
Optionally, in the above technical solution, the multiple preset components of the combine harvester include a header, a drum, a grain unloading drum, and a fan.
Optionally, in the above technical solution, the cause of the failure is displayed.
Optionally, in the above technical solution, the data for monitoring the combine harvester in real time includes: header operating data, roller operating data, grain unloading barrel state data and fan state data.
The above-mentioned steps for realizing the corresponding functions of each parameter and each unit module in the fault diagnosis system of the combine harvester of the present invention can refer to each parameter and step in the above-mentioned embodiment of the fault diagnosis method of the combine harvester, and are not described herein again.
The controller provided by the embodiment of the invention comprises a control chip, and the control chip is used for executing the fault diagnosis method of the combine harvester.
The combine harvester of the embodiment of the invention is characterized by comprising the controller.
As will be appreciated by one skilled in the art, the present invention may be embodied as a system, method or computer program product.
Accordingly, the present disclosure may be embodied in the form of: may be embodied entirely in hardware, entirely in software (including firmware, resident software, micro-code, etc.) or in a combination of hardware and software, and may be referred to herein generally as a "circuit," module "or" system. Furthermore, in some embodiments, the invention may also be embodied in the form of a computer program product in one or more computer-readable media having computer-readable program code embodied in the medium.
Any combination of one or more computer-readable media may be employed. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium include an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
Although embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are exemplary and not to be construed as limiting the present invention, and that changes, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (10)
1. A method of diagnosing a malfunction of a combine harvester, comprising:
establishing a fault tree model of each fault type corresponding to each preset component of the combine harvester;
carrying out hierarchical classification on each fault of a preset component corresponding to a fault tree model by using any fault tree model to obtain a minimum cut set corresponding to each fault, forming a structural body by using any minimum cut set and the upper-level category of the minimum cut set to obtain a plurality of structural bodies, and calibrating a threshold condition corresponding to each structural body;
and acquiring actual data corresponding to each structural body from the data for monitoring the combine harvester in real time, comparing the actual data with the threshold condition of each structural body respectively, and determining the minimum cut set of the structural bodies which do not meet the threshold condition as the fault cause of the combine harvester.
2. The method of claim 1, wherein the plurality of predetermined components of the combine harvester include a header, a drum, a dump bin, and a fan.
3. The method according to claim 1, wherein the cause of the failure is displayed.
4. A method as set forth in claim 1, wherein the data for real-time monitoring of the combine comprises: header operating data, roller operating data, unloading cylinder state data and fan state data.
5. A fault diagnosis system of a combine harvester is characterized by comprising an establishing module, a classification calibration module and a logic judgment module;
the establishing module is used for: establishing a fault tree model of each fault type corresponding to each preset component of the combine harvester;
the classification calibration module is used for: carrying out hierarchical classification on each fault of a preset component corresponding to a fault tree model by using any fault tree model to obtain a minimum cut set corresponding to each fault, forming a structural body by using any minimum cut set and the upper-level category of the minimum cut set to obtain a plurality of structural bodies, and calibrating a threshold condition corresponding to each structural body;
the logic judgment module is used for: and obtaining actual data corresponding to each structural body from the data for monitoring the combine harvester in real time, comparing the actual data with the threshold condition of each structural body respectively, and determining the minimum cut set of the structural bodies which do not meet the threshold condition as the fault reason of the combine harvester.
6. A fault diagnosis system for a combine harvester according to claim 5, characterised in that the plurality of predetermined components of the combine harvester comprise a header, a drum, a grain discharge drum and a fan.
7. The system of claim 5, wherein the cause of the fault is displayed.
8. A system as claimed in claim 5, wherein the data for real-time monitoring of the combine comprises: header operating data, roller operating data, unloading cylinder state data and fan state data.
9. A controller comprising a control chip, wherein the control chip is used for executing the fault diagnosis method of the combine harvester of any one of claims 1 to 4.
10. A combine harvester comprising a controller as claimed in claim 9.
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| CN101846992A (en) * | 2010-05-07 | 2010-09-29 | 上海理工大学 | Fault tree construction method based on fault case of numerical control machine |
| CN103235881A (en) * | 2013-04-21 | 2013-08-07 | 中国科学院合肥物质科学研究院 | Minimal cut set based system for monitoring faults of nuclear reactors |
| CN103544389A (en) * | 2013-10-18 | 2014-01-29 | 丽水学院 | Fault tree and fuzzy neural network based automobile crane fault diagnosis method |
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