CN111289283A - Remote fault diagnosis system of combine harvester based on Internet of things and mobile phone - Google Patents

Remote fault diagnosis system of combine harvester based on Internet of things and mobile phone Download PDF

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Publication number
CN111289283A
CN111289283A CN202010195934.0A CN202010195934A CN111289283A CN 111289283 A CN111289283 A CN 111289283A CN 202010195934 A CN202010195934 A CN 202010195934A CN 111289283 A CN111289283 A CN 111289283A
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fault diagnosis
combine harvester
mobile phone
things
internet
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许太白
陈谊
张微微
高东菊
周鹏
俞平高
徐菲
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Shanghai Vocational College Of Agriculture And Forestry
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Shanghai Vocational College Of Agriculture And Forestry
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M99/00—Subject matter not provided for in other groups of this subclass
    • G01M99/005—Testing of complete machines, e.g. washing-machines or mobile phones
    • A—HUMAN NECESSITIES
    • A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
    • A01D—HARVESTING; MOWING
    • A01D41/00—Combines, i.e. harvesters or mowers combined with threshing devices
    • A01D41/12—Details of combines
    • A01D41/127—Control or measuring arrangements specially adapted for combines

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Abstract

The invention discloses a remote fault diagnosis system of a combine harvester based on the Internet of things and a mobile phone, which comprises a microprocessor, a data acquisition system, a fault diagnosis system, a cloud platform communication module and a mobile phone client, wherein the microprocessor is respectively connected with the data acquisition system, the fault diagnosis system and the cloud platform communication module, the data acquisition system acquires important working parameters of the combine harvester, the fault diagnosis system calculates and analyzes the important working parameters acquired by the data acquisition system and obtains corresponding diagnosis result data, the microprocessor sends the diagnosis result data to the cloud platform communication module, and the mobile phone client is connected with the cloud platform communication module and can receive the diagnosis result data. The invention overcomes the defects that the existing combine harvester has limited number of remote detection terminals, is not movable, has low information sharing degree and is independently divided by real-time monitoring and fault diagnosis.

Description

Remote fault diagnosis system of combine harvester based on Internet of things and mobile phone
Technical Field
The invention relates to the field of remote fault diagnosis systems of working states of agricultural machinery such as a combine harvester and the like, in particular to a remote fault diagnosis system of a combine harvester based on the Internet of things and a mobile phone.
Background
At present, the working environment of the combine harvester is severe and the working condition is complex, and drivers have difficulty in distinguishing whether the operation sound of each part of the combine harvester is abnormal or not by using ears, so that in recent years, a plurality of monitoring control systems aiming at the working state of the combine harvester exist. The frequent fault parts of the combine harvester are mainly header, conveying chute, threshing cylinder, grain conveying auger and the like, and mainly cause blockage faults. The speed of the forward speed can also have very important influence on the harvester: too high advancing speed often causes too large feeding amount of the harvester, causes the excessive load of the harvester and low operation quality; too low forward speed will result in lower feeding amount of the harvester and lower working efficiency. Therefore, the parts easy to block and the advancing speed are mainly the key objects for monitoring the harvester. With the development of agriculture, the existing manual regular-maintenance harvester is not suitable for the development requirements of the current agriculture by maintaining the harvester by means of personal experience, and the development of the current agriculture needs to have a more intelligent fault diagnosis method applied to the harvester, namely, the harvester is judged in advance when a fault seedling head is found, and the potential fault hazard which possibly exists is eliminated. Therefore, the method has important significance for real-time monitoring and remote fault diagnosis of key working parameters of the combine harvester in order to effectively improve the fault diagnosis accuracy of the combine harvester, prevent the occurrence of faults in time and prolong the fault-free time.
At present, the domestic Chinese patent inventions and researches aiming at the remote fault diagnosis system of the combine harvester are few, are in the relatively blank field, and only Chinese patent inventions with an authorization notice number of CN207319055U and a name of 'the fault diagnosis system of the combine harvester based on a CAN bus' discloses a fault diagnosis system of the combine harvester based on the CAN bus, which passes through the CAN bus and a main control computer. The combination of an engine rotating speed sensor, a gearbox rotating speed sensor, a header input rotating speed sensor, a conveying auger rotating speed sensor and a main control machine realizes effective monitoring and fault diagnosis of the working state of the combine harvester; chinese patent with the publication number CN105210543A entitled "combine real-time monitoring system" discloses a combine real-time monitoring system, which accesses the measurement signal of a sensor to the programmable controller through a signal line, processes the signal through the programmable controller, and transmits the processed data value to the operation end through an exchanger in real time, thereby realizing real-time and accurate monitoring of the combine.
Although the two patents can realize real-time monitoring and fault diagnosis of the working state of the combine harvester, the problems of limited and fixed remote monitoring terminal, low information sharing degree and the like still exist.
Disclosure of Invention
The invention aims to solve the problems, and provides a combine harvester remote fault diagnosis system based on the Internet of things and a mobile phone.
In order to achieve the purpose, the technical scheme of the invention is as follows:
the remote fault diagnosis system of the combine harvester based on the Internet of things and the mobile phone comprises a microprocessor, a data acquisition system, a fault diagnosis system, a cloud platform communication module and a mobile phone client, wherein the microprocessor is respectively connected with the data acquisition system, the fault diagnosis system and the cloud platform communication module, the data acquisition system acquires important working parameters of the combine harvester, the fault diagnosis system calculates and analyzes the important working parameters acquired by the data acquisition system and obtains corresponding diagnosis result data, the microprocessor sends the diagnosis result data to the cloud platform communication module, and the mobile phone client is connected with the cloud platform communication module and can receive the diagnosis result data.
In a preferred embodiment of the present invention, the microprocessor is a C8051F020 microprocessor.
In a preferred embodiment of the invention, the data acquisition system comprises a plurality of hall sensors and a conditioning circuit processing module, the hall sensors are installed on the combine harvester for data acquisition, acquired signals are processed by the conditioning circuit and then are accessed to 0, 1 and 2 external interrupts of a C8051F020 microprocessor, and the signals are acquired and processed in a circulating manner.
In a preferred embodiment of the present invention, the fault diagnosis system includes a fault diagnosis method based on a transient variation tendency of a target signal and 3 judgment case subroutines,
the method comprises the steps of judging 3 judgment condition subprograms of data after passing through a processing module of a conditioning circuit by a fault diagnosis method based on the instantaneous change trend of a target signal, triggering an early warning interruption subprogram when a system judges that an early warning state occurs in a signal, and sending data 1; when the system judges that the signal has an alarm state, triggering an alarm subprogram and sending data of 2; under the condition that the rotating speed is normal, data 0 is sent.
In a preferred embodiment of the invention, the fault diagnosis method based on the instantaneous change trend of the target signal is used for carrying out the instantaneous value R of each sensor after passing through the processing module of the conditioning circuitijCalculating the first order difference Delta D of each instantaneous value of the signals and the second order difference Delta of each instantaneous value of the signals2D, combining the rated values R of the operating parametersiJudgment of the 3 judgment case subroutines is performed.
In a preferred embodiment of the present invention, the fault diagnosis method based on the transient variation tendency of the target signal is calculated by the following formula:
Figure BDA0002417597980000031
Figure BDA0002417597980000032
the early warning program is ① 0.8.8Ri＜Rij≤0.9Ri
②
Figure BDA0002417597980000033
Alarm program ① Rij≤0.8Ri
②
Figure BDA0002417597980000034
Wherein the value of i is 1-6, which respectively represents the advancing speed, the rotating speed of the conveying trough, the rotating speed of the grain conveying auger, the rotating speed of the tangential flow roller, the rotating speed of the longitudinal flow roller and the rotating speed of the cutting table auger;
j is each sensor signal sampling sequence;
in the above formula RijRepresenting the instantaneous value of each sensor; Δ D represents the first order difference of the instantaneous values of the respective signals, which may represent the loss rate; delta2D represents the second order difference of the instantaneous value of each signal; riIndicating the respective operating parameter ratings.
In a preferred embodiment of the present invention, the cloud platform communication module includes a SIM900A module, a UART0 serial interface, and a OneNet internet of things platform, and the SIM900A module is connected to the processor through the UART0 serial interface, and transmits TCP to connect to the OneNet internet of things platform, so as to complete communication between the diagnosis result data and the cloud platform.
In a preferred embodiment of the present invention, the client includes a data monitoring module, a data storage module and a fault information processing module, and receives data from the OneNet internet of things platform to the data monitoring module and the fault information processing module in a SOCKET communication manner, and the data is displayed by a real-time broken line graph, so that a user can store a processing process, a reason record and a critical value parameter display for fault information in the data storage module.
In a preferred embodiment of the present invention, the data storage module comprises a manual failure diagnosis collection interface and a parameter setting interface.
In a preferred embodiment of the present invention, the client is an Android client or an IOS client.
The invention has the beneficial effects that:
(1) the invention utilizes the real-time signals of the multi-channel Hall sensors, utilizes the conditioning circuit, utilizes the difference theory and utilizes the fault diagnosis method based on the instantaneous change trend of the target signal, thereby more effectively making correct diagnosis for the operation flow. (ii) a
(2) According to the invention, the SIM900A module is connected with the C8051F020 single chip microcomputer through a UART0 serial interface, and the TCP is sent to be connected to the OneNet Internet of things platform, so that the communication between the data acquisition system and the fault diagnosis system and the cloud platform is completed, and the sharing and the real-time storage of the data are realized;
(3) the invention realizes the real-time data butt joint of the remote monitoring terminal and the OneNet Internet of things platform in a SOCKET communication mode, can overcome the defects that the number of the traditional remote detection terminals is limited and fixed, and realizes the real-time analysis and remote multi-person monitoring of the working parameters of the farmland working instruments with the movable remote terminals;
(4) the remote fault diagnosis system of the combine harvester based on the Internet of things and the Android mobile phone is simple and convenient to operate, low in cost and high in reliability, combines real-time data monitoring and fault condition monitoring of the combine harvester, achieves multi-source information fusion, enhances data complementation, can be widely applied to remote monitoring and fault diagnosis of the working state of the existing combine harvester in the market, and is good in universality.
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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 diagram of the operation of the present invention;
fig. 2 is a schematic diagram of a fault diagnosis method based on the transient variation trend of the target signal.
Detailed Description
In order to make the technical means, the creation characteristics, the achievement purposes and the effects of the invention easy to understand, the invention is further explained below by combining the specific drawings.
Referring to fig. 1 and 2, the remote fault diagnosis system of the combine harvester based on the internet of things and the mobile phone, provided by the invention, comprises a microprocessor 1, a data acquisition system 2, a fault diagnosis system 3, a cloud platform communication module 4 and a mobile phone client 5.
The microprocessor 1 is respectively connected with the data acquisition system 2, the fault diagnosis system 3 and the cloud platform communication module 4, the data acquisition system 2 acquires important working parameters of the combine harvester, the fault diagnosis system 3 calculates and analyzes the important working parameters acquired by the data acquisition system 2 to obtain corresponding diagnosis result data, the microprocessor 1 sends the diagnosis result data to the cloud platform communication module 4, and the mobile phone client 5 is connected with the cloud platform communication module 4 and can receive the diagnosis result data.
The microprocessor 1 is specifically a C8051F020 microprocessor.
The data acquisition system 2, which is used to complete the collection of important working parameters of the combine harvester, may specifically include a plurality of hall sensors 201 and a conditioning circuit processing module 202.
The Hall sensors 201 are respectively installed on corresponding positions of the combine harvester and can acquire data of the corresponding positions of the combine harvester, acquired signals are processed by the conditioning circuit processing module 202 and then are accessed to the 0, 1 and 2 external parts of the C8051F020 microprocessor for interruption, and the signals are acquired and processed in a circulating mode.
The fault diagnosis system 3 may specifically include a fault diagnosis method 301 based on the transient variation tendency of the target signal and a 3-judgment-case subroutine 302.
The method comprises the steps of executing judgment of 3 judgment condition subprograms 302 on data after passing through a conditioning circuit processing module 202 through a fault diagnosis method 301 based on the instantaneous change trend of a target signal, triggering an early warning interruption subprogram when a system judges that an early warning state occurs in a signal, and sending data 1; when the system judges that the signal has an alarm state, triggering an alarm subprogram and sending data of 2; under the condition that the rotating speed is normal, data 0 is sent.
In addition, based on the target signal transientThe time-varying trend fault diagnosis method 301 performs a process of adjusting the instantaneous value R of each sensor after passing through the conditioning circuit processing module 202ijCalculating the first order difference Delta D of each instantaneous value of the signals and the second order difference Delta of each instantaneous value of the signals2D, combining the rated values R of the operating parametersiJudgment of the 3 judgment case subroutines is performed.
The above-mentioned fault diagnosis method 301 based on the transient variation trend of the target signal can be specifically calculated by the following formula:
Figure BDA0002417597980000051
Figure BDA0002417597980000052
the early warning program is ① 0.8.8Ri＜Rij≤0.9Ri
②
Figure BDA0002417597980000061
Alarm program ① Rij≤0.8Ri
②
Figure BDA0002417597980000062
Wherein the value of i is 1-6, which respectively represents the advancing speed, the rotating speed of the conveying trough, the rotating speed of the grain conveying auger, the rotating speed of the tangential flow roller, the rotating speed of the longitudinal flow roller and the rotating speed of the cutting table auger;
j is each sensor signal sampling sequence;
in the above formula RijRepresenting the instantaneous value of each sensor; Δ D represents the first order difference of the instantaneous values of the respective signals, which may represent the loss rate; delta2D represents the second order difference of the instantaneous value of each signal; riIndicating the respective operating parameter ratings.
The process of the method for determining the instantaneous trend shown in fig. 2 is as follows:
when the negative value of Delta D appears in 4 times of continuous recording of the rotation speed signal and the absolute value of Delta D is added to exceed the absolute valueWhen the rated rotating speed is 5 percent, the system judges that the signal has an early warning state, and when the absolute value sum of the signals exceeds 10 percent of the rated rotating speed, the system judges that the signal has an alarm state; if Δ D is negative and Δ is recorded 3 times in succession2The value D is gradually reduced, the rotating speed of the part is in a descending trend, the descending is slowed down, and the system judges that the signal has an early warning state; if Δ D is negative and Δ is recorded 3 times in succession2The value D is gradually increased, which shows that the component is blocked and has an acceleration trend, and the system judges that the signal has an alarm state. Similarly, when the positive value of the delta D of the loss signal recorded for 4 times continuously occurs, the system judges that the signal has an early warning state; if Δ D is positive and Δ is recorded for 3 consecutive times2The value D is gradually reduced, which shows that although the loss is in an increasing state, the increase is slowed down, and the system judges that the signal has an early warning state; if Δ D is positive and Δ is recorded for 3 consecutive times2The value of D is gradually large, which indicates that the loss is rapidly increased, and the system judges that the signal is in an alarm state. The determination of the signals can be done simultaneously, without interference with each other, and the measured values of the signals can be displayed simultaneously on a display.
The cloud platform communication module 4 specifically comprises a SIM900A module 401, a UART0 serial interface 402, and a OneNet internet of things platform 403.
The SIM900A module 401 is connected with the C8051F020 single chip microcomputer 1 through a UART0 serial interface 402, transmits TCP to be connected to the OneNet Internet of things platform 403, and completes communication between the data acquisition system 2 and the fault diagnosis system 3 and the cloud platform.
The mobile phone client 5 specifically comprises a data monitoring module 501, a data storage module 502 and a fault information processing module 505, and receives data with the OneNet internet of things platform 403 to the data monitoring module 501 and the fault information processing module 505 in a SOCKET communication mode, the data is displayed through a real-time broken line graph, and a user can store information such as processing process, reason record and critical value parameter display of fault information in the data storage module 502.
The data storage module 502 specifically includes a manual failure diagnosis collection interface 503 and a parameter setting interface 504.
The mobile phone client 5 may be an Android client or an IOS client.
The remote fault diagnosis system of the combine harvester based on the Internet of things and the mobile phone, which is formed by the structure, overcomes the defects that the number of remote detection terminals of the combine harvester is limited and the combine harvester cannot be moved, the information sharing degree is low, and the real-time monitoring and fault diagnosis are independently divided in the prior art.
The foregoing shows and describes the general principles and broad features of the present invention and advantages thereof. 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 illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.

Claims (10)

1. The remote fault diagnosis system of the combine harvester based on the Internet of things and the mobile phone is characterized by comprising a microprocessor, a data acquisition system, a fault diagnosis system, a cloud platform communication module and a mobile phone client, wherein the microprocessor is respectively connected with the data acquisition system, the fault diagnosis system and the cloud platform communication module, the data acquisition system acquires important working parameters of the combine harvester, the fault diagnosis system calculates and analyzes the important working parameters acquired by the data acquisition system and obtains corresponding diagnosis result data, the microprocessor sends the diagnosis result data to the cloud platform communication module, and the mobile phone client is connected with the cloud platform communication module and can receive the diagnosis result data.
2. The remote fault diagnosis system of the combine harvester based on the internet of things and the mobile phone as claimed in claim 1, wherein the microprocessor is a C8051F020 microprocessor.
3. The remote fault diagnosis system of the combine harvester based on the internet of things and the mobile phone as claimed in claim 2, wherein the data acquisition system comprises a plurality of Hall sensors and a conditioning circuit processing module, the Hall sensors are installed on the combine harvester for data acquisition, acquired signals are processed by the conditioning circuit and then are accessed to 0, 1 and 2 external interrupts of a C8051F020 microprocessor, and the signals are acquired and processed in a circulating manner.
4. The remote fault diagnosis system of the combine harvester based on the Internet of things and the mobile phone as claimed in claim 3, wherein the fault diagnosis system comprises a fault diagnosis method based on the instantaneous change trend of the target signal and 3 judgment condition subprograms,
the method comprises the steps of judging 3 judgment condition subprograms of data after passing through a processing module of a conditioning circuit by a fault diagnosis method based on the instantaneous change trend of a target signal, triggering an early warning interruption subprogram when a system judges that an early warning state occurs in a signal, and sending data 1; when the system judges that the signal has an alarm state, triggering an alarm subprogram and sending data of 2; under the condition that the rotating speed is normal, data 0 is sent.
5. The remote fault diagnosis system of the combine harvester based on the internet of things and the mobile phone as claimed in claim 4, wherein the fault diagnosis method based on the instantaneous change trend of the target signal is used for performing fault diagnosis on the instantaneous value R of each sensor after passing through the processing module of the conditioning circuitijCalculating the first order difference Delta D of each instantaneous value of the signals and the second order difference Delta of each instantaneous value of the signals2D, combining the rated values R of the operating parametersiJudgment of the 3 judgment case subroutines is performed.
6. The remote fault diagnosis system of the combine harvester based on the internet of things and the mobile phone as claimed in claim 5, wherein the fault diagnosis method based on the instantaneous change trend of the target signal is calculated by the following formula:
Figure FDA0002417597970000021
Figure FDA0002417597970000022
the early warning program is ① 0.8.8Ri＜Rij≤0.9Ri
②
Figure FDA0002417597970000023
Alarm program ① Rij≤0.8Ri
②
Figure FDA0002417597970000024
Wherein the value of i is 1-6, which respectively represents the advancing speed, the rotating speed of the conveying trough, the rotating speed of the grain conveying auger, the rotating speed of the tangential flow roller, the rotating speed of the longitudinal flow roller and the rotating speed of the cutting table auger;
j is each sensor signal sampling sequence;
in the above formula RijRepresenting the instantaneous value of each sensor; Δ D represents the first order difference of the instantaneous values of the respective signals, which may represent the loss rate; delta2D represents the second order difference of the instantaneous value of each signal; riIndicating the respective operating parameter ratings.
7. The remote fault diagnosis system for the combine harvester based on the internet of things and the mobile phone as claimed in claim 1, wherein the cloud platform communication module comprises a SIM900A module, a UART0 serial interface and a OneNet internet of things platform, the SIM900A module is connected with the processor through the UART0 serial interface, and transmits TCP to connect to the OneNet internet of things platform, so as to complete communication between diagnosis result data and the cloud platform.
8. The remote fault diagnosis system of the combine harvester based on the internet of things and the mobile phone as claimed in claim 1, wherein the client comprises a data monitoring module, a data storage module and a fault information processing module, data reception with the OneNet internet of things platform is realized through a SOCKET communication mode to the data monitoring module and the fault information processing module, the data is displayed through a real-time broken line diagram, and a user can store processing process, reason record and display critical value parameters of fault information in the data storage module.
9. The remote fault diagnosis system of the combine harvester based on the internet of things and the mobile phone as claimed in claim 8, wherein the data storage module comprises a manual fault diagnosis acquisition interface and a parameter setting interface.
10. The remote fault diagnosis system of the combine harvester based on the internet of things and the mobile phone as claimed in claim 1, wherein the client is an Android client or an IOS client.
CN202010195934.0A 2020-03-19 2020-03-19 Remote fault diagnosis system of combine harvester based on Internet of things and mobile phone Pending CN111289283A (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112268719A (en) * 2020-09-29 2021-01-26 河南科技大学 Remote fault diagnosis method for header of combine harvester
CN115373369A (en) * 2022-08-24 2022-11-22 中国第一汽车股份有限公司 Vehicle fault diagnosis system and method

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112268719A (en) * 2020-09-29 2021-01-26 河南科技大学 Remote fault diagnosis method for header of combine harvester
CN115373369A (en) * 2022-08-24 2022-11-22 中国第一汽车股份有限公司 Vehicle fault diagnosis system and method
CN115373369B (en) * 2022-08-24 2024-05-03 中国第一汽车股份有限公司 Vehicle fault diagnosis system and method

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