CN113297967B - Built-in reed harvester safety assessment method based on characteristic confidence range - Google Patents
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Abstract
The invention discloses a safety evaluation method of a built-in reed harvester based on a characteristic confidence range, which comprises the following steps: s1, monitoring local vibration of a structure of a built-in reed harvester in real time; s2, determining local vibration characteristics; s3, determining a 95% density value based on Weibull distribution; s4, determining a safety early warning value based on a 95% density value; s5, determining reliability of the reed harvester under different characteristics. The method has high monitoring precision and has important practical significance for determining the health state of the reed harvester.
Description
Technical Field
The invention relates to a reed harvester, in particular to a safety evaluation method of a built-in reed harvester based on a characteristic confidence range.
Background
With the rapid development of agricultural economy and rapid progress of science and technology, the agricultural mechanization rate is greatly improved, the status of reed harvesters in modern agricultural production is increasingly outstanding, and the safety problem of cutting tables in the harvesting process is increasingly emphasized. Therefore, real-time health monitoring of the header of the reed harvester is necessary during the operation of the harvester.
At present, the existing safety monitoring method for the cutting table of the reed harvester usually comprises the steps of collecting vibration signals of the reed harvester by a field arrangement sensor and diagnosing the spectrum analysis and extraction characteristics of the vibration signals, but the method is complicated and inconvenient in field collection and has the influence of external load, so that the accuracy is not high. In view of the above, the embedded and interval-divided safety early warning mode is considered in the text so as to realize the self-detection function of the reed harvester, and the health state of the header of the reed harvester can be monitored better.
Disclosure of Invention
The invention aims to: the invention aims to provide a safety evaluation method of a built-in reed harvester based on a characteristic confidence range, which is high in monitoring precision, wherein the built-in reed harvester is characterized in that a sensor is arranged on a header in the existing reed harvester.
The technical scheme is as follows: the invention provides a safety evaluation method of a built-in reed harvester based on a characteristic confidence range, which comprises the following steps:
s1, monitoring local vibration of a structure of a built-in reed harvester in real time;
s2, determining local vibration characteristics;
s3, determining a 95% density value based on Weibull distribution;
s4, determining a safety early warning value based on a 95% density value;
s5, determining reliability of the reed harvester under different characteristics.
Further, the real-time monitoring of the local vibration of the structure of the reed harvester with the built-in S1:
the method comprises the steps of importing a three-dimensional model of a reed harvester operation structure into finite element analysis software ANSYS, meshing the three-dimensional model of the reed harvester structure by adopting an automatic grid mode, applying working condition load in a pretreatment module, then solving a post-treatment result, and determining the area where the structure is most likely to be damaged under the conditions of the maximum stress and the strain value of the reed harvester header structure.
Monitoring the area most likely to be damaged by the cutting table of the reed harvester in a mode of internally arranging sensors, internally arranging 3 vibration sensors in each damaged area according to an equilateral triangle arrangement method in the area, and collecting the vibration quantity of the ith damaged area in real time to obtain the vibration quantity A corresponding to each sensor ij (i is the number of the damaged area, j is the j-th sensor corresponding to the i-th area，j＝1，2，3)
Further, S2 determination of vibration local characteristics:
on the basis of S1, the vibration quantity A corresponding to the time range from 0 to t in the MATLAB program ij Fitting the parameter function to obtain a 5-order specific lower vibration quantity A ij (t) the best specific function corresponding to the above.
A ij (t)＝α 1 t 5 +α 2 t 4 +α 3 t 3 +α 4 t 2 +α 5 t+α 6
Wherein A is ij (t) is the best specific function of the vibration quantity under the specific 5 th order; t is time; alpha 1 ，α 2 ，α 3 ，α 4 ，α 5 ，α 6 The best fit coefficient for a specific function of order 5.
Vibration quantity A corresponding to the time range from 0 to t in Labview software ij Proceeding to feature gamma i Is performed in the first step.
Wherein A is ij (t) is the best specific function of the vibration quantity under the specific 5 th order; t is time; j is a j sensor corresponding to the i-th area; i is the number of the damaged area; n is the maximum value of the lesion field number.
Further, S3 is based on the determination of 95% density values of Weibull distribution:
the calculated vibration local characteristic gamma of each region i Leading the obtained product into a MATLAB program to automatically fit an equation G (i) under the condition of a Weibull equation to obtain a Weibull equation mode of
Wherein G (i) is a feature gamma i Weibull equation model of (C); d, d 1 Shape parameters of Weibull equation; d, d 2 Is WeiSize parameters of the bull equation; d, d 3 Position parameters of Weibull equations; e is the number of fingers; i is the number of the damaged area.
Then substituting G (i) into the 95% density value T of the characteristic of the ith damaged area of the following formula i And (5) performing calculation.
Wherein G (i) is a feature gamma i Weibull equation model of (C); n is the maximum value of the number of the damaged area; i is the number of the damaged area; c [ G (i)] max For all features gamma i Probability maxima in equation mode; c [ G (i)] min For all features gamma i Probability minima in the equation mode; t (T) i Is a 95% density value characteristic of the ith lesion field.
Further, S4 is based on the determination of the safety precaution value of the 95% density value:
on the basis of S2-S3, determining a safety early warning value M of each sensor corresponding to the moment t ij (t)。
Wherein M is ij (t) is a safety precaution value of each sensor corresponding to the time t; a is that ij (t) is the best specific function of the vibration quantity under the specific 5 th order; gamma ray i A vibration local feature for each region relative to all damaged regions; t (T) i Is a 95% density value characteristic of the ith lesion field.
Further, determining the reliability of the reed harvester under different characteristics in S5:
substituting the data calculated in S1-S4 into the following formula for feature-based reliability B t And solving.
Wherein B is t Is a degree of reliability based on the feature; m is M ij (t) is a safety precaution value of each sensor corresponding to the time t; a is that ij (t) is the best specific function of the vibration quantity under the specific 5 th order; i is the number of the damaged area; j is a j sensor corresponding to the i-th area; n is the maximum value of the lesion field number.
The beneficial effects are that: the data of the cutting table of the reed harvester are collected through the embedded structure arrangement vibration sensor, the characteristic confidence range of the cutting table is determined in real time by utilizing Labview, the 95% density value is determined by utilizing Weibull distribution, the damage warning threshold is determined based on the characteristic confidence range, the reliability under different characteristics is determined, and the health state of the reed harvester is further determined in real time.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
fig. 2 is a schematic diagram of a sensor arrangement for a reed harvester header.
Detailed Description
As shown in fig. 1, the present embodiment is based on a safety evaluation method of a built-in reed harvester based on a feature confidence range, and the method includes the following steps:
s1, monitoring local vibration of a structure of a built-in reed harvester in real time:
the method comprises the steps of importing a three-dimensional model of a reed harvester operation structure into finite element analysis software ANSYS, performing gridding division on the three-dimensional model of the reed harvester structure by adopting an automatic grid mode, applying working condition load in a pretreatment module, then solving a post-treatment result, and determining the area where the structure is most likely to be damaged under the conditions of the maximum stress and strain value of the reed harvester header 1.
Monitoring the area most likely to be damaged by the cutting table of the reed harvester through a mode of internally arranging the sensors 2, internally arranging 3 vibration sensors 2 in each damaged area according to an equilateral triangle arrangement method in the area, and collecting the vibration quantity of the ith damaged area in real time to obtain the vibration quantity A corresponding to each sensor 2 ij (i is the number of the damaged area, j is the j-th sensor corresponding to the i-th area2，j＝1，2，3)
S2, determining local vibration characteristics:
on the basis of S1, the vibration quantity A corresponding to the time range from 0 to t in the MATLAB program ij Fitting the parameter function to obtain a 5-order specific lower vibration quantity A ij (t) the best specific function corresponding to the above.
A ij (t)＝α 1 t 5 +α 2 t 4 +α 3 t 3 +α 4 t 2 +α 5 t+α 6
Wherein A is ij (t) is the best specific function of the vibration quantity under the specific 5 th order; t is time; alpha 1 ，α 2 ，α 3 ，α 4 ，α 5 ，α 6 The best fit coefficient for a specific function of order 5.
Vibration quantity A corresponding to the time range from 0 to t in Labview software ij Proceeding to feature gamma i Is performed in the first step.
Wherein A is ij (t) is the best specific function of the vibration quantity under the specific 5 th order; t is time; j is the j sensor 2 corresponding to the i-th area; i is the number of the damaged area; n is the maximum value of the lesion field number.
S3, determining 95% density values based on Weibull distribution:
the calculated vibration local characteristic gamma of each region i Leading the obtained product into a MATLAB program to automatically fit an equation G (i) under the condition of a Weibull equation to obtain a Weibull equation mode of
Wherein G (i) is a feature gamma i Weibull equation model of (C); d, d 1 Shape parameters of Weibull equation; d, d 2 Size parameters of Weibull equation;d 3 position parameters of Weibull equations; e is the number of fingers; i is the number of the damaged area.
Then substituting G (i) into the 95% density value T of the characteristic of the ith damaged area of the following formula i And (5) performing calculation.
Wherein G (i) is a feature gamma i Weibull equation model of (C); n is the maximum value of the number of the damaged area; i is the number of the damaged area; c [ G (i)] max For all features gamma i Probability maxima in equation mode; c [ G (i)] min For all features gamma i Probability minima in the equation mode; t (T) i Is a 95% density value characteristic of the ith lesion field.
S4, determining a safety early warning value based on a 95% density value:
on the basis of S2-S3, determining a safety early warning value M of each sensor 2 corresponding to the moment t ij (t)。
Wherein M is ij (t) is a safety precaution value of each sensor 2 corresponding to the time t; a is that ij (t) is the best specific function of the vibration quantity under the specific 5 th order; gamma ray i A vibration local feature for each region relative to all damaged regions; t (T) i Is a 95% density value characteristic of the ith lesion field.
S5, determining reliability of reed harvesters under different characteristics:
substituting the data calculated in S1-S4 into the following formula for feature-based reliability B t And solving.
Wherein B is t Is based onThe degree of reliability of the feature; m is M ij (t) is a safety precaution value of each sensor 2 corresponding to the time t; a is that ij (t) is the best specific function of the vibration quantity under the specific 5 th order; i is the number of the damaged area; j is the j sensor 2 corresponding to the i-th area; n is the maximum value of the lesion field number.
Claims (2)
1. A safety evaluation method of a built-in reed harvester based on a characteristic confidence range is characterized by comprising the following steps of: the method comprises the following steps:
s1, monitoring local vibration of a structure of a built-in reed harvester in real time;
s2, determining local vibration characteristics;
s3, determining a 95% density value based on Weibull distribution;
s4, determining a safety early warning value based on a 95% density value;
s5, determining the reliability of the reed harvester under different characteristics,
and S2, determining the vibration local characteristics: on the basis of S1, the vibration quantity A corresponding to the time range from 0 to t in the MATLAB program ij Fitting the parameter function to obtain a 5-order specific lower vibration quantity A ij (t) the best specific function corresponding to the specific function,
A ij (t)＝α 1 t 5 +α 2 t 4 +α 3 t 3 +α 4 t 2 +α 5 t+α 6
wherein A is ij (t) is the best specific function of the vibration quantity under the specific 5 th order; t is time; alpha 1 ，α 2 ，α 3 ，α 4 ，α 5 ，α 6 The best fit coefficient for a specific function of order 5,
vibration quantity A corresponding to the time range from 0 to t in Labview software ij Proceeding to feature gamma i Is obtained from the acquisition of (a),
wherein A is ij (t) is the best specific function of the vibration quantity under the specific 5 th order; t is time; j is a j sensor corresponding to the i-th area; i is the number of the damaged area; n is the maximum value of the number of damaged areas,
the S3 is based on the determination of 95% density values of the Weibull distribution: the calculated vibration local characteristic gamma of each region i Leading the obtained product into a MATLAB program to automatically fit an equation G (i) under the condition of a Weibull equation to obtain a Weibull equation mode,
wherein G (i) is a feature gamma i Weibull equation model of (C); d, d 1 Shape parameters of Weibull equation; d, d 2 Size parameters of Weibull equation; d, d 3 Position parameters of Weibull equations; e is the number of fingers; i is the number of the damaged area,
then substituting G (i) into the 95% density value T of the characteristic of the ith damaged area of the following formula i The calculation is performed such that,
wherein G (i) is a feature gamma i Weibull equation model of (C); n is the maximum value of the number of the damaged area; i is the number of the damaged area; c [ G (i)] max For all features gamma i Probability maxima in equation mode; c [ G (i)] min For all features gamma i Probability minima in the equation mode; t (T) i Is a 95% density value characteristic of the ith lesion field,
the S4 is based on the determination of the safety precaution value of the 95% density value:
on the basis of S2-S3, determining a safety early warning value M of each sensor corresponding to the moment t ij (t)，
Wherein M is ij (t) is a safety precaution value of each sensor corresponding to the time t; a is that ij (t) is the best specific function of the vibration quantity under the specific 5 th order; gamma ray i A vibration local feature for each region relative to all damaged regions; t (T) i Is a 95% density value characteristic of the ith lesion field,
and (5) determining the reliability of the reed harvester under different characteristics:
substituting the data calculated in S1-S4 into the following formula for feature-based reliability B t The solution is carried out so that,
wherein B is t Is a degree of reliability based on the feature; m is M ij (t) is a safety precaution value of each sensor corresponding to the time t; a is that ij (t) is the best specific function of the vibration quantity under the specific 5 th order; i is the number of the damaged area; j is a j sensor corresponding to the i-th area; n is the maximum value of the lesion field number.
2. The safety assessment method for the built-in reed harvester based on the characteristic confidence range as set forth in claim 1, wherein: s1 built-in reed harvester structure local vibration real-time monitoring: introducing a three-dimensional model of a reed harvester operation structure into finite element analysis software ANSYS, performing gridding division on the three-dimensional model of the reed harvester structure by adopting an automatic grid mode, applying working condition load in a pretreatment module, then solving a post-treatment result, determining the area where the structure is most easily damaged under the conditions of maximum stress and strain value of the reed harvester header structure,
monitoring the most damaged area of the header of the reed harvester by a built-in sensor, arranging 3 vibration sensors in each damaged area according to an equilateral triangle arrangement method in the area,collecting vibration quantity of the ith damaged area in real time to obtain vibration quantity A corresponding to each sensor ij i is the number of the damaged area, j is the j-th sensor corresponding to the i-th area, j=1, 2,3.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2014172838A1 (en) * | 2013-04-22 | 2014-10-30 | Abb Technology Ltd | Method and apparatus for defect pre-warning of power device |
| CN105893711A (en) * | 2016-05-07 | 2016-08-24 | 安徽科技学院 | Wind power generator blade and structural design method thereof |
| CN107991095A (en) * | 2017-12-28 | 2018-05-04 | 哈工大机器人（合肥）国际创新研究院 | The life test apparatus and method of robot precision cycloid decelerator |
| CN111597682A (en) * | 2020-04-14 | 2020-08-28 | 新疆大学 | Method for predicting remaining life of bearing of gearbox of wind turbine |
| CN112362274A (en) * | 2020-10-10 | 2021-02-12 | 中铁大桥局集团有限公司 | Method and system for monitoring, early warning and evaluating vortex-induced vibration of long-span bridge in operation period |
-
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- 2021-05-24 CN CN202110568964.6A patent/CN113297967B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2014172838A1 (en) * | 2013-04-22 | 2014-10-30 | Abb Technology Ltd | Method and apparatus for defect pre-warning of power device |
| CN105893711A (en) * | 2016-05-07 | 2016-08-24 | 安徽科技学院 | Wind power generator blade and structural design method thereof |
| CN107991095A (en) * | 2017-12-28 | 2018-05-04 | 哈工大机器人（合肥）国际创新研究院 | The life test apparatus and method of robot precision cycloid decelerator |
| CN111597682A (en) * | 2020-04-14 | 2020-08-28 | 新疆大学 | Method for predicting remaining life of bearing of gearbox of wind turbine |
| CN112362274A (en) * | 2020-10-10 | 2021-02-12 | 中铁大桥局集团有限公司 | Method and system for monitoring, early warning and evaluating vortex-induced vibration of long-span bridge in operation period |
Non-Patent Citations (2)
| Title |
|---|
| "改进威布尔分布的矿冶零部件可靠性寿命预测研究";伍建军等;《机械科学与技术》;第36卷(第3期);436-441 * |
| Hua-Peng Chen et al.."Aerodynamic Performance and Fatigue Damage Assessment of Wind Turbine Composite Blades using Corrected BEM Method".《 The 8 th International Conference on Structural Health Monitoring of Intelligent Infrastructure 》.2018,第1-10页. * |
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