CN112393891B - Wireless monitoring system and method for fatigue damage of key parts of agricultural operation machinery - Google Patents
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Abstract
The invention relates to the technical field of agricultural machinery, in particular to a wireless monitoring system and method for fatigue damage of key components of agricultural operation machinery, wherein the system comprises a wireless strain sensor node, a wireless gateway, a local test terminal, a DTU network uploading module and a remote server; the wireless strain sensor node is arranged at a vulnerable point in a key part of the agricultural operation machine and is communicated with the wireless gateway through an RF radio frequency transceiver function; the wireless gateway is connected with the local test terminal through a serial port, the DTU network uploading module is connected with the local test terminal through the serial port, the remote server side receives uploading data, and cloud storage is carried out on the data through a database technology. The monitoring system provided by the invention can monitor a plurality of agricultural working machines in a regional range in real time, and can be used for analyzing, evaluating and predicting fatigue damage of key parts of the agricultural working machines under a big data sample.
Description
Technical Field
The invention relates to the technical field of agricultural machinery, in particular to a wireless monitoring system and method for fatigue damage of key components of agricultural operation machinery.
Background
As core equipment for agricultural operation, the agricultural machinery has large workload, complex and changeable operation working conditions, and severe working environments can aggravate fatigue damage of key parts of an operation mechanism and parts such as support, transmission and the like, so that the working efficiency and the safety are reduced. In order to improve the usability, safety and reliability of the agricultural machine, the strain data of each key component under the field operation working condition of the agricultural machine is monitored, the load level under the current working condition is evaluated accurately in real time, the driving operation of a driver can be standardized, the component use condition information can be provided for the driver, and early warning prompt is carried out under the dangerous working condition, so that accidents are effectively avoided.
With the continuous development of technologies such as big data and internet of things (Internet of Things, ioT for short), the traditional agriculture in China is rapidly transformed into modern agriculture, and the emerging technologies are continuously applied to agricultural production and agricultural equipment. The sensors are connected through an internet of things (IoT) technology and form a huge data network, strain data of key parts of the agricultural machinery are collected in real time, and comprehensive and effective basic data support can be provided for research, development and design of agricultural machinery products.
At present, fatigue damage monitoring usually adopts an excitation-response principle to detect and evaluate damage, namely, an excitation signal with a fixed frequency band is sent out to a measured component, damage evaluation is performed through the frequency response characteristic of an echo signal, more technical links are needed in a monitoring method based on the principle, and system construction is complex. On the other hand, this method can only monitor the instantaneous damage state of the measured component, and ignores the accumulated damage factor that causes fatigue failure of the component. In addition, most of the current monitoring systems can only monitor a single local working machine, and cannot monitor a plurality of working machines in a regional range in real time.
Disclosure of Invention
The invention aims to provide a wireless monitoring system for fatigue damage of key components of agricultural operation machinery, which realizes data acquisition and working condition monitoring synchronously at local and cloud based on three-layer network construction of wireless strain sensor nodes, a local test terminal and a remote server. The invention also provides a wireless monitoring method for fatigue damage of the key parts of the agricultural operation machine, which evaluates and pre-warns the load working condition of the agricultural machine by fatigue damage pre-warning evaluation indexes based on accumulated damage criteria, and estimates the residual life of the key parts based on the S-N characteristic curve of the material.
The invention aims at realizing the following technical scheme:
the system comprises a wireless strain sensor node 1, a wireless gateway 2, a local test terminal 3, a DTU network uploading module 5 and a remote server end 6; wherein,
The wireless strain sensor node 1 is arranged at a vulnerable point in a key part of the agricultural operation machine, strain parameters of each measuring point are collected in real time, hardware signal conditioning is carried out on the data, and the wireless strain sensor node is communicated with the wireless gateway 2 through an RF radio frequency transceiver function;
the wireless gateway 2 is connected with the local test terminal 3 through a serial port, receives and integrates the strain data transmitted by the wireless strain sensor node 1, and transmits the data to the local test terminal 3;
The local test terminal 3 receives strain data transmitted from each wireless strain sensor node 1 through the wireless gateway 2;
the DTU network uploading module 5 is connected with the local test terminal 3 through a serial port, and uploads strain data and fatigue damage analysis data acquired by the local test terminal 3 in real time to the remote server side 6 through a TCP/HTTP network;
the remote server side 6 receives the uploaded data and performs cloud storage on the data through a database technology.
The local test terminal 3 is fixedly placed in the cab of the work machine.
The system also comprises a local monitoring display 4, wherein the local monitoring display 4 is fixedly arranged at the side of the instrument board of the cab of the working machine, visually displays real-time working indexes of the working machine and provides an overload early warning prompt function.
The vulnerable points in the key parts of the agricultural working machine are one or more of a power output shaft, a transmission shaft, an engine, a gearbox, a steering drive axle and an electrohydraulic suspension device of the agricultural working machine.
A method for wirelessly monitoring fatigue damage of key parts of an agricultural operation machine by using the wireless monitoring system for fatigue damage of key parts of the agricultural operation machine comprises the following steps:
step1: collecting strain signals of each measuring point
Selecting vulnerable points in key parts of a plurality of agricultural operation machines as measuring points, and arranging wireless strain sensor nodes 1; selecting sampling time length delta t and sampling interval delta t', carrying out strain measurement by adopting a preset sampling frequency f s, and collecting strain signals of all measuring points; the wireless gateway 2 receives strain data acquired from each wireless strain sensor node 1 in real time and transmits the strain data to the local test terminal 3 through a serial port;
Step2: pretreatment of signals
After the local test terminal 3 receives the strain data collected by each measuring point of the agricultural operation machine, firstly, removing trend items in the strain data by adopting a polynomial fitting method; then, removing singular points in the strain data by adopting a standard deviation detection method; finally, filtering high-frequency components in the strain data by using a Butterworth low-pass filter according to the low-frequency characteristic of the load of the agricultural working machine;
2-1 removing trend terms in the strain data by adopting a polynomial fitting method
The trend term in the strain data is set asThe m-order polynomial fitting is adopted, and then
In the method, in the process of the invention,The unit is MPa for trend items in the original strain data; a i is the coefficient to be determined; k represents the kth term of the strain data; n * represents a positive integer; i is the sum order; m represents the order of the polynomial, and m=1 to 3 is often taken in actual signal data processing;
The original strain data obtained in the test process is represented by x k, and then is obtained according to the least square method correlation theory:
wherein Q (a) represents the sum of squares of errors of the trend term data and the original test strain data; The unit is MPa for trend items in the original strain data; x k is original strain data, and the unit is MPa; k represents the kth term of the strain data; a i is the coefficient to be determined; i is the sum order; n is the total actual data amount for removing trend items; m represents the order of the polynomial, and m=1 to 3 is often taken in actual signal data processing;
solving the minimum value of Q (a), solving a undetermined coefficient a i, further solving a trend term, and obtaining a test signal after removing the trend term through a formula 3:
Wherein x max is a test signal after removing trend items, and the unit is MPa; x k is original strain data, and the unit is MPa; The unit is MPa for trend items in the original strain data;
2-2 removal of singular points in strain data using standard deviation detection
For the strain data with trend items removed, calculating the dynamic standard deviation of the strain data in real time, setting a range interval, and judging as singular points and removing the singular points when the test signal exceeds the range;
2-3 filtering out high frequency components in the strain data using a Butterworth low pass filter
The high-frequency components in the test signal are filtered, so that the signal to noise ratio of the test signal can be improved, and the pretreated signal can reflect the actual working condition;
Step 3: fatigue life prediction: setting the sampling time length as delta t and the sampling frequency as fs, converting the strain data preprocessed in the step 2, counting the rain flow, and converting the time domain load process into a rain flow domain to obtain an average value-amplitude matrix M r; the average value and the amplitude value are quantized into r numerical values, namely the matrix has r kinds of stress circulation, and the matrix is an r-order square matrix;
Firstly, correcting a mean value-amplitude matrix M r in a sampling duration Deltat, and for a load amplitude S i of an ith stress cycle, calculating a corrected load amplitude S i' according to a formula 4:
Wherein S i' is the corrected load amplitude, and the unit is MPa; s i is the load amplitude before correction, and the unit is MPa; r is the order of the mean-magnitude matrix M r; s u is the strength limit in MPa; i is the sum order;
Reconstructing the corrected load amplitude S i ' calculated by the formula 4 into a new mean-amplitude matrix M r ', and predicting the fatigue life according to the new mean-amplitude matrix M r '; the expected fatigue life T p is calculated from equation 5:
Wherein T p is the expected fatigue life, and the unit is h; delta t is the sampling duration, and the unit is s; alpha is a fatigue strength coefficient, beta is a fatigue strength index, and both alpha and beta are related to material and process characteristics and are obtained by fitting a part S-N curve; s i' is the corrected load amplitude in MPa; n i is the cycle frequency corresponding to the ith stress cycle; r is the order of the new mean-magnitude matrix M r'; i is the sum order;
The expected remaining life T r is calculated over the fatigue life T p calculated in each sampling period using the iterative operation of equation 6:
Wherein T r is the expected remaining life in h; t p is the calculated expected fatigue life in h for each sampling period; the numbers 1, 2..n in T p1、Tp2...Tpn are the number of operations for the expected fatigue life;
Step4: fatigue damage early warning
Calculating the allowable life expectancy (T) according to the design parameters of the measured component, wherein the unit is h, and the estimated life expectancy is used as a fatigue damage early warning evaluation index; comparing the fatigue life expectancy T p calculated in step 3, in h, with the allowable life expectancy [ T ]: if T p > T, the load of the component meets the working requirement; if T p is less than or equal to [ T ], the load exceeds the allowable threshold, the local test terminal 3 sends out overload early warning prompt to the driver through the local monitoring display 4, and uploads the early warning record to the remote server 6;
Wherein the allowable life expectancy is calculated by equation 7:
Wherein [ T ] is the allowable life expectancy in h; t p is the expected fatigue life in h; n is a safety coefficient, n=0.8-0.9, and is dimensionless and determined by test performance parameters of the component;
Step 5: uploading monitoring data: the local test terminal 3 packages and encapsulates the monitoring data, and the monitoring data is uploaded to the remote server 6 by the DTU network uploading module 5 through a TCP/HTTP communication technology; the remote server side 6 stores the monitoring data in a cloud based on a database technology, and further analyzes the fatigue damage by combining the historical data.
In step 1, the vulnerable points in the key parts of the agricultural working machine are one or more of a power output shaft, a transmission shaft, an engine, a gearbox, a steering drive axle and an electrohydraulic suspension device of the agricultural working machine.
In the step 2-3, the cut-off frequency of the Butterworth low-pass filter is 40Hz, and the order is 8-10.
The invention has the beneficial effects that:
1. When the monitoring system provided by the invention monitors key components of the agricultural operation machine, only strain data is required to be acquired in real time without generating excitation signals in advance, and the system consists of wireless sensor nodes, so that the data acquisition steps are greatly simplified, and the system construction efficiency is improved.
2. The monitoring method provided by the invention is based on a fatigue analysis theory, and can calculate the expected fatigue life and the expected residual life of the measured component in real time. The index parameters can be used for evaluating the load working condition, and can also prompt the service condition of key components of the agricultural working machine, so that the key components can be conveniently maintained in actual use.
3. The monitoring system provided by the invention can monitor a plurality of agricultural working machines in a regional range in real time, and can be used for analyzing, evaluating and predicting fatigue damage of key parts of the agricultural working machines under a big data sample.
Drawings
Fig. 1 is a schematic structural diagram of a wireless monitoring system for fatigue damage of key components of an agricultural work machine according to the present invention.
FIG. 2 is a flow chart of a wireless monitoring method of fatigue damage of key components of the agricultural work machine of the present invention.
FIG. 3 is a flow chart of fatigue life prediction and fatigue damage early warning of the wireless monitoring method for fatigue damage of key components of the agricultural work machine.
Reference numerals:
1. Wireless strain sensor node
2. Wireless gateway
3. Local test terminal
4. Local monitoring display
5. DTU network uploading module
6. Remote server side
Detailed Description
The following describes in further detail the embodiments of the present invention with reference to the drawings and examples.
The system comprises a wireless strain sensor node 1, a wireless gateway 2, a local test terminal 3, a local monitor display 4, a DTU network uploading module 5 and a remote server end 6. Wherein,
The wireless strain sensor node 1 is arranged at a vulnerable point in a key part of the agricultural operation machine, strain parameters of each measuring point are collected in real time, hardware signal conditioning is carried out on the data, and the wireless strain sensor node is communicated with the wireless gateway 2 through an RF radio frequency transceiver function.
Specifically, the vulnerable points in the key parts of the agricultural working machine are one or more of a power output shaft, a transmission shaft, an engine, a gearbox, a steering drive axle and an electrohydraulic suspension device of the agricultural working machine.
The wireless gateway 2 is connected with the local test terminal 3 through a serial port, receives and integrates the strain data transmitted by the wireless strain sensor node 1, and transmits the data to the local test terminal 3.
The local test terminal 3 is fixedly arranged in a cab of the operation machine, receives strain data transmitted by each wireless strain sensor node 1 through the wireless gateway 2, evaluates and pre-warns the load working condition of the agricultural operation machine based on the monitoring algorithm, and predicts the residual life of key components.
The local monitoring display 4 is fixedly arranged at the side of the instrument panel of the cab of the working machine, visually displays real-time working indexes of the working machine (obtained through analysis of the local test terminal 3), and provides an overload early warning prompt function.
The DTU network uploading module 5 is connected with the local test terminal 3 through a serial port, and uploads strain data and fatigue damage analysis data acquired by the local test terminal 3 in real time to the remote server side 6 through a TCP/HTTP network.
The remote server side 6 receives the uploaded data and performs cloud storage on the data through a database technology. Meanwhile, strain data of key parts of the working machine monitored in real time is beneficial to enterprises and scientific research institutions to compile actual measurement load spectrums of the working machine, and experimental evaluation basis is provided in the design and research and development processes.
Referring to fig. 1, the wireless monitoring system for fatigue damage of key components of the agricultural operation machine is constructed in the following manner: the local test terminal 3 receives real-time data of each wireless strain sensor node 1 through the wireless gateway 2; the local test terminal 3 processes and analyzes the acquired data; the local monitoring display 4 visually displays the processed and analyzed data and provides an overload early warning prompt function; the local test terminal 3 is connected with the DTU network uploading module 5 in a serial port mode, and uploads data to the remote server end 6; and the remote server side 6 receives the uploading data, performs cloud storage on the data and performs predictive analysis on fatigue damage by combining the historical data. The local test network is centered on the local test terminal 3, and is arranged by adopting a star network topology structure, and the remote server side 6 is mapped to each local test network through a TCP/HTTP network in the test area range.
Referring to fig. 2, a wireless monitoring method for fatigue damage of key components of an agricultural operation machine comprises the following steps:
step 1: strain signals of all the measuring points are collected.
Selecting vulnerable points in key parts of a plurality of agricultural operation machines as measuring points, and arranging wireless strain sensor nodes 1; selecting sampling time length delta t and sampling interval delta t', carrying out strain measurement by adopting a preset sampling frequency f s, and collecting strain signals of all measuring points; the wireless gateway 2 receives strain data acquired from each wireless strain sensor node 1 in real time and transmits the strain data to the local test terminal 3 through a serial port.
Specifically, the vulnerable points in the key parts of the agricultural working machine are one or more of a power output shaft, a transmission shaft, an engine, a gearbox, a steering drive axle and an electrohydraulic suspension device of the agricultural working machine.
Step2: preprocessing of signals.
After the local test terminal 3 receives the strain data collected by each measuring point of the agricultural operation machine, firstly, removing trend items in the strain data by adopting a polynomial fitting method; then, removing singular points in the strain data by adopting a standard deviation detection method; finally, according to the low-frequency characteristic of the agricultural work machine load, a Butterworth low-pass filter is used for filtering out high-frequency components in the strain data.
2-1 Removing trend items in the strain data by adopting a polynomial fitting method.
The trend term in the strain data is set asThe m-order polynomial fitting is adopted, and then
In the method, in the process of the invention,The unit is MPa for trend items in the original strain data; a i is the coefficient to be determined; k represents the kth term of the strain data; n * represents a positive integer; i is the sum order; m represents the order of the polynomial, and m=1 to 3 is often taken in actual signal data processing.
The original strain data obtained in the test process is represented by x k, and then is obtained according to the least square method correlation theory:
wherein Q (a) represents the sum of squares of errors of the trend term data and the original test strain data; The unit is MPa for trend items in the original strain data; x k is original strain data, and the unit is MPa; k represents the kth term of the strain data; a i is the coefficient to be determined; i is the sum order; n is the total actual data amount for removing trend items; m represents the order of the polynomial, and m=1 to 3 is often taken in actual signal data processing.
Solving the minimum value of Q (a), solving a undetermined coefficient a i, further solving a trend term, and obtaining a test signal after removing the trend term through a formula 3:
Wherein x max is a test signal after removing trend items, and the unit is MPa; x k is original strain data, and the unit is MPa; Is a trend term in the original strain data, and is expressed in MPa.
2-2 Singular points in the strain data were removed using standard deviation detection.
And (3) for the strain data with the trend item removed, calculating the dynamic standard deviation in real time, setting a range interval (the upper limit of the interval is generally not more than 6 times of variance, and a specific interval threshold value is selected according to the characteristics and experience of the test signal), and judging the test signal to be a singular point and removing the singular point when the test signal exceeds the range.
2-3 High frequency components in the strain data are filtered out using a butterworth low pass filter.
Preferably, the cut-off frequency of the butterworth low-pass filter is 40Hz, and the order is 8 to 10. The high-frequency components in the test signal are filtered, so that the signal to noise ratio of the test signal can be improved, and the pretreated signal can reflect the actual working condition.
Step 3: fatigue life prediction. Setting the sampling time length as Deltat (unit: s) and the sampling frequency as fs (unit: hz), converting the strain data preprocessed in the step 2, counting the rain flow, and converting the time domain load history into a rain flow domain to obtain a mean value-amplitude matrix M r. The average value and the amplitude value are quantized into r numerical values, namely the matrix has r kinds of stress circulation, and the matrix is an r-order square matrix.
Referring to fig. 3, the mean-magnitude matrix M r within the sampling duration Δt is first modified, and for the load magnitude S i of the ith stress cycle, the modified load magnitude S i' is calculated by equation 4:
wherein S i' is the corrected load amplitude, and the unit is MPa; s i is the load amplitude before correction, and the unit is MPa; r is the order of the mean-magnitude matrix M r; s u is the strength limit in MPa; i is the sum order.
The corrected load amplitude S i ' calculated by the formula 4 is reconstructed into a new mean-amplitude matrix M r ', and fatigue life prediction is performed according to the new mean-amplitude matrix M r '. The expected fatigue life T p is calculated from equation 5:
wherein T p is the expected fatigue life, and the unit is h; delta t is the sampling duration, and the unit is s; alpha is a fatigue strength coefficient, beta is a fatigue strength index, and both alpha and beta are related to material and process characteristics and are obtained by fitting a part S-N curve; s i' is the corrected load amplitude in MPa; n i is the cycle frequency corresponding to the ith stress cycle; r is the order of the new mean-magnitude matrix M r'; i is the sum order.
The expected remaining life T r is calculated over the fatigue life T p calculated in each sampling period using the iterative operation of equation 6:
Wherein T r is the expected remaining life in h; t p is the calculated expected fatigue life in h for each sampling period; the numbers 1 and 2..n in T p1、Tp2...Tpn are the number of times of calculation for the expected fatigue life.
Step4: and (5) fatigue damage early warning.
Referring to FIG. 3, according to the design parameters of the measured components, calculating the allowable life expectancy [ T ] in h as a fatigue damage early warning evaluation index; comparing the fatigue life expectancy T p calculated in step 3, in h, with the allowable life expectancy [ T ]: if T p > T, the load of the component meets the working requirement; if T p is less than or equal to [ T ], the load exceeds the allowable threshold, the local test terminal 3 sends out overload early warning prompt to the driver through the local monitoring display 4, and the early warning record is uploaded to the remote server side 6.
Wherein the allowable life expectancy is calculated by equation 7:
Wherein [ T ] is the allowable life expectancy in h; t p is the expected fatigue life in h; n is a safety coefficient, n=0.8 to 0.9, and is dimensionless and is determined by test performance parameters of the component.
Step 5: and (5) uploading monitoring data. The local test terminal 3 packages and encapsulates the monitoring data, and the monitoring data is uploaded to the remote server 6 by the DTU network uploading module 5 through a TCP/HTTP communication technology; the remote server side 6 stores the monitoring data in a cloud based on a database technology, and further analyzes the fatigue damage by combining the historical data.
Claims (2)
1. The wireless monitoring system for the fatigue damage of the key parts of the agricultural operation machine comprises a wireless strain sensor node (1), a wireless gateway (2), a local test terminal (3), a DTU network uploading module (5) and a remote server (6); the wireless strain sensor node (1) is arranged at a vulnerable point in a key component of the agricultural operation machine, strain parameters of each measuring point are collected in real time, hardware signal conditioning is carried out on the data, and the wireless strain sensor node is communicated with the wireless gateway (2) through an RF radio frequency transceiver function; the wireless gateway (2) is connected with the local test terminal (3) through a serial port, receives and integrates strain data transmitted by the wireless strain sensor node (1), and transmits the data to the local test terminal (3); the local test terminal (3) receives strain data transmitted from each wireless strain sensor node (1) through the wireless gateway (2); the DTU network uploading module (5) is connected with the local test terminal (3) through a serial port, and strain data and fatigue damage analysis data collected by the local test terminal (3) in real time are uploaded to the remote server (6) through a TCP/HTTP network; the remote server (6) receives the uploaded data and performs cloud storage on the data through a database technology; the local test terminal (3) is fixedly arranged in a cab of the working machine; the system also comprises a local monitoring display (4), wherein the local monitoring display (4) is fixedly arranged at the side of an instrument board of a cab of the working machine, visually displays real-time working indexes of the working machine and provides an overload early warning prompt function; the vulnerable points in the key parts of the agricultural working machine are one or more of a power output shaft, a transmission shaft, an engine, a gearbox, a steering drive axle and an electrohydraulic suspension device of the agricultural working machine;
the method is characterized in that: the monitoring method comprises the following steps:
step1: collecting strain signals of each measuring point
Selecting vulnerable points in key parts of a plurality of agricultural operation machines as measuring points, and arranging wireless strain sensor nodes (1); selecting sampling time length delta t and sampling interval delta t', carrying out strain measurement by adopting a preset sampling frequency f s, and collecting strain signals of all measuring points; the wireless gateway (2) receives strain data acquired from each wireless strain sensor node (1) in real time and transmits the strain data to the local test terminal (3) through a serial port;
Step2: pretreatment of signals
After the local test terminal (3) receives the strain data collected by each measuring point of the agricultural operation machine, firstly, removing trend items in the strain data by adopting a polynomial fitting method; then, removing singular points in the strain data by adopting a standard deviation detection method; finally, filtering high-frequency components in the strain data by using a Butterworth low-pass filter according to the low-frequency characteristic of the load of the agricultural working machine;
2-1 removing trend terms in the strain data by adopting a polynomial fitting method
The trend term in the strain data is set asThe m-order polynomial fitting is adopted, and then
In the method, in the process of the invention,The unit is MPa for trend items in the original strain data; a i is the coefficient to be determined; k represents the kth term of the strain data; n Watch (watch) represents a positive integer; i is the sum order; m represents the order of the polynomial, and m=1 to 3 is often taken in actual signal data processing;
The original strain data obtained in the test process is represented by x k, and then is obtained according to the least square method correlation theory:
wherein Q (a) represents the sum of squares of errors of the trend term data and the original test strain data; The unit is MPa for trend items in the original strain data; x k is original strain data, and the unit is MPa; k represents the kth term of the strain data; a i is the coefficient to be determined; i is the sum order; n is the total actual data amount for removing trend items; m represents the order of the polynomial, and m=1 to 3 is often taken in actual signal data processing;
solving the minimum value of Q (a), solving a undetermined coefficient a i, further solving a trend term, and obtaining a test signal after removing the trend term through a formula 3:
Wherein x max is a test signal after removing trend items, and the unit is MPa; x k is original strain data, and the unit is MPa; The unit is MPa for trend items in the original strain data;
2-2 removal of singular points in strain data using standard deviation detection
For the strain data with trend items removed, calculating the dynamic standard deviation of the strain data in real time, setting a range interval, and judging as singular points and removing the singular points when the test signal exceeds the range;
2-3 filtering out high frequency components in the strain data using a Butterworth low pass filter
The high-frequency components in the test signal are filtered, so that the signal to noise ratio of the test signal can be improved, and the pretreated signal can reflect the actual working condition;
Step 3: fatigue life prediction: setting the sampling time length as deltat and the sampling frequency as fs, converting the strain data preprocessed in the step 2, counting the rain flow, and converting the time domain load course into a rain flow domain to obtain an average value-amplitude matrix M r; the average value and the amplitude value are quantized into r numerical values, namely the matrix has r kinds of stress circulation, and the matrix is an r-order square matrix;
Firstly, the average value-amplitude matrix M r in the sampling duration delta t is corrected, and for the load amplitude S i of the ith stress cycle, the corrected load amplitude S i' is calculated by the formula 4:
Wherein S i' is the corrected load amplitude, and the unit is MPa; s i is the load amplitude before correction, and the unit is MPa; r is the order of the mean-magnitude matrix M r; s u is the strength limit in MPa; i is the sum order;
Reconstructing the corrected load amplitude S i ' calculated by the formula 4 into a new mean-amplitude matrix M r ', and predicting the fatigue life according to the new mean-amplitude matrix M r '; the expected fatigue life T p is calculated from equation 5:
Wherein T p is the expected fatigue life, and the unit is h; Δt is the sampling duration, with the unit being s; alpha is a fatigue strength coefficient, beta is a fatigue strength index, and both alpha and beta are related to material and process characteristics and are obtained by fitting a part S-N curve; s i' is the corrected load amplitude in MPa; n i is the cycle frequency corresponding to the ith stress cycle; r is the order of the new mean-magnitude matrix M r'; i is the sum order;
The expected remaining life Tr is obtained by repeating iterative operation of the fatigue life T p calculated in each sampling period using the formula 6:
Wherein T r is the expected remaining life in h; t p is the calculated expected fatigue life in h for each sampling period; the number 1 and the number 2 … n in the T p1、Tp2...Tpn are the operation times of the expected fatigue life;
Step4: fatigue damage early warning
Calculating the allowable life expectancy (T) according to the design parameters of the measured component, wherein the unit is h, and the estimated life expectancy is used as a fatigue damage early warning evaluation index; comparing the fatigue life expectancy T p calculated in step 3, in h, with the allowable life expectancy [ T ]: if T p > [ T ], the load of the component meets the operating requirement; if T p is less than or equal to [ T ], the load exceeds the allowable threshold, the local test terminal (3) sends out overload early warning prompt to the driver through the local monitoring display (4), and uploads the early warning record to the remote server (6);
Wherein the allowable life expectancy is calculated by equation 7:
Wherein [ T ] is the allowable life expectancy in h; t p is the expected fatigue life in h; n is a safety coefficient, n=0.8-0.9, and is dimensionless and determined by test performance parameters of the component;
Step 5: uploading monitoring data: the local test terminal (3) packages and encapsulates the monitoring data, and the monitoring data is uploaded to the remote server (6) by the DTU network uploading module (5) through a TCP/HTTP communication technology; and the remote server (6) stores the monitoring data in a cloud based on a database technology, and further analyzes the fatigue damage by combining the historical data.
2. The wireless monitoring method for fatigue damage of key parts of agricultural working machinery according to claim 1, wherein the method comprises the following steps: in the step 2-3, the cut-off frequency of the Butterworth low-pass filter is 40Hz, and the order is 8-10.
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