CN113125150A - Method and system for monitoring health state of drive chain on line - Google Patents
Method and system for monitoring health state of drive chain on line Download PDFInfo
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- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M13/00—Testing of machine parts
- G01M13/02—Gearings; Transmission mechanisms
- G01M13/023—Power-transmitting endless elements, e.g. belts or chains
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
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Abstract
The invention relates to the technical field of mechanical equipment, and discloses a method and a system for monitoring the health state of a drive chain on line. The invention has the following advantages and effects: the invention creatively discloses a method for monitoring the health state of a drive chain on line, which realizes the on-line monitoring of the sag and the health state of the drive chain, meets the requirements of the health state management and the predictive maintenance of the drive chain of mechanical equipment on data acquisition and operation, effectively improves the equipment management level, identifies the safety risk during the operation of an escalator, and ensures that the safety risk is controllable.
Description
Technical Field
The invention relates to the technical field of mechanical equipment, in particular to a method and a system for monitoring the health state of a driving chain on line.
Background
The escalator is generally applied to public places with dense personnel, and casualties are often caused once accidents occur, and mass casualties are easy to occur. Therefore, safety management of the escalator is always very important, wherein the health state of the main driving chain of the escalator has important influence on the safety of the escalator, the health state of the driving chain of the escalator is monitored on line, and the health condition of the driving chain of the escalator is mastered in real time, so that pre-control of safety risks is realized, and the safety management of the escalator is very important.
The state of the art monitoring of known escalator drive chains is performed in the out-of-run state and includes measuring wear and sag of the drive chain. Sag is the most important indicator describing the eligibility of the drive chain, while the deterioration of the health of the chain is in a continuous process with the operation of the escalator, thus leading to two consequences: firstly, serious degradation of a driving chain in operation cannot be found in time, so that the escalator operates with diseases and the risk of accidents exists; secondly, in order to reduce or stop accidents as much as possible, the frequency of workpiece replacement work is inevitably increased, so that the operation and maintenance cost is increased.
Based on the structure of the roller type driving chain, the space of the detected surface is greatly changed, so that the real data of the sag of the driving chain in operation is difficult to detect, and no known method for monitoring the actual sag of the driving chain in an online manner exists at present.
Disclosure of Invention
The invention aims to provide a method and a system for monitoring the health state of a drive chain on line.
The technical purpose of the invention is realized by the following technical scheme: a method for monitoring the health state of a drive chain on line comprises the following steps,
firstly, selecting a driver and determining a monitoring reference point;
secondly, measuring the distance from the reference point to the arc surface of the roller of the driving chain;
thirdly, determining a distance value between a measured point of the driving chain and a measurement reference point;
fourthly, repeating the third step to obtain a discrete time sequence data set of the distance values in the third step, and extracting the minimum value and the maximum value in the discrete data set formed by a plurality of time sequence data in a set time period;
step five, repeating the step four, and constructing a minimum time sequence array [ li]And maximum timing sequence [ b ]i];
Sixth, a polynomial model is selected to express the change process of the sag of the driving chain along with time, namely S
si=ai,1*tn-1+ai,2*tn-2...+ai,n-1*t+ai,n+ei
Or:
in the formula IiIs the value at time t in the minimum time series, biIs the value at time t in the maximum time sequence series, n is the number of terms of the polynomial function, eiIs a random error;
and (3) performing regression operation of a plurality of models on the time sequence series constructed in the fifth step according to the models, wherein the calculation result is expressed as follows:
step seven, rolling and updating the time sequence array of the step five according to time sequence, and performing regression operation once by using the method of the step six every time the time sequence array is updated, so as to obtain a regressed time sequence array;
and eighthly, describing the length or tension change trend of the driving chain by using the regressed time sequence series of the maximum sag value of the driving chain, and describing the change trend of the up-and-down swing of the driving chain in the running process by using the regressed time sequence series of the minimum sag value of the driving chain. The time-series trend of the above parameters, which contains most of the health information of the drive chain, can be deeply mined in the data center for equipment health analysis and predictive maintenance management related to the drive chain.
The method is suitable for a chain for transmitting power in mechanical equipment, and is structurally characterized in that: the driving chain is composed of a plurality of sections of main structural parts as follows: the chain link structure at least comprises 2 outer chain plates and 2 inner chain plates, wherein one ends of the inner chain plates and the outer chain plates are connected through shaft pins, two ends of each shaft pin can be locked by split pins, sleeves are arranged outside the shaft pins between the two inner chain plates, rollers are sleeved outside the sleeves, the other ends of the pair of inner chain plates and the pair of outer chain plates respectively form a new chain link structure with the outer chain plates and the inner chain plates at two link positions, and the center distance of the two shaft pins is the pitch of the chain.
The invention is further provided with: in the first step, according to the structure and the running speed of the measured driving chain, selecting the sensor parameters of the measured distance, wherein the sensor parameters comprise the sampling frequency, the sampling mode, the distance measuring range and the progress of the sensor, and the sampling mode is one of laser, ultrasound, electromagnetism and inductance.
The invention is further provided with: in the second step, the sampling frequency is set according to the technical parameters of the sensor and the chain structure, the vertical distance between the reference point and the roller arc surface at the lowest point of the non-stressed side vertical arc section of the driving chain is measured, and the sampling frequency of the pitch of each driving chain is required to be enough to ensure that the distance sampling frequency of the roller is not less than 5 times.
The invention is further provided with: in the third step, the method for determining the distance value between the measured point of the driving chain and the measuring reference point selects one of the following three methods:
the method comprises the following steps: describing the distance by using the distance from the reference point to the arc vertex of the roller of the driving chain or the position of the chain plate corresponding to the shaft pin: setting data acquisition frequency and each continuous data acquisition time length according to the running speed of the driving chain, and extracting a minimum value from a set data set of each continuous acquisition time period for describing the distance from a reference point to the top point of the roller of the driving chain wheel;
the method 2 comprises the following steps: the sag is described by the circle center of a roller of a driving chain: setting the frequency of data acquisition (requiring that distance data is acquired for each pitch of the drive chain for not less than 5 times) and the duration of continuously acquiring data for each time (requiring that data acquired for each time is not less than 100) according to the running speed of the drive chain, extracting a point with the sum of two adjacent values being the minimum value from the set data set acquired for each time, and defining the maximum value of the two points as h1A decimal value of h2,a=h1-h2(ii) a Defining the speed of the chain as v, the sampling time interval of the two even points as t, and defining b as v t; defining the radius of the chain ball as r; the distance H between the center of the roller of the driving chain and the reference point is as follows:
subtracting the radius value of the chain from the H value obtained by the algorithm to obtain the distance between the top point of the roller of the driving chain and the reference point; compared with method 1, method 2 is more accurate;
the method 3 comprises the following steps: sag is described by the distance between the reference point and the link plate of the drive chain: taking the side surface of a chain plate as a measurement target, determining the number n of times that each section of driving chain plate can be continuously sampled according to the length of the driving chain plate/the driving continuous speed and the sampling frequency (the mantissa after the decimal point is cut off and an integer is taken), and carrying out average value calculation on the data obtained by continuous sampling, wherein the calculation model is as follows:
in the formulaN is an average value of n successive data of the distance from the base point to the link plate side face, n is the number of distance time series data used for calculating the average value (i.e. the number of times each link plate of the driving chain can be continuously sampled), i is a time series number of the distance time series data used for calculating the average value, s is a time series number of the distance time series data used for calculating the average valueiAnd the distance data from the base point used for calculating the average value to the chain plate is shown.
A calculation cycle is set, and the minimum value is found in the data set of the distance moving average value in the calculation cycle, and the minimum value is used for describing the required distance value.
The invention is further provided with: in the method 1, the frequency requirement of data acquisition is that distance data is acquired for each pitch of a driving chain for not less than 5 times, and the duration of continuously acquiring data for each time is required to be not less than 100 data acquired for each time; in the method 2, the frequency requirement of data acquisition is that distance data is acquired for each pitch of the driving chain for not less than 5 times, and the duration of each continuous data acquisition is required to be not less than 100 data acquired each time.
The invention is further provided with: in the fourth step, in a discrete data set formed by a plurality of time sequence data in a set time period, the specific number of the time sequence data is determined according to the target accuracy.
It is a second object of the present invention to provide a system for online monitoring of drive chain health, comprising
The magnitude acquisition unit is used for finishing the setting of a reference point for distance detection and the magnitude acquisition of the distance between the reference point and the vertical arc of the driving chain;
the A/D conversion unit is used for receiving the analog quantity value sent by the quantity value acquisition unit and converting the analog quantity value into digital quantity for output;
the data filtering unit is used for filtering the output data of the A/D conversion unit, screening the data of the distance from the real characterization reference point to the vertical arc of the driving chain and removing invalid data;
the operation unit is used for receiving the data result screened by the data filtering unit and performing time sequence data regression operation;
and the trend management unit is used for describing the length or tension change trend of the driving chain by using the regressed time sequence series of the maximum sag value of the driving chain and describing the change trend of the up-and-down swing of the driving chain in the running process by using the regressed time sequence series of the minimum sag value of the driving chain.
By adopting the technical scheme, the magnitude acquisition unit and the A/D conversion unit jointly implement the first step and the second step, the data filtering unit implements the third step, the fourth step and the fifth step, the arithmetic unit implements the sixth step and the seventh step, and the trend management unit implements the eighth step.
The invention is further provided with: the quantity value acquisition unit comprises a sensor and a sensor adjusting device, the sensor is a distance sensor, and the reaction time is not more than 1.5 milliseconds.
The invention has the beneficial effects that: the invention discloses a method for monitoring the health state of a driving chain on line, which comprises the steps of determining the distance value between a measured point of the driving chain and a measuring reference point, obtaining a discrete time sequence data set, constructing a minimum time sequence and a maximum time sequence, expressing the change process of the sag of the driving chain along with the time through a polynomial model, updating the time sequence in a rolling way, describing the change trend of the length or the tension by using the regression maximum value time sequence of the sag of the driving chain, describing the change trend of the vertical swing of the driving chain in the running process by using the regression minimum value time sequence of the sag of the driving chain, realizing the on-line monitoring of the sag and the health state of the driving chain, meeting the requirements of the health state management and the predictive maintenance of the driving chain of mechanical equipment on data acquisition and operation, effectively improving the equipment management level, and identifying the safety risk of an escalator in the running, the security risk is made controllable.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a schematic diagram of the system of the present invention.
Detailed Description
The technical solution of the present invention will be clearly and completely described below with reference to specific embodiments. It is to be understood that the described embodiments are merely a few embodiments of the invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without any inventive step, are within the scope of the present invention.
A method for monitoring the health state of a drive chain on line comprises the following steps,
the method comprises the steps of firstly, selecting a driver, determining a monitoring reference point, and selecting parameters of a sensor for measuring distance according to the structure and the running speed of a measured driving chain, wherein the parameters comprise the sampling frequency, the sampling mode, the distance measuring range and the precision of the sensor, and the sampling mode is one of laser, ultrasound, electromagnetism and inductance.
And secondly, measuring the distance from the reference point to the arc surface of the roller of the driving chain, setting sampling frequency according to sensor technical parameters and a chain structure, measuring the vertical distance between the reference point and the arc surface of the roller at the lowest point of the non-stressed side vertical arc section of the driving chain, and requiring that the sampling frequency of each driving chain pitch is enough to ensure that the distance sampling frequency of the roller is not less than 5 times.
Thirdly, determining a distance value between a measured point of the driving chain and a measurement reference point; the method for determining the distance value between the measured point of the drive chain and the measuring reference point selects one of the following three methods:
the method comprises the following steps: describing the distance by using the distance from the reference point to the arc vertex of the roller of the driving chain or the position of the chain plate corresponding to the shaft pin: setting data acquisition frequency and each continuous data acquisition time length according to the running speed of the driving chain, and extracting a minimum value from a set data set of each continuous acquisition time period for describing the distance from a reference point to the top point of the roller of the driving chain wheel; the frequency requirement of data acquisition is that distance data is acquired for each pitch of the driving chain for not less than 5 times, and the duration of continuously acquiring data for each time is required to be not less than 100 data acquired for each time.
The method 2 comprises the following steps: the sag is described by the circle center of a roller of a driving chain: setting the frequency of data acquisition (requiring that distance data is acquired for each pitch of the drive chain for not less than 5 times) and the duration of continuously acquiring data for each time (requiring that data acquired for each time is not less than 100) according to the running speed of the drive chain, extracting a point with the sum of two adjacent values being the minimum value from the set data set acquired for each time, and defining the maximum value of the two points as h1A decimal value of h2,a=h1-h2(ii) a Defining the speed of the chain as v, the sampling time interval of the two even points as t, and defining b as v t; defining the radius of the chain ball as r; the distance H between the center of the roller of the driving chain and the reference point is as follows:
subtracting the radius value of the chain from the H value obtained by the algorithm to obtain the distance between the top point of the roller of the driving chain and the reference point; the frequency requirement of data acquisition is that distance data is acquired for each pitch of the driving chain for not less than 5 times, and the duration of continuously acquiring data for each time is required to be not less than 100 data acquired for each time.
The method 3 comprises the following steps: sag is described by the distance between the reference point and the link plate of the drive chain: taking the side surface of a chain plate as a measurement target, determining the number n of times that each section of driving chain plate can be continuously sampled according to the length of the driving chain plate/the driving continuous speed and the sampling frequency (the mantissa after the decimal point is cut off and an integer is taken), and carrying out average value calculation on the data obtained by continuous sampling, wherein the calculation model is as follows:
in the formulaN is an average value of n successive data of the distance from the base point to the link plate side face, n is the number of distance time series data used for calculating the average value (i.e. the number of times each link plate of the driving chain can be continuously sampled), i is a time series number of the distance time series data used for calculating the average value, s is a time series number of the distance time series data used for calculating the average valueiAnd the distance data from the base point used for calculating the average value to the chain plate is shown.
A calculation cycle is set, and the minimum value is found in the data set of the distance moving average value in the calculation cycle, and the minimum value is used for describing the required distance value.
And fourthly, repeating the third step to obtain a discrete time sequence data set of the distance value in the third step, and extracting the minimum value and the maximum value in the discrete data set formed by a plurality of time sequence data (the specific number of the time sequence data is determined according to the target accuracy) in a set time period.
Step five, repeating the step four, and constructing a minimum time sequence array [ li]And maximum timing sequence [ b ]i]。
Sixth, a polynomial model is selected to express the change process of the sag of the driving chain along with time, namely S
si=ai,1*tn-1+ai,2*tn-2...+ai,n-1*t+ai,n+ei
Or:
in the formula IiIs the value at time t in the minimum time series, biIs the value at time t in the maximum time sequence series, n is the number of terms of the polynomial function, eiIs a random error;
and (3) performing regression operation of a plurality of models on the time sequence series constructed in the fifth step according to the models, wherein the calculation result is expressed as follows:
and seventhly, rolling and updating the time sequence array of the fifth step according to time sequence, and performing regression operation once by using the method of the sixth step every time the time sequence array is updated, so as to obtain the time sequence array after regression.
And eighthly, describing the length or tension change trend of the driving chain by using the regressed time sequence series of the maximum sag value of the driving chain, and describing the change trend of the up-and-down swing of the driving chain in the running process by using the regressed time sequence series of the minimum sag value of the driving chain. The time-series trend of the above parameters, which contains most of the health information of the drive chain, can be deeply mined in the data center for equipment health analysis and predictive maintenance management related to the drive chain.
A system for implementing the method for monitoring the health state of the drive chain on line comprises
The magnitude acquisition unit is used for finishing the setting of a reference point for distance detection and the magnitude acquisition of the distance between the reference point and the vertical arc of the driving chain; the quantity value acquisition unit comprises a sensor and a sensor adjusting device, the sensor is a distance sensor, and the reaction time is not more than 1.5 milliseconds.
And the A/D conversion unit is used for receiving the analog quantity value sent by the quantity value acquisition unit and converting the analog quantity value into digital quantity for output.
And the data filtering unit is used for filtering the output data of the A/D conversion unit, screening the data of the distance from the real characterization reference point to the vertical arc of the driving chain and removing invalid data.
And the operation unit is used for receiving the data result screened by the data filtering unit and performing time sequence data regression operation.
And the trend management unit is used for describing the length or tension change trend of the driving chain by using the regressed time sequence series of the maximum sag value of the driving chain and describing the change trend of the up-and-down swing of the driving chain in the running process by using the regressed time sequence series of the minimum sag value of the driving chain.
The magnitude acquisition unit and the A/D conversion unit jointly implement the first step and the second step, the data filtering unit implements the third step, the fourth step and the fifth step, the arithmetic unit implements the sixth step and the seventh step, and the trend management unit implements the eighth step.
Claims (8)
1. A method for monitoring the health state of a drive chain on line is characterized in that: comprises the following steps of (a) carrying out,
firstly, selecting a driver and determining a monitoring reference point;
secondly, measuring the distance from the reference point to the arc surface of the roller of the driving chain;
thirdly, determining a distance value between a measured point of the driving chain and a measurement reference point;
fourthly, repeating the third step to obtain a discrete time sequence data set of the distance values in the third step, and extracting the minimum value and the maximum value in the discrete data set formed by a plurality of time sequence data in a set time period;
step five, repeating the step four, and constructing a minimum time sequence array [ li]And maximum timing sequence [ b ]i];
Sixth, a polynomial model is selected to express the change process of the sag of the driving chain along with time, namely S
si=ai,1*tn-1+ai,2*tn-2...+ai,n-1*t+ai,n+ei
Or:
in the formula IiIs the value at time t in the minimum time series, biIs the value at time t in the maximum time sequence series, n is the number of terms of the polynomial function, eiIs a random error;
and (3) performing regression operation of a plurality of models on the time sequence series constructed in the fifth step according to the models, wherein the calculation result is expressed as follows:
step seven, rolling and updating the time sequence array of the step five according to time sequence, and performing regression operation once by using the method of the step six every time the time sequence array is updated, so as to obtain a regressed time sequence array;
and eighthly, describing the length or tension change trend of the driving chain by using the regressed time sequence series of the maximum sag value of the driving chain, and describing the change trend of the up-and-down swing of the driving chain in the running process by using the regressed time sequence series of the minimum sag value of the driving chain.
2. The method for on-line monitoring the health status of the drive chain according to claim 1, wherein: in the first step, according to the structure and the running speed of the measured driving chain, selecting the sensor parameters of the measured distance, wherein the sensor parameters comprise the sampling frequency, the sampling mode, the distance measuring range and the progress of the sensor, and the sampling mode is one of laser, ultrasound, electromagnetism and inductance.
3. The method for on-line monitoring the health status of the drive chain according to claim 1, wherein: in the second step, the sampling frequency is set according to the technical parameters of the sensor and the chain structure, the vertical distance between the reference point and the roller arc surface at the lowest point of the non-stressed side vertical arc section of the driving chain is measured, and the sampling frequency of the pitch of each driving chain is required to be enough to ensure that the distance sampling frequency of the roller is not less than 5 times.
4. The method for on-line monitoring the health status of the drive chain according to claim 1, wherein: in the third step, the method for determining the distance value between the measured point of the driving chain and the measuring reference point selects one of the following three methods:
the method comprises the following steps: and describing sag by using the distance from the reference point to the arc vertex of the roller of the driving chain or the position of a chain plate corresponding to the shaft pin: setting data acquisition frequency and each continuous data acquisition time length according to the running speed of the driving chain, and extracting a minimum value from a set data set of each continuous acquisition time period for describing the distance from a reference point to the top point of the roller of the driving chain wheel;
the method 2 comprises the following steps: the sag is described by the circle center of a roller of a driving chain: setting the frequency of data acquisition (requiring that distance data is acquired for each pitch of the drive chain for not less than 5 times) and the duration of continuously acquiring data for each time (requiring that data acquired for each time is not less than 100) according to the running speed of the drive chain, extracting a point with the sum of two adjacent values being the minimum value from the set data set acquired for each time, and defining the maximum value of the two points as h1A decimal value of h2,a=h1-h2(ii) a Defining the speed of the chain as v, the sampling time interval of the two even points as t, and defining b as v t; defining the radius of the chain ball as r; the distance H between the center of the roller of the driving chain and the reference point is as follows:
subtracting the radius value of the chain from the H value obtained by the algorithm to obtain the distance between the top point of the roller of the driving chain and the reference point;
the method 3 comprises the following steps: sag is described by the distance between the reference point and the link plate of the drive chain: taking the side surface of a chain plate as a measurement target, determining the number n of times that each section of driving chain plate can be continuously sampled according to the length of the driving chain plate/the driving continuous speed and the sampling frequency (the mantissa after the decimal point is cut off and an integer is taken), and carrying out average value calculation on the data obtained by continuous sampling, wherein the calculation model is as follows:
in the formulaN is an average value of n successive data of the distance from the base point to the link plate side face, n is the number of distance time series data used for calculating the average value (i.e. the number of times each link plate of the driving chain can be continuously sampled), i is a time series number of the distance time series data used for calculating the average value, s is a time series number of the distance time series data used for calculating the average valueiAnd the distance data from the base point used for calculating the average value to the chain plate is shown.
A calculation cycle is set, and the minimum value is found in the data set of the distance moving average value in the calculation cycle, and the minimum value is used for describing the required distance value.
5. The method for on-line monitoring the health status of the drive chain as claimed in claim 4, wherein: in the method 1, the frequency requirement of data acquisition is that distance data is acquired for each pitch of a driving chain for not less than 5 times, and the duration of continuously acquiring data for each time is required to be not less than 100 data acquired for each time; in the method 2, the frequency requirement of data acquisition is that distance data is acquired for each pitch of the driving chain for not less than 5 times, and the duration of each continuous data acquisition is required to be not less than 100 data acquired each time.
6. The method for on-line monitoring the health status of the drive chain according to claim 1, wherein: in the fourth step, in a discrete data set formed by a plurality of time sequence data in a set time period, the specific number of the time sequence data is determined according to the target accuracy.
7. A system for on-line monitoring the health state of a drive chain is characterized in that: comprises that
The magnitude acquisition unit is used for finishing the setting of a reference point for distance detection and the magnitude acquisition of the distance between the reference point and the vertical arc of the driving chain;
the A/D conversion unit is used for receiving the analog quantity value sent by the quantity value acquisition unit and converting the analog quantity value into digital quantity for output;
the data filtering unit is used for filtering the output data of the A/D conversion unit, screening the data of the distance from the real characterization reference point to the vertical arc of the driving chain and removing invalid data;
the operation unit is used for receiving the data result screened by the data filtering unit and performing time sequence data regression operation;
and the trend management unit is used for describing the length or tension change trend of the driving chain by using the regressed time sequence series of the maximum sag value of the driving chain and describing the change trend of the up-and-down swing of the driving chain in the running process by using the regressed time sequence series of the minimum sag value of the driving chain.
8. The system for on-line monitoring the health status of the drive chain according to claim 7, wherein: the quantity value acquisition unit comprises a sensor and a sensor adjusting device, the sensor is a distance sensor, and the reaction time is not more than 1.5 milliseconds.
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CN110002329A (en) * | 2018-12-29 | 2019-07-12 | 广州地铁设计研究院股份有限公司 | A kind of escalator on-line monitoring early warning system and method based on cloud platform |
CN209685114U (en) * | 2019-03-21 | 2019-11-26 | 通力电梯有限公司 | Monitoring system and staircase including the monitoring system |
CN109813544A (en) * | 2019-03-26 | 2019-05-28 | 武汉众犇慧通科技有限公司 | A kind of rotating machinery Incipient Fault Diagnosis method and system based on on-line monitoring |
CN111071907A (en) * | 2019-12-12 | 2020-04-28 | 广东寰球智能科技有限公司 | Escalator main driving chain monitoring method and system based on light curtain sensor |
CN112429622A (en) * | 2020-12-09 | 2021-03-02 | 中铁第四勘察设计院集团有限公司 | Escalator monitoring network system based on block chain and IPFS |
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