CN114913302B - Rotary joint service life prediction system and method based on multi-sensor fusion - Google Patents
Rotary joint service life prediction system and method based on multi-sensor fusion Download PDFInfo
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Abstract
The invention discloses a rotary joint life prediction system and method based on multi-sensor fusion, and belongs to the field of industrial equipment life prediction. And (3) carrying out three-dimensional grid division on the rotary joint in space, setting data acquisition frequency for the multiple sensors, carrying out rotary joint index data acquisition according to the data acquisition frequency, establishing a database, then establishing a rotary joint life prediction model, predicting the service life of each sub-grid, and finally taking the minimum value of the service life of all the sub-grids as the whole service life of the rotary joint. The invention can not only know the integral condition of the rotary joint, but also observe the local details, and the obtained information is more comprehensive; the actual conditions of the rotary joint are reflected from multiple angles, the accuracy of prediction is improved by using multiple index data, the state of the rotary joint is accurately evaluated and predicted from multiple dimensions, the prediction result is accurate and reliable, and the probability of fault occurrence is reduced.
Description
Technical Field
The invention relates to the field of industrial equipment life prediction, in particular to a rotary joint life prediction device and method.
Background
The rotary joint is a closed rotary connector for 360-degree rotary conveying medium. The rotary joint is used for feeding the liquid from the side of the pipeline into the rotary or reciprocating equipment and discharging the liquid from the rotary or reciprocating equipment. The application field of the rotary joint almost covers various processing and manufacturing industries. Such as: metallurgy, machine tools, power generation, petroleum, rubber, plastics, textile, printing and dyeing, pharmacy, cigarette, papermaking, food, feed processing and the like.
With the rapid development of economy, the use amount of industrial equipment is increased, the problems brought by the use amount of the industrial equipment are also more obvious, the longer the industrial equipment is used, the mechanical structures inside the equipment are damaged to different degrees, and the service performance is reduced continuously. Damage to the interior of the swivel can lead to reduced tightness, in which the medium transported runs the risk of leakage, and even high temperature and high pressure media can cause the device to burst. Conventional swivel service is not satisfactory for an ever-increasing number of applications. At present, the fault detection of the rotary joint is mostly confirmed by methods of factory acceptance, first inspection, calibration and the like, but whether the actual running state of the rotary joint is healthy or not cannot be confirmed, and whether safety or equipment hidden danger exists in a calibration period or not greatly influences the stability and reliability of the rotary joint as conveying equipment. And whether the actual running state of the rotary joint is healthy or not and whether safety or equipment hidden danger in the detection period are shown on the service life of the rotary joint. Therefore, a reasonable scheme for predicting the service life of the rotary joint is provided to avoid faults in industrial production caused by the loss of the internal mechanical structure of the rotary joint in advance. In practical use and in most patents, little consideration is given to accurately predicting the useful life of a rotary joint, often after a failure, to take remedial action. Therefore, the invention considers the situation, improves the traditional method, can predict the service life of the rotary joint in time, better knows the situation of the rotary joint, and makes the enterprises make reasonable production plans.
Disclosure of Invention
The invention aims to provide a rotary joint life prediction system and method based on multi-sensor fusion, aiming at the defects in the prior art.
The technical scheme is as follows: the invention adopts the technical scheme that:
the rotary joint life prediction system based on multi-sensor fusion comprises a grid division module, a multi-sensor data acquisition module, a database module, a model building module, a life prediction module and a result analysis module, wherein the grid division module is used for realizing three-dimensional grid division of the rotary joint in space and marking coordinates of each sub-grid; the multi-sensor data acquisition module sets data acquisition frequency for the multi-sensor, acquires rotary joint index data according to the data acquisition frequency, and stores the rotary joint index data into the database module; the model building module invokes and trains all the rotary joint index data in the database module to obtain the relation between the service life and other indexes, so as to obtain a trained rotary joint life prediction model, and the trained rotary joint life prediction model is stored in the life prediction module; the service life prediction module stores a trained rotary joint service life prediction model, rotary joint index data to be predicted are input into the service life prediction module, the service life of each rotary joint sub-grid can be obtained, the service life of all the sub-grids is ordered by the result analysis module, and the service life of the rotary joint with the shortest service life in all the sub-grids is screened out to be used as the whole service life of the rotary joint.
Further, the sensor comprises a speed sensor for collecting the rotating speed S, a torque sensor for collecting the torque N, a timer for collecting the service life H, a vibration sensor for collecting the vibration signal V, a temperature sensor for collecting the medium temperature T and a pressure sensor for collecting the medium pressure P.
The invention also discloses a rotary joint service life prediction method based on multi-sensor fusion, which comprises the following steps:
s1: dividing the rotary joint into three-dimensional grids in space, and marking the coordinates of each divided sub-grid;
s2: acquiring index data of the rotary joint and storing the index data;
s3: training all acquired rotary joint index data to obtain the relation between the service life and other indexes, thereby obtaining a trained rotary joint life prediction model;
s4: inputting the rotary joint index data to be predicted into a life prediction model to obtain the service life of each rotary joint sub-grid, sequencing the service lives of all the sub-grids, and screening out the shortest service life of all the sub-grids as the whole service life of the rotary joint.
Further, in step S1, the dividing range of the three-dimensional grid includes at least a main body portion of the rotary joint, the three-dimensional cube grid is divided by taking a length of one tenth of a diameter of the main body portion of the rotary joint as a side length, and each sub-grid is marked according to row and column coordinates.
Further, in step S2, the collected index data includes a rotation speed S, a torque N, a service life H, a vibration signal V, a medium temperature T, and a medium pressure P, and the collection frequency of each index data is 0.1S.
Further, the collected vibration signal V is converted into a decibel value of the sound signal D by the following formula.
Wherein a is the overall average value of vibration acceleration;
is the amplification factor; d is the average value of sound decibels, and the unit is dB; a is acquired by a vibration sensor.
Further, the service life H in the rotary joint index data is taken as an output predicted value, the rest indexes are taken as predicted model input values, and the data is continuously recorded until the rotary joint is completely damaged for 10 hours.
Further, in step S3, a rotary joint lifetime prediction model expression is constructed, and the specific expression is:
H t，i ＝f{S t，i ，N t ，g i (d)·D t，i ，g i (d)·T t，i ，P t，i }
wherein t is a certain time; i is the number of sub-grids, and the sub-grids are marked according to the sequence of rows; h t，i The service life of the ith sub-grid t moment; n (N) t The torque value of all the sub-grids at the moment t is the same as the torque of the rotary joint to be tested at the moment t; f is a corresponding rule of input and output index data; s is S t，i The rotation speed at the moment t of the ith sub-grid; d (D) t，i Sound decibels at the time t of the ith sub-grid; t (T) t，i The temperature value at the moment t of the ith sub-grid; p (P) t，i For the pressure value at time t of the ith sub-grid；g i (d) G when i is the subgrid where the monitoring point of the sensor is located as the correlation coefficient function changing along with the distance i (d) 1, the rest sub-grids conform to the distribution of g (d), and g (d) is an exponential decay function; d is the distance between the two sub-grids.
Further, the calculation formula of the distance d between the two sub-grids is specifically as follows:
wherein p and q represent any two sub-grids in 3-dimensional space; p is p j And q j Representing the corresponding components of p and q in three-dimensional space, respectively.
Further, predicting the service life H of each sub-grid from the corresponding rule f and the input index data of each sub-grid t，i 。
The beneficial effects are that: compared with the prior art, the invention has the following advantages:
(1) Different from the mode that the conventional rotary joint utilizes the sensor to carry out integral monitoring, the rotary joint body part is divided into a plurality of sub-grids in space by the three-dimensional grid dividing method, the state of each sub-grid is estimated and predicted, the integral condition of the rotary joint can be known, local details can be observed, and the obtained information is more comprehensive.
(2) The acquired index data comprise indexes acquired by 5 different sensors, the actual conditions of the rotary joint can be reflected from a plurality of angles, and the accuracy of prediction is improved by using various index data.
(3) The multi-sensor-based prediction regression device is used for predicting the service life of each sub-grid, and the minimum service life is used as the whole service life of the rotary joint, so that the probability of fault occurrence is reduced.
Drawings
FIG. 1 is a block diagram of a rotary joint life prediction system based on multi-sensor fusion according to the present invention;
FIG. 2 is a schematic sensor diagram of a rotary joint life prediction system based on multi-sensor fusion according to the present invention;
FIG. 3 is a flow chart of a method for predicting the service life of a rotary joint based on multi-sensor fusion.
Detailed Description
The invention will be further illustrated by the following drawings and specific examples, which are carried out on the basis of the technical solutions of the invention, it being understood that these examples are only intended to illustrate the invention and are not intended to limit the scope of the invention.
As shown in fig. 1, the invention discloses a rotary joint life prediction system based on multi-sensor fusion, which comprises a grid division module 1, a multi-sensor data acquisition module 2, a database module 3, a model building module 4, a life prediction module 5 and a result analysis module 6.
The grid division module 1 performs three-dimensional grid division on the rotary joint in space, and marks coordinates of each sub-grid.
The multi-sensor data acquisition module 2 sets data acquisition frequency for the multi-sensor, acquires rotary joint index data according to the data acquisition frequency, and stores the rotary joint index data into the database module 3; as shown in fig. 2, the sensors include a speed sensor that collects a rotational speed S, a torque sensor that collects a torque N, a timer that collects a service life H, a vibration sensor that collects a vibration signal V, a temperature sensor that collects a medium temperature T, and a pressure sensor that collects a medium pressure P.
The model building module 4 invokes and trains all the rotary joint index data in the database module 3 to obtain the relation between the service life and other indexes, thereby obtaining a trained rotary joint life prediction model.
The life prediction module 5 stores a trained life prediction model of the rotary joint, and the life prediction module 5 inputs the index data of the rotary joint to be predicted, so that the service life of each rotary joint sub-grid can be obtained.
The result analysis module 6 sorts the service lives of all the sub-grids, and screens out the service life which is shortest in all the sub-grids and is used as the whole service life of the rotary joint.
The invention also discloses a rotary joint life prediction method based on multi-sensor fusion, which is realized based on the system, wherein the rotary joint is subjected to three-dimensional grid division in space, meanwhile, the multi-sensor is set with data acquisition frequency, the rotary joint index data acquisition is carried out according to the data acquisition frequency, a database is built, then a rotary joint life prediction model is built, the service life of each sub-grid is predicted, and finally, the service life of all the sub-grids is minimized and is used as the whole service life of the rotary joint. As shown in fig. 3, the method specifically comprises the following steps:
step S1: the grid dividing module 1 is used for dividing the rotary joint into three-dimensional grids in space, and the coordinates of each sub-grid are marked.
The dividing range at least comprises a main body part of the rotary joint, so that most of the space of the rotary joint can be predicted, three-dimensional cube grid division is performed by taking the length of one tenth of the diameter of the main body part of the rotary joint as the side length, and each sub grid is marked according to row and column coordinates. The smaller the side length of the scoring sub-grid, the better the final predicted effect, and the more clearly the condition of each part of the swivel is understood.
Step S2: the multi-sensor data acquisition module 2 is adopted to set data acquisition frequency for the multi-sensor, the rotary joint index data acquisition is carried out according to the data acquisition frequency, and the rotary joint index data are stored in the database module 3.
In the multi-sensor data acquisition module 2, the acquired index and the acquisition frequency are set as follows: every 0.1S of rotation speed S, every 0.1S of torque N, every 1S of service life H, every 0.1S of vibration signal V, every 0.1S of medium temperature T and every 0.1S of medium pressure P. The collected vibration signal V is converted into a decibel value of the sound signal D through the following formula;
wherein a is the overall average value of vibration acceleration;
is the amplification factor; d is the average value of sound decibels, and the unit is dB; a is acquired by a vibration sensor.
In the database module 3, the service life H in the index data of the rotary joint is used as an output predicted value, the rest indexes are used as predicted model input values, the data are continuously recorded until 10 hours after the rotary joint is completely damaged, and the recording of enough index data with good performance to complete damage is beneficial to the fitting of a follow-up regression predictor, so that the follow-up prediction structure is more accurate.
Step S3: the model building module 4 is adopted to call and train index data of all the rotary joints in the database module 3, so as to obtain the relation between the service life and other indexes, thereby obtaining a trained rotary joint life prediction model, and storing the model in the life prediction module 5;
constructing a rotary joint life prediction expression, wherein the specific expression is as follows:
H t，i ＝f{S t，i ，N t ，g i (d)·D t，i ，g i (d)·T t，i ，P t，i }
wherein t is a certain time; i is the number of sub-grids, and the sub-grids are marked according to the sequence of rows; h t，i The service life of the ith sub-grid t moment; n (N) t The torque value of all the sub-grids at the moment t is the same as the torque of the rotary joint to be tested at the moment t; f is a corresponding rule of input and output index data; s is S t，i The rotation speed at the moment t of the ith sub-grid; d (D) t，i Sound decibels at the time t of the ith sub-grid; t (T) t，i The temperature value at the moment t of the ith sub-grid; p (P) t，i The pressure value at the moment t of the ith sub-grid; g i (d) Is thatG when i is the subgrid where the monitoring point of the sensor is located, the correlation coefficient function changing along with the distance i (d) 1, the rest sub-grids conform to the distribution of g (d), and g (d) is an exponential decay function; d is the distance between the two sub-grids.
The distance d between the two sub-grids is obtained by adopting a Euclidean distance formula, and the calculation formula is specifically as follows:
wherein p and q represent any two sub-grids in 3-dimensional space; p is p j And q j Representing the corresponding components of p and q in three-dimensional space, respectively.
Predicting the service life H of each sub-grid by the corresponding rule f and the input index data of each sub-grid t，i . The corresponding rule f of the input and output index data is generated by the following multi-Bayesian regression predictor, which can predict the required index data according to the input index data, wherein the prediction result is an actual physical quantity, and the specific expression of the multi-Bayesian regression predictor is as follows:
wherein x is 1 ，...，x 5 Inputting index data for the rotary joint; y is the rotation joint to output the index data to be predicted;
outputting prediction index data for the rotary joint; p (y|x) 1 ，...，x 5 ) Is at x 1 ，...，x 5 Probability of y occurring in case of both occurrence; p (y) is the probability that y occurs.
Step S4: the service life of each rotary joint sub-grid can be obtained by inputting rotary joint index data to be predicted into the service life prediction module 5, and the result analysis module 6 sorts the service lives of all the sub-grids and screens out the service life which is shortest in all the sub-grids and is used as the whole service life of the rotary joint, wherein the specific expression is as follows:
wherein H is t，i The predicted service life of the ith sub-grid t moment; h t min The minimum service life of the device is t.
The method is different from the mode of integrally monitoring the rotary joint by using a sensor in the prior art, wherein the method divides the rotary joint main body part into a plurality of sub-grids in space, and evaluates and predicts the state of each sub-grid. The acquired index data comprise indexes acquired by 5 different sensors, and the actual conditions of the rotary joint can be reflected from a plurality of angles. The multi-sensor is used for collecting index data of the rotary joint, establishing a life prediction model and giving out a multi-Bayesian estimation-based prediction regression for predicting the service life of each sub-grid by considering the change condition of indexes of different sub-grids along with the distance. The invention can predict the service life of the rotary joint in time, and better understand the condition of the rotary joint, so that enterprises can make reasonable production plans.
The above detailed description is only a preferred embodiment of the present invention and is not intended to limit the scope of the claims, but all equivalent changes and modifications that can be made according to the protection scope of the claims are included in the scope of the claims.
Claims (9)
1. A rotary joint life prediction system based on multi-sensor fusion is characterized in that: comprises a grid dividing module (1), a multi-sensor data acquisition module (2), a database module (3), a model building module (4), a life prediction module (5) and a result analysis module (6),
the grid dividing module (1) is used for realizing three-dimensional grid division of the rotary joint in space and marking coordinates of each sub-grid;
the multi-sensor data acquisition module (2) sets data acquisition frequency for the multi-sensor, acquires rotary joint index data according to the data acquisition frequency, and stores the rotary joint index data into the database module (3);
the model building module (4) is used for retrieving and training index data of all the rotary joints in the database module (3) to obtain the relation between the service life and other indexes, so as to obtain a trained rotary joint life prediction model; the specific expression of the rotary joint life prediction model is as follows:
H t,i ＝f{S t,i ,N t ,g i (d)·D t,i ,g i (d)·T t,i ,P t,i }
wherein t is a certain time; i is the number of sub-grids, and the sub-grids are marked according to the sequence of rows; h t,i The service life of the ith sub-grid t moment; n (N) t The torque value of all the sub-grids at the moment t is the same as the torque of the rotary joint to be tested at the moment t; f is a corresponding rule of input and output index data; s is S t,i The rotation speed at the moment t of the ith sub-grid; d (D) t,i Sound decibels at the time t of the ith sub-grid; t (T) t,i The temperature value at the moment t of the ith sub-grid; p (P) t,i The pressure value at the moment t of the ith sub-grid; g i (d) G when i is the subgrid where the monitoring point of the sensor is located as the correlation coefficient function changing along with the distance i (d) 1, the rest sub-grids conform to the distribution of g (d), and g (d) is an exponential decay function; d is the distance between two sub-grids;
the life prediction module (5) stores a trained life prediction model of the rotary joint, the rotary joint index data to be predicted is input into the life prediction module (5) to obtain the service life of each rotary joint sub-grid,
and the result analysis module (6) sorts the service lives of all the sub-grids, and screens out the service life which is shortest in all the sub-grids and is used as the whole service life of the rotary joint.
2. A rotary joint life prediction system based on multi-sensor fusion according to claim 1, wherein: the sensor comprises a speed sensor for collecting the rotating speed S, a torque sensor for collecting the torque N, a timer for collecting the service life H, a vibration sensor for collecting the vibration signal V, a temperature sensor for collecting the medium temperature T and a pressure sensor for collecting the medium pressure P.
3. The rotary joint service life prediction method based on multi-sensor fusion is characterized by comprising the following steps of:
s1: dividing the rotary joint into three-dimensional grids in space, and marking the coordinates of each divided sub-grid;
s2: acquiring index data of the rotary joint and storing the index data;
s3: training all acquired rotary joint index data to obtain the relation between the service life and other indexes, thereby obtaining a trained rotary joint life prediction model; constructing a rotary joint life prediction model expression, wherein the specific expression is as follows:
H t,i ＝f{S t,i ,N t ,g i (d)·D t,i ,g i (d)·T t,i ,P t,i }
wherein t is a certain time; i is the number of sub-grids, and the sub-grids are marked according to the sequence of rows; h t,i The service life of the ith sub-grid t moment; n (N) t The torque value of all the sub-grids at the moment t is the same as the torque of the rotary joint to be tested at the moment t; f is a corresponding rule of input and output index data; s is S t,i The rotation speed at the moment t of the ith sub-grid; d (D) t,i Sound decibels at the time t of the ith sub-grid; t (T) t,i The temperature value at the moment t of the ith sub-grid; p (P) t,i The pressure value at the moment t of the ith sub-grid; g i (d) G when i is the subgrid where the monitoring point of the sensor is located as the correlation coefficient function changing along with the distance i (d) 1, the rest sub-grids conform to the distribution of g (d), and g (d) is an exponential decay function; d is the distance between two sub-grids;
s4: inputting the rotary joint index data to be predicted into a life prediction model to obtain the service life of each rotary joint sub-grid, sequencing the service lives of all the sub-grids, and screening out the shortest service life of all the sub-grids as the whole service life of the rotary joint.
4. A method for predicting the lifetime of a rotary joint based on multi-sensor fusion according to claim 3, wherein: in step S1, the dividing range of the three-dimensional grid at least includes a main body portion of the rotary joint, the three-dimensional cube grid is divided by taking the length of one tenth of the diameter of the main body portion of the rotary joint as the side length, and each sub-grid is marked according to row and column coordinates.
5. A method for predicting the lifetime of a rotary joint based on multi-sensor fusion according to claim 3, wherein: in step S2, the collected index data includes a rotation speed S, a torque N, a service life H, a vibration signal V, a medium temperature T and a medium pressure P, and the collection frequency of each index data is 0.1S.
6. The rotary joint life prediction method based on multi-sensor fusion according to claim 5, wherein: the collected vibration signal V is converted into a decibel value of the sound signal D by the following formula
Wherein a is the overall average value of vibration acceleration;
is the amplification factor; d is the average value of sound decibels, and the unit is dB; a is acquired by a vibration sensor.
7. A method for predicting the lifetime of a rotary joint based on multi-sensor fusion according to claim 3, wherein: and taking the service life H in the rotary joint index data as an output predicted value, taking the rest indexes as predicted model input values, and continuously recording the data until the rotary joint is completely damaged for 10 hours.
8. A method for predicting the lifetime of a rotary joint based on multi-sensor fusion according to claim 3, wherein: the calculation formula of the distance d between the two sub-grids is specifically as follows:
wherein p and q represent any two sub-grids in 3-dimensional space; p is p j And q j Representing the corresponding components of p and q in three-dimensional space, respectively.
9. A method for predicting the lifetime of a rotary joint based on multi-sensor fusion according to claim 3, wherein: predicting the service life H of each sub-grid by the corresponding rule f and the input index data of each sub-grid t,i 。
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