CN115456222A - Remote intelligent predictive maintenance operation and maintenance service method - Google Patents
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Abstract
The invention discloses a remote intelligent predictive maintenance, operation and maintenance service method, which relates to the technical field of intelligent manufacturing and solves the technical problems that when predictive maintenance is carried out in the prior art, a large amount of data needs to be collected in stages, the remote maintenance effect cannot be ensured, and the remote predictive maintenance efficiency is low; the method comprises the steps of establishing connection between a remote operation and maintenance platform and a plurality of intelligent manufacturing devices, periodically obtaining operation data of the intelligent manufacturing devices, analyzing the intelligent manufacturing devices once according to the operation data, determining whether the change trend of the device data needs to be analyzed, and analyzing the change trend by combining all data of the intelligent manufacturing devices. All data analysis processes are on a remote operation and maintenance platform, operation and maintenance judgment can be completed by regularly acquiring operation data, the accuracy of maintenance strategies can be ensured by two times of analysis, and the service life of intelligent manufacturing equipment is prolonged.
Description
Technical Field
The invention belongs to the field of intelligent manufacturing, relates to a remote intelligent predictive maintenance operation and maintenance service technology, and particularly relates to a remote intelligent predictive maintenance operation and maintenance service method.
Background
With the transformation and upgrading of automation, digitization and intelligence of the manufacturing industry, intelligent manufacturing equipment represented by industrial robots plays an increasingly important role in the manufacturing industry. However, intelligent manufacturing equipment is prone to failure and damage during long, repetitive operations, and therefore, remote predictive operation and maintenance services are critical.
The prior art (invention patent with application number 2021112182509) discloses a predictive maintenance system and method for intelligent manufacturing equipment, which obtain the primary residual life and the secondary residual life of intelligent manufacturing equipment through an algorithm, and then determine a predictive maintenance strategy for the intelligent manufacturing equipment according to the primary residual life and the secondary residual life, so that active operation and maintenance guarantee can be provided, and operation and maintenance efficiency can be improved. In the prior art, when predictive maintenance is carried out, control parameters and point inspection parameters of intelligent manufacturing equipment are combined to judge whether a maintenance strategy is needed or not, a large amount of data needs to be collected in stages, the remote maintenance effect cannot be ensured, and the remote predictive maintenance efficiency is low; therefore, a remote intelligent predictive maintenance operation and maintenance service method is needed.
Disclosure of Invention
The present invention is directed to solving at least one of the problems of the prior art; therefore, the invention provides a remote intelligent predictive maintenance operation and maintenance service method, which is used for solving the technical problem that the efficiency of remote predictive maintenance is low because a large amount of data needs to be collected in stages when the predictive maintenance is carried out in the prior art, and the remote maintenance effect cannot be ensured.
To achieve the above object, a first aspect of the present invention provides a remote intelligent predictive maintenance operation and maintenance service method, including:
establishing connection between the remote operation and maintenance platform and each intelligent manufacturing device, periodically acquiring operation data of the intelligent manufacturing device and uploading the operation data to the remote operation and maintenance platform; wherein the operational data includes environmental data and device data;
the remote operation and maintenance platform builds model analysis data according to the equipment information and the operation data, and acquires equipment scores by combining an equipment score model; wherein, the operation scoring model is established based on an artificial intelligence model;
when the equipment score is larger than the score threshold value, maintaining through a conventional maintenance strategy; otherwise, analyzing the variation trend of the equipment data, and selecting an abnormal maintenance strategy according to the analysis result.
Preferably, the remote operation and maintenance platform establishes communication and/or electrical connection with a plurality of intelligent manufacturing devices; a plurality of data sensors are arranged inside or outside the intelligent manufacturing equipment;
the remote operation and maintenance platform simulates the work of intelligent manufacturing equipment, sets a conventional maintenance strategy and an abnormal maintenance strategy according to a simulation result, and stores the conventional maintenance strategy and the abnormal maintenance strategy in the remote operation and maintenance platform.
Preferably, the remote operation and maintenance platform sets a data acquisition cycle according to the device information, and the data acquisition cycle includes:
acquiring equipment information; the equipment information comprises an equipment name, an equipment level and an equipment age;
respectively marking the equipment level and the equipment age as SJ and SN; the equipment level is set by a worker according to the importance of the intelligent manufacturing equipment, and the higher the equipment level is, the higher the importance is;
acquiring a data acquisition cycle SCZ through a formula SCZ = alpha/(SJ multiplied by SN); wherein alpha is a set constant larger than 0, and the values of alpha comprise 10, 20 or 30.
Preferably, the remote operation and maintenance platform builds model analysis data according to the device information and the operation data, and the method includes:
extracting a data measured value in the equipment data, and acquiring a reference value of a corresponding data element from a remote operation and maintenance platform; wherein i is a positive integer, and the data elements include vibration, noise, voltage and current;
and splicing and integrating the environmental data, the equipment age, the data element actual measurement value and the data element reference value to generate model analysis data.
Preferably, the device scoring model is established based on an artificial intelligence model, and the artificial intelligence model comprises:
simulating the operating environment of intelligent manufacturing equipment in a laboratory under a standard working condition to obtain standard working condition data; simultaneously simulating the operating environment under the nonstandard working condition to obtain nonstandard working condition data;
the method comprises the following steps that a worker compares nonstandard working condition data with standard working condition data, and meanwhile, equipment scores corresponding to the nonstandard working condition data are obtained in an expert scoring mode by combining simulated equipment ages;
integrating the simulated operating environment, the simulated equipment age, the nonstandard working condition data and the standard working condition data to be used as model input data, and using the corresponding equipment score as model output data;
training an artificial intelligence model through model input data and model output data to obtain an equipment scoring model; the artificial intelligence model comprises an error back propagation neural network model or an RBF neural network model.
Preferably, the remote operation and maintenance platform compares the device score with a score threshold, including:
after the device score is obtained, extracting a corresponding score threshold value;
when the equipment score is larger than a score threshold value, judging that the intelligent manufacturing equipment normally operates, and calling a conventional maintenance strategy for maintenance; otherwise, judging that the intelligent manufacturing equipment is abnormal in operation, and analyzing the variation trend of the equipment data.
Preferably, the remote operation and maintenance platform establishes a variation curve of the vibration data, and determines a variation trend of the variation curve, including:
extracting vibration data corresponding to all working time of the intelligent manufacturing equipment according to the equipment name, and establishing a vibration change curve by taking the time as an independent variable and the vibration data as a dependent variable;
and analyzing the change trend of the vibration change curve, and selecting an abnormal maintenance strategy according to the change trend.
Preferably, the remote operation and maintenance platform analyzes the variation trend of the vibration variation curve, and includes:
judging whether the vibration change curve is increased or decreased; if yes, acquiring a first derivative value; if not, selecting an abnormal maintenance strategy according to the average value of the vibration data;
extracting the maximum value in the first derivative value, comparing the maximum value with a derivative threshold value, and selecting an abnormal maintenance strategy according to the comparison result; wherein the first derivative maximum is associated with the mean match of the seismic data.
Compared with the prior art, the invention has the beneficial effects that:
the method comprises the steps of establishing connection between a remote operation and maintenance platform and a plurality of intelligent manufacturing devices, obtaining operation data of the intelligent manufacturing devices periodically, analyzing the intelligent manufacturing devices once according to the operation data, determining whether the change trend of the device data needs to be analyzed, and analyzing the change trend by combining all data of the intelligent manufacturing devices. All data analysis processes are on a remote operation and maintenance platform, operation and maintenance judgment can be completed by periodically acquiring operation data, the accuracy of a maintenance strategy can be ensured by two times of analysis, and the service life of intelligent manufacturing equipment is prolonged.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the prior art descriptions will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a schematic diagram of the working steps of the present invention.
Detailed Description
The technical solutions of the present invention will be described below clearly and completely in conjunction with the embodiments, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, a first embodiment of the present invention provides a remote intelligent predictive maintenance operation and maintenance service method, including: establishing connection between the remote operation and maintenance platform and each intelligent manufacturing device, periodically acquiring operation data of the intelligent manufacturing device and uploading the operation data to the remote operation and maintenance platform; the remote operation and maintenance platform builds model analysis data according to the equipment information and the operation data, and obtains equipment scores by combining an equipment score model; when the equipment score is larger than a score threshold value, maintaining through a conventional maintenance strategy; otherwise, analyzing the variation trend of the equipment data, and selecting an abnormal maintenance strategy according to the analysis result.
In the prior art, when predictive maintenance is performed, a first-level residual life is acquired according to control parameters of intelligent manufacturing equipment, a second-level residual life is acquired according to point inspection parameters, and then the first-level residual life and the second-level residual life are combined to determine a maintenance strategy. The prior art needs to continuously acquire control parameters and point inspection parameters, and the acquisition and transmission of the data are not suitable for performing predictive maintenance on a large number of intelligent manufacturing equipment, which affects the efficiency of remote predictive maintenance.
The method comprises the steps of establishing connection between a remote operation and maintenance platform and a plurality of intelligent manufacturing devices, periodically obtaining operation data of the intelligent manufacturing devices, analyzing the intelligent manufacturing devices once according to the operation data, determining whether the change trend of the device data needs to be analyzed, and analyzing the change trend by combining all data of the intelligent manufacturing devices. All data analysis processes are on a remote operation and maintenance platform, operation and maintenance judgment can be completed by periodically acquiring operation data, the accuracy of a maintenance strategy can be ensured by two times of analysis, and the service life of intelligent manufacturing equipment is prolonged.
The remote operation and maintenance platform is communicated and/or electrically connected with a plurality of intelligent manufacturing devices; a plurality of data sensors are arranged inside or outside the intelligent manufacturing equipment; the remote operation and maintenance platform simulates the work of the intelligent manufacturing equipment, sets a conventional maintenance strategy and an abnormal maintenance strategy according to the simulation result, and stores the conventional maintenance strategy and the abnormal maintenance strategy in the remote operation and maintenance platform. The intelligent manufacturing equipment is automatic production equipment, such as industrial robot.
The remote operation and maintenance platform is essentially a remote server and is mainly responsible for data processing; the remote operation and maintenance platform can be applied to a large intelligent manufacturing equipment enterprise, the enterprise can remotely maintain the intelligent manufacturing equipment produced under the flag, and certainly, the enterprise can also be a third-party operation and maintenance enterprise. Operation data of intelligent manufacturing equipment is needed in the operation and maintenance process, so that a data sensor is needed to collect the operation data; the data sensor mainly comprises a temperature sensor, a humidity sensor, a vibration sensor, a noise sensor, a voltage sensor and a current sensor.
Meanwhile, the remote operation and maintenance platform needs to perform simulation experiments to obtain the running states of various types of intelligent manufacturing equipment under various working conditions, sets various maintenance strategies according to the running states, stores the maintenance strategies in the remote operation and maintenance platform, and certainly can also send the maintenance strategies to the intelligent manufacturing equipment for storage, and the operation and maintenance platform can be directly called when needed.
In a preferred embodiment, the remote operation and maintenance platform sets a data acquisition cycle according to the device information, and the method includes: acquiring equipment information; respectively marking the equipment level and the equipment age as SJ and SN; the data collection period SCZ is obtained by the formula SCZ = α/(SJ × SN).
The equipment level is set by a worker according to the importance of the intelligent manufacturing equipment, and the higher the equipment level is, the higher the importance is. It can be understood that the higher the importance of intelligence manufacture equipment, the longer the equipment year then the corresponding data acquisition cycle shorter, through the data acquisition cycle set up can rationally arrange remote operation and maintenance platform's data processing volume, avoid a large amount of data to pour into remote operation and maintenance platform simultaneously as far as possible.
The technical solution in this embodiment is illustrated as follows:
assuming that there are two intelligent manufacturing devices, the corresponding device classes are 2 and 1, respectively, the corresponding device ages are 1, and α =30 is selected, the respective data acquisition cycles are 10 and 15, respectively.
In a preferred embodiment, the remote operation and maintenance platform builds model analysis data according to the equipment information and the operation data, and the method comprises the following steps: extracting a data measured value in the equipment data, and acquiring a reference value of a corresponding data element from a remote operation and maintenance platform; and splicing and integrating the environmental data, the equipment age limit, the data element actual measurement value and the data element reference value to generate model analysis data.
When one-time analysis is carried out, whether the operation data of the intelligent manufacturing equipment is normal or not is mainly considered by combining the environmental data, the equipment age and the actual measurement value and the reference value of the data element to be integrated to be used as model analysis data, and then the corresponding model scoring model is called to obtain the equipment score.
In this embodiment, i is a positive integer, and the data elements include vibration, noise, voltage, and current. Illustrating how model input data is generated: assuming that the temperature is 20, the humidity is 0.2, the equipment age is 1, the measured values and reference values of vibration, noise, voltage and current are (15, 10), (70, 50), (210, 220) and (5, 10) in the environmental data, the generated model input data is (1, 20,0.2, 15, 10, 70, 50, 210, 220,5, 10).
In an alternative embodiment, the device scoring model is built based on an artificial intelligence model, including: simulating the operating environment of intelligent manufacturing equipment in a laboratory under a standard working condition to obtain standard working condition data; simultaneously simulating the operating environment under the nonstandard working condition to obtain nonstandard working condition data; the method comprises the following steps that a worker compares nonstandard working condition data with standard working condition data, and meanwhile, equipment scores corresponding to the nonstandard working condition data are obtained in an expert scoring mode by combining simulated equipment ages; integrating the simulated operation environment, the simulated equipment age, the nonstandard working condition data and the standard working condition data to be used as model input data, and using the corresponding equipment score as model output data; training an artificial intelligence model through model input data and model output data to obtain an equipment scoring model; the artificial intelligence model comprises an error back propagation neural network model or an RBF neural network model.
The method includes the steps that an artificial intelligence model with strong nonlinear fitting capacity is used for training and obtaining a device scoring model, running environments of intelligent manufacturing devices under different working conditions need to be simulated before training, running data under various working conditions are obtained, then the running data are scored through experienced workers, and corresponding device scores are obtained. The equipment score here indicates the degree of deviation of the operation data from the standard operating condition, and a higher equipment score indicates a lower degree of deviation, and a lower equipment score indicates a higher degree of deviation.
The remote operation and maintenance platform then compares the device score to a score threshold, including: after the device score is obtained, extracting a corresponding score threshold value; when the equipment score is larger than the score threshold value, judging that the intelligent manufacturing equipment normally operates, and calling a conventional maintenance strategy for maintenance; otherwise, judging that the intelligent manufacturing equipment is abnormal in operation, and analyzing the variation trend of the equipment data.
After the equipment score corresponding to the model analysis data is obtained according to the equipment score model, comparing the equipment score with a score threshold, and when the equipment score is greater than the score threshold, indicating that the running state of the intelligent manufacturing equipment does not deviate much from the normal state; and when the equipment score is less than or equal to the score threshold value, the running state of the intelligent manufacturing equipment deviates from the normal state more, and the variation trend of the data elements needs to be analyzed.
In the judgment of the variation trend of the data elements, the voltage and the current can be judged according to the deviation value, and the vibration and the noise can be judged according to the curve variation trend.
In a preferred embodiment, the remote operation and maintenance platform establishes a variation curve of the vibration data and determines a variation trend of the variation curve, including: extracting vibration data corresponding to all working time of the intelligent manufacturing equipment according to the equipment name, and establishing a vibration change curve by taking the time as an independent variable and the vibration data as a dependent variable; and analyzing the change trend of the vibration change curve, and selecting an abnormal maintenance strategy according to the change trend.
All vibration data corresponding to the intelligent manufacturing equipment during the basic data of the vibration change curve, namely the operation data of the previous data acquisition period are also taken into account. Judging whether the vibration change curve is increased or decreased; if yes, acquiring a first derivative value; if not, selecting an abnormal maintenance strategy according to the average value of the vibration data; and extracting the maximum value in the first derivative value, comparing the maximum value with a derivative threshold value, and selecting an abnormal maintenance strategy according to the comparison result. It should be noted that the maximum of the first derivative is associated with the mean matching of the vibration data, that is, the two different determination methods may be matched to the same abnormal maintenance strategy.
The conventional maintenance strategy in the invention refers to normal maintenance of the intelligent manufacturing equipment, such as circuit maintenance, lubricant addition and other conventional maintenance; and the abnormal maintenance strategy is set according to different abnormalities, and if the vibration data is abnormal, whether the connection of each part of the intelligent manufacturing equipment is abnormal is checked.
Part of data in the formula is obtained by removing dimension and taking the value to calculate, and the formula is obtained by simulating a large amount of collected data through software and is closest to a real situation; the preset parameters and the preset threshold values in the formula are set by those skilled in the art according to actual conditions or obtained through simulation of a large amount of data.
The working principle of the invention is as follows:
and establishing connection between the remote operation and maintenance platform and each intelligent manufacturing device, and periodically acquiring operation data of the intelligent manufacturing device and uploading the operation data to the remote operation and maintenance platform.
And the remote operation and maintenance platform builds model analysis data according to the equipment information and the operation data and acquires equipment scores by combining an equipment score model.
When the equipment score is larger than the score threshold value, maintaining through a conventional maintenance strategy; otherwise, analyzing the variation trend of the equipment data, and selecting an abnormal maintenance strategy according to the analysis result.
Although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from the scope of the present invention.
Claims (8)
1. A remote intelligent predictive maintenance operation and maintenance service method is characterized by comprising the following steps:
establishing connection between the remote operation and maintenance platform and each intelligent manufacturing device, periodically acquiring operation data of the intelligent manufacturing device and uploading the operation data to the remote operation and maintenance platform; wherein the operational data includes environmental data and device data;
the remote operation and maintenance platform builds model analysis data according to the equipment information and the operation data, and acquires equipment scores by combining an equipment score model; wherein, the operation scoring model is established based on an artificial intelligence model;
when the equipment score is larger than the score threshold value, maintaining through a conventional maintenance strategy; otherwise, analyzing the variation trend of the equipment data, and selecting an abnormal maintenance strategy according to the analysis result.
2. The remote intelligent predictive maintenance operation and maintenance service method according to claim 1, wherein the remote operation and maintenance platform establishes communication and/or electrical connection with a plurality of intelligent manufacturing devices; a plurality of data sensors are arranged inside or outside the intelligent manufacturing equipment;
the remote operation and maintenance platform simulates the work of intelligent manufacturing equipment, sets a conventional maintenance strategy and an abnormal maintenance strategy according to a simulation result, and stores the conventional maintenance strategy and the abnormal maintenance strategy in the remote operation and maintenance platform.
3. The remote intelligent predictive maintenance operation and maintenance service method according to claim 1, wherein the remote operation and maintenance platform sets a data collection period according to the device information, and the method comprises the following steps:
acquiring equipment information; the equipment information comprises an equipment name, an equipment level and an equipment age;
respectively marking the equipment level and the equipment age as SJ and SN; the equipment level is set by a worker according to the importance of the intelligent manufacturing equipment, and the higher the equipment level is, the higher the importance is;
acquiring a data acquisition cycle SCZ through a formula SCZ = alpha/(SJ multiplied by SN); wherein alpha is a set constant greater than 0, and the values include 10, 20 or 30.
4. The remote intelligent predictive maintenance operation and maintenance service method according to claim 2 or 3, wherein the remote operation and maintenance platform builds model analysis data according to the equipment information and the operation data, and comprises the following steps:
extracting data measured values in the equipment data, and acquiring reference values of corresponding data elements from a remote operation and maintenance platform; wherein i is a positive integer, and the data elements include vibration, noise, voltage and current;
and splicing and integrating the environmental data, the equipment age limit, the data element actual measurement value and the data element reference value to generate model analysis data.
5. The remote intelligent predictive maintenance operation and maintenance service method according to claim 4, wherein the equipment scoring model is built based on an artificial intelligence model, comprising:
simulating the operating environment of intelligent manufacturing equipment in a laboratory under a standard working condition to obtain standard working condition data; simultaneously simulating the operating environment under the nonstandard working condition to obtain nonstandard working condition data;
the method comprises the following steps that a worker compares nonstandard working condition data with standard working condition data, and meanwhile, equipment scores corresponding to the nonstandard working condition data are obtained in an expert scoring mode by combining simulated equipment ages;
integrating the simulated operation environment, the simulated equipment age, the nonstandard working condition data and the standard working condition data to be used as model input data, and using the corresponding equipment score as model output data;
training an artificial intelligence model through model input data and model output data to obtain an equipment scoring model; the artificial intelligence model comprises an error back propagation neural network model or an RBF neural network model.
6. The remote intelligent predictive maintenance operation and maintenance service method of claim 5, wherein the remote operation and maintenance platform compares the equipment score to a score threshold, comprising:
after the device score is obtained, extracting a corresponding score threshold value;
when the equipment score is larger than a score threshold value, judging that the intelligent manufacturing equipment normally operates, and calling a conventional maintenance strategy for maintenance; otherwise, judging that the intelligent manufacturing equipment is abnormal in operation, and analyzing the variation trend of the equipment data.
7. The remote intelligent predictive maintenance operation and maintenance service method according to claim 6, wherein the remote operation and maintenance platform establishes a variation curve of the vibration data and determines a variation trend of the variation curve, comprising:
extracting vibration data corresponding to all working time of the intelligent manufacturing equipment according to the equipment name, and establishing a vibration change curve by taking the time as an independent variable and the vibration data as a dependent variable;
and analyzing the change trend of the vibration change curve, and selecting an abnormal maintenance strategy according to the change trend.
8. The remote intelligent predictive maintenance operation and maintenance service method according to claim 7, wherein the remote operation and maintenance platform analyzes the variation trend of the vibration variation curve, and comprises:
judging whether the vibration change curve is increased or decreased; if yes, acquiring a first derivative value; if not, selecting an abnormal maintenance strategy according to the average value of the vibration data;
extracting the maximum value in the first derivative value, comparing the maximum value with a derivative threshold value, and selecting an abnormal maintenance strategy according to the comparison result; wherein the first derivative maximum is associated with a mean match of the vibration data.
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