CN116088444A - Health detection method and device for automation equipment - Google Patents

Health detection method and device for automation equipment Download PDF

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Publication number
CN116088444A
CN116088444A CN202211714846.2A CN202211714846A CN116088444A CN 116088444 A CN116088444 A CN 116088444A CN 202211714846 A CN202211714846 A CN 202211714846A CN 116088444 A CN116088444 A CN 116088444A
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health
equipment
actual
preset
score
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CN202211714846.2A
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杨威
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Luster LightTech Co Ltd
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Luster LightTech Co Ltd
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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM]
    • G05B19/4184—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM] characterised by fault tolerance, reliability of production system
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/30—Nc systems
    • G05B2219/31—From computer integrated manufacturing till monitoring
    • G05B2219/31088—Network communication between supervisor and cell, machine group

Abstract

The application relates to the technical field of automation control, in particular to a health detection method and device of automation equipment, wherein the method comprises the following steps: acquiring maintenance information of the target automation equipment, service life of a preset key device, key parameters of a preset core device, actual machining deviation and/or machining quality, calculating health scores of the key device, the key parameters of the preset core device, the actual machining deviation and/or the machining quality based on the maintenance information of the corresponding weight computing equipment, the service life of the preset key device, and generating actual health degree of the target automation equipment. According to the embodiment of the application, based on quantitative processing of equipment health, equipment is diagnosed systematically and the current health degree of the equipment is fed back, and visualization of equipment health conditions is achieved, so that customers know the implicit working conditions of the equipment, equipment detection is enabled to be more comprehensive and systematic, the fault probability of sudden equipment is reduced, and the safety, reliability and practicability of the equipment are further guaranteed.

Description

Health detection method and device for automation equipment
Technical Field
The present disclosure relates to the field of automation control technologies, and in particular, to a method and an apparatus for detecting health of an automation device.
Background
With the development of technology, automation equipment is widely applied to various aspects of society, plays an increasingly important role, and the health status of the automation equipment is also more concerned. Factory automation equipment often evaluates the manufacturing efficiency of equipment or production lines with OEE (Overall Equipment Effectiveness, overall equipment efficiency) indicators.
In the related art, part of equipment operation problems are accumulated in a hidden state, the actual operation of the equipment is influenced, and the final production loss of a customer is caused, so that an equipment comprehensive index is needed, the health state and risk point of the current equipment are known in time, part of single support functions such as predictive maintenance of the equipment exist aiming at the series of factors, and maintenance early warning is sent out when equipment components reach a maintenance period.
However, in the related art, tracking and early warning are only performed aiming at single-point factors, and systematic summarization is lacking on influencing factors of equipment operation, so that the integrity of a detection result is insufficient, the limitation exists in functional early warning, the health state of equipment cannot be quantitatively fed back, automatic detection of the health degree of the equipment cannot be realized, the reliability of health detection is reduced, and the problem is to be solved urgently.
Disclosure of Invention
The application provides a health detection method and device of automation equipment, which are used for solving the problems that in the related technology, tracking and early warning are only carried out aiming at single-point factors, systematic summarization is lacking on influencing factors of equipment operation, the integrity of detection results is insufficient, limitation exists in functional early warning, the health state of the equipment cannot be quantitatively fed back, the automatic detection of the equipment health degree cannot be realized, the reliability of the health detection is reduced and the like.
An embodiment of a first aspect of the present application provides a health detection method of an automation device, including the following steps: acquiring maintenance information of target automation equipment, the service life of preset key devices, key parameters of preset core devices, actual machining deviation and/or machining quality; calculating maintenance information of the equipment, service life of the preset key device, key parameters of the preset core device, actual machining deviation and/or health score of the machining quality based on the maintenance information of the equipment, service life of the preset key device, key parameters of the preset core device, the actual machining deviation and/or the machining quality and corresponding weights; generating the actual health degree of the target automation equipment according to the maintenance information of the equipment, the service life of the preset key device, the key parameters of the preset core device, the actual machining deviation and/or the health score of the machining quality.
Wherein in an embodiment of the present application, the calculating the maintenance information of the equipment, the service life of the preset critical device, the critical parameter of the preset critical device, the actual machining deviation and/or the machining quality and the corresponding weight based on the maintenance information of the equipment, the service life of the preset critical device, the critical parameter of the preset critical device, the actual machining deviation and/or the health score of the machining quality includes: calculating a device state score according to the actual use value of each hardware device of the equipment, and obtaining a health score of maintenance information of the equipment according to the device state score and the device coefficient; obtaining a health score of the service life of the preset key device according to the actual accumulated use value of the preset key device; obtaining health scores of key parameters of the preset core device according to the working parameters of the preset core device; obtaining a health score of the actual processing deviation according to the processing result of the equipment; and obtaining the health score of the processing quality according to the processing yield of the equipment.
Optionally, in one embodiment of the present application, further includes: and generating a health trend curve according to the historical health and the actual health while displaying the actual health to a user.
Optionally, in one embodiment of the present application, further includes: receiving a query instruction of the user; and acquiring the actual health score of the target shift corresponding to the query instruction from the historical health degree.
Optionally, in an embodiment of the present application, after obtaining the actual health score of the target shift corresponding to the query instruction, the method further includes: generating failure items and reasons of the target automation equipment according to maintenance information of the target automation equipment of the target shift, service lives of preset key devices, key parameters of preset core devices, actual machining deviation and/or machining quality.
An embodiment of a second aspect of the present application provides a health detection apparatus of an automation device, including: the acquisition module is used for acquiring maintenance information of the target automation equipment, the service life of a preset key device, key parameters of the preset core device, actual machining deviation and/or machining quality; the calculation module is used for calculating maintenance information of the equipment, the service life of the preset key device, the key parameters of the preset core device, the actual machining deviation and/or the machining quality and the corresponding weight based on the maintenance information of the equipment, the service life of the preset key device, the key parameters of the preset core device, the actual machining deviation and/or the health score of the machining quality; the detection module is used for generating the actual health degree of the target automation equipment according to the maintenance information of the equipment, the service life of the preset key device, the key parameters of the preset core device, the actual machining deviation and/or the health score of the machining quality.
Wherein, in one embodiment of the present application, the computing module comprises: a first obtaining unit, configured to calculate a device status score according to an actual usage value of each hardware device of the apparatus, and obtain a health score of maintenance information of the apparatus according to the device status score and a device coefficient; the second acquisition unit is used for obtaining a health score of the service life of the preset key device according to the actual accumulated use value of the preset key device; a third obtaining unit, configured to obtain a health score of a key parameter of the preset core device according to the working parameter of the preset core device; a fourth obtaining unit, configured to obtain a health score of the actual machining deviation according to a machining result of the apparatus; and a fifth acquisition unit, configured to obtain a health score of the processing quality according to the processing yield of the equipment.
Optionally, in one embodiment of the present application, further includes: the generation module is used for generating a health trend curve according to the historical health degree and the actual health degree while displaying the actual health degree to a user.
Optionally, in one embodiment of the present application, further includes: the receiving module is used for receiving the inquiry instruction of the user; and the query module is used for acquiring the actual health score of the target shift corresponding to the query instruction from the historical health degree.
Optionally, in one embodiment of the present application, the query module further includes: the generation unit is used for generating the failure items and reasons of the target automation equipment according to the maintenance information of the target automation equipment of the target shift, the service life of a preset key device, the key parameters of the preset core device, the actual machining deviation and/or the machining quality after acquiring the actual health score of the target shift corresponding to the query instruction.
An embodiment of a third aspect of the present application provides an electronic device, including: the health detection system comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the program to realize the health detection method of the automation device.
A fourth aspect of the present application provides a computer readable storage medium storing a computer program which when executed by a processor implements a method for health detection of an automation device as above.
According to the embodiment of the application, based on quantitative processing of equipment health, equipment is diagnosed systematically and the current health degree of the equipment is fed back, and visualization of equipment health conditions is achieved, so that customers know the implicit working conditions of the equipment, equipment detection is enabled to be more comprehensive and systematic, the fault probability of sudden equipment is reduced, and the safety, reliability and practicability of the equipment are further guaranteed. Therefore, the problems that in the related technology, tracking and early warning are only carried out aiming at single-point factors, systematic summarization is lacking on influencing factors of equipment operation, the integrity of detection results is insufficient, limitation exists in functional early warning, the health state of equipment cannot be quantitatively fed back, automatic detection of the health degree of the equipment cannot be realized, the reliability of health detection is reduced and the like are solved.
Additional aspects and advantages of the application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the application.
Drawings
The foregoing and/or additional aspects and advantages of the present application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings, in which:
fig. 1 is a flowchart of a method for detecting health of an automation device according to an embodiment of the present application;
FIG. 2 is a schematic diagram of a deviation of a screw locking process according to one embodiment of the present application;
FIG. 3 is a schematic diagram of device health in accordance with one embodiment of the present application;
fig. 4 is a schematic structural view of a health detection device of an automation apparatus according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
Embodiments of the present application are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the drawings are exemplary and intended for the purpose of explaining the present application and are not to be construed as limiting the present application.
The following describes a health detection method and apparatus for an automation device according to an embodiment of the present application with reference to the accompanying drawings. Aiming at the problems that in the related technology mentioned in the background technology center, tracking and early warning are only carried out aiming at single-point factors, systematic summarization is lack of influencing factors of equipment operation, so that the integrity of detection results is insufficient, limitation exists in functional early warning, the health state of equipment cannot be quantitatively fed back, automatic detection of the health degree of the equipment cannot be realized, and the reliability of the health detection is reduced. Therefore, the problems that in the related technology, tracking and early warning are only carried out aiming at single-point factors, systematic summarization is lacking on influencing factors of equipment operation, the integrity of detection results is insufficient, limitation exists in functional early warning, the health state of equipment cannot be quantitatively fed back, automatic detection of the health degree of the equipment cannot be realized, the reliability of health detection is reduced and the like are solved.
Specifically, fig. 1 is a flow chart of a health detection method of an automation device according to an embodiment of the present application.
As shown in fig. 1, the health detection method of the automated equipment includes the following steps:
in step S101, maintenance information of the target automation device, a service life of a preset key device, key parameters of the preset core device, an actual machining deviation and/or machining quality are obtained.
It can be understood that in the embodiment of the application, the maintenance information of the automation device and the service life of the preset key device can be formed by introducing the hardware of the device through the system, so that the maintenance period and the service life of the device are configured, the key parameters of the preset core device can be derived from the temperature and humidity change of the key position of the device and the pressure parameter analysis of the processing part of the device, the actual processing deviation can be the processing deviation generated in the cumulative use of the partial processing, and the processing quality can be the processing yield level.
It should be noted that the preset key device and the preset core device are set by those skilled in the art according to actual situations, and are not specifically limited herein.
In the actual execution process, when maintenance information of the automatic equipment and the service life of a preset key device are acquired, the system performs targeted tracking, and different hardware characteristics lead to different tracking modes, such as the recording and analysis of cylinder hardware by operation times, the recording and analysis of motor hardware by stroke length and the recording and analysis of sensor hardware by service time.
For the actual machining deviation, as shown in fig. 2, a schematic diagram of the machining deviation of the lock screw according to an embodiment of the present application is shown. When the screw locking position is not in the center, the obtained instant machining result is correct, but continuous deviation can occur after long-term use, so that subsequent machining deviation is generated, and the actual machining deviation is obtained by measuring the machining result.
According to the method and the device, maintenance information of the target automation device, service life of the preset key device, key parameters of the preset core device, actual machining deviation and/or machining quality can be obtained, intelligent definition of the health degree of the device is achieved through collecting the device information in multiple aspects, and comprehensiveness of the health detection process is improved.
In step S102, the maintenance information of the equipment, the service life of the preset key device, the key parameters of the preset core device, the actual machining deviation and/or the machining quality and the corresponding weights are calculated based on the maintenance information of the equipment, the service life of the preset key device, the key parameters of the preset core device, the actual machining deviation and/or the health score of the machining quality.
It can be understood that in the embodiment of the present application, the device information obtained in the above steps may be combined with different weights corresponding to each parameter to perform weighted accumulation, so as to implement a calculation process of the health score corresponding to each parameter.
According to the method and the device, the equipment health quantification process can be perfected by conducting further data processing on the obtained equipment information based on the maintenance information of the equipment, the service life of the preset key device, the key parameters of the preset core device, the actual machining deviation and/or the machining quality and the corresponding maintenance information of the weight computing equipment, the service life of the preset key device, the key parameters of the preset core device, the actual machining deviation and/or the health score of the machining quality.
In one embodiment of the present application, calculating maintenance information of the equipment, service life of the preset key device, key parameters of the preset core device, actual machining deviation and/or machining quality and corresponding weights, and health scores of the preset key device, key parameters of the preset core device, actual machining deviation and/or machining quality based on the maintenance information of the equipment, service life of the preset key device, and key parameters of the preset core device include: calculating a device state score according to the actual use value of each hardware device of the equipment, and obtaining a health score of maintenance information of the equipment according to the device state score and the device coefficient; health scores of service lives of the preset key devices are obtained according to actual accumulated use values of the preset key devices; obtaining health scores of key parameters of the preset core device according to the working parameters of the preset core device; obtaining a health score of the actual processing deviation according to the processing result of the equipment; and obtaining a health score of the processing quality according to the processing yield of the equipment.
It may be understood that, in the embodiment of the present application, for the maintenance information data, each hardware component in the system may have an actual use value, a reference maintenance threshold and a maximum maintenance threshold, where the actual use value is smaller than the reference maintenance threshold, and no influence is caused when the use value is greater than or equal to the reference maintenance threshold, and the component should be maintained, otherwise, there is a probability of generating a use risk, and the maximum maintenance threshold is greater than the reference maintenance threshold, and when the actual use value is greater than the maximum maintenance threshold, there is a higher risk to influence the use, and the use cannot be received.
For the service life of a preset key device, in the hardware component configuration, a reference service life threshold value and a maximum service life threshold value can be set, when the use accumulated value exceeds the reference service life threshold value, the hardware has a use risk, and when the use accumulated value exceeds the maximum service life threshold value, the use risk is greatly influenced and is not receivable.
Aiming at key parameter analysis of a core device, an upper normal working threshold and a lower normal working threshold, and an upper alarm working threshold and a lower alarm working threshold can be set from a scene of temperature and humidity change of key positions of equipment and pressure parameter analysis of processing parts of the equipment. For a temperature and humidity change scene of a key position of the equipment, because a temperature and humidity application range exists in hardware devices of the equipment, if the equipment contains a heating part, if the environmental temperature is poorly managed, or a cooling part such as a fan breaks down or ages, the temperature rises to a certain threshold value, and the operation of the equipment can be influenced. The excessive high temperature attribution alarm belongs to the health degree monitoring range, and informs clients of direct treatment, and continuous low temperature can cause faults and service life shortening, and belongs to health degree analysis. For pressure parameter analysis of equipment processing parts, such as parts like pressure maintaining parts and electric screwdrivers, pressure monitoring exists in working, but as equipment is used continuously, a motion system can generate accumulated deviation to enable deviation of a designated position to occur, so that processing failure is caused. For example, taking a pressure maintaining component as an example, processing failure of a product generated by too light pressure belongs to an alarm category and does not belong to health degree analysis, and long-term deviation from the pressure in a normal range can cause batch hidden trouble to equipment under the condition that a processing result shows to be normal.
For the actual machining deviation, a maximum threshold, a minimum threshold and a normal threshold of the actual machining deviation may be set, the apparatus may have the machining deviation in cumulative use, the machining result may be measured, and the process capability parameter CPK (Complex Process Capability) value may be calculated to describe the machining deviation based on determining the maximum threshold, the minimum threshold and the like.
For the processing quality, a minimum threshold of the processing quality can be set, and if the processing yield rate is reduced beyond a threshold range, obvious loss can be caused to the actual customer productivity.
In the actual execution process, when the computing device maintains the health score, firstly, calculating the device state score of each hardware, setting the maximum state score to be 1, and setting the current accumulated use value to be 0 when the current accumulated use value is larger than the maximum maintenance threshold. The state score formula is
Figure BDA0004027544710000061
After the status score of each hardware device is obtained, if the status score is a key device, the status score is multiplied by a key device coefficient, and the coefficient is a system configuration parameter due to different key device conditions of different devices. And adding the state scores of all the weighted hardware devices, dividing the added state scores by the state score of the maximum weighted hardware device, and multiplying the added state scores by the data weight to obtain the health score of the equipment maintenance information. When the equipment is maintained, maintenance hardware is selected, and the actual use value is reset and the maintenance history is recorded.
When the health score of the service life of the preset key device is calculated, the same as the calculation process of the equipment maintenance health score, the health score of the service life of the preset key device is calculated and obtained through the actual accumulated use value of the preset key device. When the equipment is maintained, the hardware is selected to be replaced, and the actual accumulated use value is reset and the maintenance history is recorded.
When the health score of the key parameters of the preset core device is calculated, setting the initial value of each parameter to be 1, and when the parameter exceeds the normal working threshold and does not reach the alarm working threshold, the score is reduced by half every continuous shift, and finally the score tends to zero. And after the key parameter scores of all the core devices are summed, dividing the sum by the maximum score, and multiplying the sum by the data weight to obtain the health score of the key parameters of the preset core devices.
When calculating the healthy score of the actual machining deviation, the score starts to decrease when the machining deviation continues to increase, exceeding the normal threshold. The higher the duty cycle of the process profile outside the normal threshold, the lower the score, the sum of all CPK items divided by the maximum score, multiplied by the data weight, resulting in a health score of the actual process deviation.
When calculating the health score of the processing quality, the health score of the processing quality can be obtained by analyzing the continuous yield in shifts, returning the partial score to zero when the continuous yield is lower than a minimum threshold value, otherwise, multiplying the full score by the data weight.
According to the method and the device, corresponding health scores can be obtained by calculating maintenance information of equipment, service lives of preset key devices, key parameters of the preset core devices, actual machining deviation and/or machining quality and corresponding weights, so that operation risk references in various aspects of automatic machining equipment are obtained, and a measurable process of equipment health conditions is further implemented.
In step S103, the actual health of the target automation device is generated according to the maintenance information of the device, the service life of the preset key device, the key parameters of the preset core device, the actual machining deviation and/or the health score of the machining quality.
It may be appreciated that, in the embodiment of the present application, when the actual health degree of the target automation device is generated, the accumulated result may be recorded as the actual health degree of the automation device by accumulating the corresponding health degree scores obtained in the above steps.
In some embodiments, the health maximum score may be set to 100 points as a comprehensive analysis criterion. For example, the calculated health score corresponding to the maintenance information of the obtained equipment is 15.34 points, the service life corresponding to the preset key device is 16.56 points, the health score corresponding to the key parameter of the preset core device is 17.78 points, the actual processing deviation corresponding to the health score is 19.49 points, the processing quality corresponding to the health score is 15.21 points, and the actual health degree of the target automation equipment can be obtained by adding the obtained health scores, namely the health score of the automation equipment is 84.38 points.
As shown in fig. 3, which is a schematic diagram of the health degree of the equipment according to an embodiment of the present application, it can be known that the system health degree is intelligent through predictive maintenance, quality early warning, critical device life early warning, core device risk early warning and processing deviation early warning.
According to the embodiment of the application, the actual health degree of the target automation equipment can be generated according to the maintenance information of the equipment, the service life of the preset key device, the key parameters of the preset core device, the actual machining deviation and/or the health score of the machining quality, so that the hidden working condition of the automation equipment is visually realized, and the intelligent level of the health detection process is improved.
Optionally, in one embodiment of the present application, further includes: and generating a health trend curve according to the historical health and the actual health while displaying the actual health to the user.
It may be appreciated that in embodiments of the present application, the historical health may be from a health score of a historical shift, and the actual health may be a score of the health of the on-duty device, thereby obtaining a health trend curve of the target automation device.
For example, the actual health degree of the automatic equipment on duty is obtained in the current month 10, the obtained actual health score can be displayed to the user through the electronic display screen, the health degree score detection record of each shift in the first ten days from the current month is queried, the historical health degree is obtained, and the health trend curve is generated from the obtained data and displayed on the electronic display screen.
According to the method and the device for displaying the health degree of the automatic equipment, the actual health degree can be displayed to the user, meanwhile, the health trend curve is generated according to the historical health degree and the actual health degree, and the visualization of the change condition of the health degree of the automatic equipment is achieved by displaying the trend curve of the health degree within a certain time range to the client, so that the client can analyze the degradation condition of the health degree of the equipment, and the method and the device are more practical.
Optionally, in one embodiment of the present application, further includes: receiving a query instruction of a user; and obtaining the actual health score of the target shift corresponding to the query instruction from the historical health degree.
It can be appreciated that, in the embodiment of the present application, the query instruction of the user may be an acquisition request issued by the user for the health degree of a specified shift, so as to view the health degree score details of any shift.
In some embodiments, the health score obtained in each shift may be uploaded or stored locally, and when the user sends a health query condition at a specified time, corresponding health score data is obtained. For example, the user sends out a query command to request to query the actual health score of the automation equipment on a certain day, and all the shift health score information on the same day can be acquired and displayed according to the command.
According to the embodiment of the invention, the query instruction of the user can be received, and the actual health score of the target shift corresponding to the query instruction is obtained from the historical health, so that the management of the user on the health state of the equipment is further simplified, the systematic operation of the health score of the equipment is realized, and the operation of the user is facilitated.
Optionally, in one embodiment of the present application, after obtaining the actual health score of the target shift corresponding to the query instruction, the method further includes: and generating failure items and reasons of the target automation equipment according to maintenance information of the target automation equipment of the target shift, service lives of preset key devices, key parameters of the preset core devices, actual machining deviation and/or machining quality.
It can be understood that in the embodiment of the present application, the failure items and reasons of the target automation device may be obtained by performing data analysis on the maintenance information of the target automation device for the target shift, the service life of the preset key device, the key parameters of the preset core device, the actual machining deviation and the machining quality.
For example, the average value of the historical health scores corresponding to different items is obtained through the health score information of the historical shifts within a certain time range, and the average value is compared with the health scores of the items of the current shifts, so that the misclassified items are obtained in a comparison mode. Or observing the health trend curves corresponding to the items to obtain the misclassified items and analyzing root cause.
According to the method and the device, the failure items and reasons of the target automation equipment can be generated according to maintenance information of the target automation equipment of a target shift, the service life of a preset key device, key parameters of the preset core device, actual machining deviation and/or machining quality, so that root cause analysis is conducted according to the failure items, adjustment is actively conducted, production loss caused by faults is avoided, and the probability of sudden equipment faults is reduced.
According to the health detection method of the automatic equipment, provided by the embodiment of the application, based on quantitative treatment of equipment health, equipment can be diagnosed systematically and the current health degree of the equipment is fed back, and the visualization of equipment health conditions is realized, so that customers can know the implicit working conditions of the equipment, the equipment detection is more comprehensive and systematic, the fault probability of sudden equipment is reduced, and the safety, reliability and practicability of the equipment are further guaranteed. Therefore, the problems that in the related technology, tracking and early warning are only carried out aiming at single-point factors, systematic summarization is lacking on influencing factors of equipment operation, the integrity of detection results is insufficient, limitation exists in functional early warning, the health state of equipment cannot be quantitatively fed back, automatic detection of the health degree of the equipment cannot be realized, the reliability of health detection is reduced and the like are solved.
Next, a health detection device of an automation apparatus according to an embodiment of the present application will be described with reference to the accompanying drawings.
Fig. 4 is a block schematic diagram of a health detection device of an automated apparatus according to an embodiment of the present application.
As shown in fig. 4, the health detection apparatus 10 of the automated equipment includes: an acquisition module 100, a calculation module 200 and a detection module 300.
The acquiring module 100 is configured to acquire maintenance information of the target automation device, a service life of a preset key device, a key parameter of the preset core device, an actual machining deviation and/or a machining quality.
The calculating module 200 is configured to calculate maintenance information of the equipment, service life of the preset key device, critical parameters of the preset core device, actual machining deviation and/or machining quality, and health scores of the corresponding weights, based on the maintenance information of the equipment, the service life of the preset key device, the critical parameters of the preset core device, the actual machining deviation and/or machining quality.
The detection module 300 is configured to generate an actual health degree of the target automation device according to maintenance information of the device, a service life of a preset key device, a key parameter of a preset core device, an actual machining deviation and/or a health score of machining quality.
Wherein, in one embodiment of the present application, the computing module 200 comprises: a first acquisition unit, a second acquisition unit, a third acquisition unit, a fourth acquisition unit, and a fifth acquisition unit.
The first acquisition unit is used for calculating a device state score according to the actual use value of each hardware device of the equipment and obtaining a health score of maintenance information of the equipment according to the device state score and the device coefficient.
And the second acquisition unit is used for obtaining the health score of the service life of the preset key device according to the actual accumulated use value of the preset key device.
And the third acquisition unit is used for obtaining the health score of the key parameters of the preset core device according to the working parameters of the preset core device.
And the fourth acquisition unit is used for obtaining the health score of the actual machining deviation according to the machining result of the equipment.
And a fifth acquisition unit, configured to obtain a health score of the processing quality according to the processing yield of the equipment.
Optionally, in one embodiment of the present application, the apparatus 10 further comprises: and generating a module.
The generation module is used for generating a health trend curve according to the historical health degree and the actual health degree while displaying the actual health degree to the user.
Optionally, in one embodiment of the present application, the apparatus 10 further comprises: a receiving module and a query module.
The receiving module is used for receiving the inquiry instruction of the user.
And the query module is used for acquiring the actual health score of the target shift corresponding to the query instruction from the historical health degree.
Optionally, in one embodiment of the present application, the query module further includes: and a generating unit.
The generating unit is used for generating the failure items and reasons of the target automation equipment according to the maintenance information of the target automation equipment of the target shift, the service life of the preset key device, the key parameters of the preset core device, the actual machining deviation and/or the machining quality after acquiring the actual health score of the target shift corresponding to the query instruction.
It should be noted that the foregoing explanation of the embodiment of the method for detecting health of an automation device is also applicable to the health detection device of the automation device of the embodiment, and will not be repeated here.
According to the health detection device of the automatic equipment, provided by the embodiment of the application, the equipment can be diagnosed and the current health degree of the equipment is fed back systematically based on quantitative treatment of the equipment health, and the visualization of the equipment health condition is realized, so that a customer can know the implicit working condition of the equipment, the equipment detection is more comprehensive, the fault probability of sudden equipment is reduced, and the safety, reliability and practicability of the equipment are further guaranteed. Therefore, the problems that in the related technology, tracking and early warning are only carried out aiming at single-point factors, systematic summarization is lacking on influencing factors of equipment operation, the integrity of detection results is insufficient, limitation exists in functional early warning, the health state of equipment cannot be quantitatively fed back, automatic detection of the health degree of the equipment cannot be realized, the reliability of health detection is reduced and the like are solved.
Fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present application. The electronic device may include:
memory 501, processor 502, and a computer program stored on memory 501 and executable on processor 502.
The processor 502, when executing the program, implements the health detection method of the automated device provided in the above embodiment.
Further, the electronic device further includes:
a communication interface 503 for communication between the memory 501 and the processor 502.
Memory 501 for storing a computer program executable on processor 502.
The memory 501 may include high-speed RAM memory and may also include non-volatile memory (non-volatile memory), such as at least one disk memory.
If the memory 501, the processor 502, and the communication interface 503 are implemented independently, the communication interface 503, the memory 501, and the processor 502 may be connected to each other via a bus and perform communication with each other. The bus may be an industry standard architecture (Industry Standard Architecture, abbreviated ISA) bus, an external device interconnect (Peripheral Component, abbreviated PCI) bus, or an extended industry standard architecture (Extended Industry Standard Architecture, abbreviated EISA) bus, among others. The buses may be divided into address buses, data buses, control buses, etc. For ease of illustration, only one thick line is shown in fig. 5, but not only one bus or one type of bus.
Alternatively, in a specific implementation, if the memory 501, the processor 502, and the communication interface 503 are integrated on a chip, the memory 501, the processor 502, and the communication interface 503 may perform communication with each other through internal interfaces.
The processor 502 may be a central processing unit (Central Processing Unit, abbreviated as CPU), or an application specific integrated circuit (Application Specific Integrated Circuit, abbreviated as ASIC), or one or more integrated circuits configured to implement embodiments of the present application.
The present embodiment also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the health detection method of an automated device as above.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or N embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present application, the meaning of "N" is at least two, such as two, three, etc., unless explicitly defined otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and additional implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order from that shown or discussed, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present application.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or N wires, a portable computer cartridge (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It is to be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the N steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. As with the other embodiments, if implemented in hardware, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
Those of ordinary skill in the art will appreciate that all or a portion of the steps carried out in the method of the above-described embodiments may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable storage medium, and where the program, when executed, includes one or a combination of the steps of the method embodiments.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing module, or each unit may exist alone physically, or two or more units may be integrated in one module. The integrated modules may be implemented in hardware or in software functional modules. The integrated modules may also be stored in a computer readable storage medium if implemented in the form of software functional modules and sold or used as a stand-alone product.
The above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, or the like. Although embodiments of the present application have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the application, and that variations, modifications, alternatives, and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the application.

Claims (12)

1. A method for health detection of an automated device, comprising the steps of:
acquiring maintenance information of target automation equipment, the service life of preset key devices, key parameters of preset core devices, actual machining deviation and/or machining quality;
calculating maintenance information of the equipment, service life of the preset key device, key parameters of the preset core device, actual machining deviation and/or health score of the machining quality based on the maintenance information of the equipment, service life of the preset key device, key parameters of the preset core device, the actual machining deviation and/or the machining quality and corresponding weights; and
generating the actual health degree of the target automation equipment according to the maintenance information of the equipment, the service life of the preset key device, the key parameters of the preset core device, the actual machining deviation and/or the health score of the machining quality.
2. The method according to claim 1, wherein the calculating the health score of the equipment maintenance information, the life span of the preset critical device, the critical parameters of the preset critical device, the actual machining deviation and/or the machining quality and the corresponding weights based on the equipment maintenance information, the life span of the preset critical device, the critical parameters of the preset critical device, the actual machining deviation and/or the machining quality comprises:
calculating a device state score according to the actual use value of each hardware device of the equipment, and obtaining a health score of maintenance information of the equipment according to the device state score and the device coefficient;
obtaining a health score of the service life of the preset key device according to the actual accumulated use value of the preset key device;
obtaining health scores of key parameters of the preset core device according to the working parameters of the preset core device;
obtaining a health score of the actual processing deviation according to the processing result of the equipment;
and obtaining the health score of the processing quality according to the processing yield of the equipment.
3. The method as recited in claim 1, further comprising:
And generating a health trend curve according to the historical health and the actual health while displaying the actual health to a user.
4. A method according to claim 3, further comprising:
receiving a query instruction of the user;
and acquiring the actual health score of the target shift corresponding to the query instruction from the historical health degree.
5. The method of claim 4, further comprising, after obtaining the actual health score of the target shift corresponding to the query instruction:
generating failure items and reasons of the target automation equipment according to maintenance information of the target automation equipment of the target shift, service lives of preset key devices, key parameters of preset core devices, actual machining deviation and/or machining quality.
6. A health detection device of an automated apparatus, comprising:
the acquisition module is used for acquiring maintenance information of the target automation equipment, the service life of a preset key device, key parameters of the preset core device, actual machining deviation and/or machining quality;
the calculation module is used for calculating maintenance information of the equipment, the service life of the preset key device, the key parameters of the preset core device, the actual machining deviation and/or the machining quality and the corresponding weight based on the maintenance information of the equipment, the service life of the preset key device, the key parameters of the preset core device, the actual machining deviation and/or the health score of the machining quality; and
The detection module is used for generating the actual health degree of the target automation equipment according to the maintenance information of the equipment, the service life of the preset key device, the key parameters of the preset core device, the actual machining deviation and/or the health score of the machining quality.
7. The apparatus of claim 6, wherein the computing module comprises:
a first obtaining unit, configured to calculate a device status score according to an actual usage value of each hardware device of the apparatus, and obtain a health score of maintenance information of the apparatus according to the device status score and a device coefficient;
the second acquisition unit is used for obtaining a health score of the service life of the preset key device according to the actual accumulated use value of the preset key device;
a third obtaining unit, configured to obtain a health score of a key parameter of the preset core device according to the working parameter of the preset core device;
a fourth obtaining unit, configured to obtain a health score of the actual machining deviation according to a machining result of the apparatus;
and a fifth acquisition unit, configured to obtain a health score of the processing quality according to the processing yield of the equipment.
8. The apparatus as recited in claim 6, further comprising:
the generation module is used for generating a health trend curve according to the historical health degree and the actual health degree while displaying the actual health degree to a user.
9. The apparatus as recited in claim 8, further comprising:
the receiving module is used for receiving the inquiry instruction of the user;
and the query module is used for acquiring the actual health score of the target shift corresponding to the query instruction from the historical health degree.
10. The apparatus of claim 9, wherein the query module further comprises:
the generation unit is used for generating the failure items and reasons of the target automation equipment according to the maintenance information of the target automation equipment of the target shift, the service life of a preset key device, the key parameters of the preset core device, the actual machining deviation and/or the machining quality after acquiring the actual health score of the target shift corresponding to the query instruction.
11. An electronic device, comprising: a memory, a processor and a computer program stored on the memory and executable on the processor, the processor executing the program to implement the method of health detection of an automated apparatus according to any one of claims 1-5.
12. A computer-readable storage medium, on which a computer program is stored, characterized in that the program is executed by a processor for implementing a health detection method of an automation device according to any one of claims 1-5.
CN202211714846.2A 2022-12-29 2022-12-29 Health detection method and device for automation equipment Pending CN116088444A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202211714846.2A CN116088444A (en) 2022-12-29 2022-12-29 Health detection method and device for automation equipment

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202211714846.2A CN116088444A (en) 2022-12-29 2022-12-29 Health detection method and device for automation equipment

Publications (1)

Publication Number Publication Date
CN116088444A true CN116088444A (en) 2023-05-09

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