CN116107282B

CN116107282B - Industrial robot predictive maintenance system based on enterprise application integration

Info

Publication number: CN116107282B
Application number: CN202310392855.2A
Authority: CN
Inventors: 郭东栋; 杜文博; 马海涛; 彭浩; 姜宗睿; 张妍; 张诗岳; 赵灿
Original assignee: Beijing Benz Automotive Co Ltd
Current assignee: Beijing Benz Automotive Co Ltd
Priority date: 2023-04-13
Filing date: 2023-04-13
Publication date: 2023-06-09
Anticipated expiration: 2043-04-13
Also published as: CN116107282A

Abstract

The application provides an industrial robot predictive maintenance system based on enterprise application integration, comprising: the integrated application terminal system, the service terminal system and the client terminal system are sequentially connected in a communication mode and are used for respectively acquiring monitoring data of all industrial robots in the automobile manufacturing site and industrial robot fault prediction tool data from enterprise applications corresponding to automobile manufacturing enterprises, storing the monitoring data, generating industrial robot fault prediction results aiming at the automobile manufacturing site based on the industrial robot fault prediction tool data and the monitoring data, and displaying the industrial robot fault prediction results. According to the method and the system, predictive maintenance for the industrial robot is achieved through integration of enterprise application and data interaction among a plurality of subsystems, various different systems and data are integrated, more comprehensive and accurate data support can be provided for the predictive maintenance, and further efficiency and comprehensiveness for the predictive maintenance of the industrial robot can be effectively improved.

Description

Industrial robot predictive maintenance system based on enterprise application integration

Technical Field

The application relates to the technical field of industrial equipment maintenance and management, in particular to an industrial robot predictive maintenance system based on enterprise application integration.

Background

Predictive maintenance is maintenance that is performed continuously or intermittently, as determined by observed conditions, to monitor, diagnose, or predict a condition indicator of an organization, system, or component. The results of such maintenance should indicate the current and future functional capabilities or the nature and schedule of scheduled maintenance. In the automotive manufacturing industry, predictive maintenance of industrial robot equipment is required in order to maintain operational reliability and timeliness of industrial robots.

At present, although the existing predictive maintenance mode can perform periodic (or continuous) state monitoring and fault diagnosis on a main (or required) part of the industrial robot when the industrial robot is in operation, the state of equipment is judged, and further maintenance activities are determined. However, due to huge data volume and numerous types of industrial robots, the existing predictive maintenance process lacks a complete large system architecture, so that when the large data volume and various types of enterprises are applied in the predictive maintenance process of the industrial robots in the automobile manufacturing industry, the efficiency and the effectiveness of the predictive maintenance of the industrial robots are limited, and the requirements of high maintenance efficiency and comprehensiveness of automobile manufacturing enterprises cannot be met.

Disclosure of Invention

In view of this, embodiments of the present application provide an industrial robot predictive maintenance system based on enterprise application integration that obviates or mitigates one or more of the disadvantages of the prior art.

The application provides an industrial robot predictive maintenance system based on enterprise application integration, which comprises:

the integrated application terminal system is used for respectively acquiring monitoring data of each industrial robot and industrial robot fault prediction tool data in the automobile manufacturing site from different types of enterprise applications corresponding to the automobile manufacturing enterprises;

the service terminal system is in communication connection with the integrated application terminal system, and is used for storing the monitoring data acquired by the integrated application terminal system and generating an industrial robot fault prediction result aiming at the automobile manufacturing site based on the industrial robot fault prediction tool data and the monitoring data;

and the client terminal system is in communication connection with the service terminal system and is used for acquiring the industrial robot fault prediction result from the service terminal system and displaying the industrial robot fault prediction result.

In some embodiments of the present application, the integrated application subsystem includes:

The industrial internet of things platform is used for monitoring all industrial robots in the automobile manufacturing site in real time based on industrial internet of things equipment so as to acquire monitoring data of all the industrial robots, wherein the monitoring data comprise: operational data and status information;

and the fault prediction tool module is used for storing industrial robot fault prediction tool data for predicting faults of the industrial robot.

In some embodiments of the present application, the fault prediction tool module includes:

the system comprises a knowledge base and a rule unit, wherein the knowledge base and rule unit is used for generating predictive maintenance rules for the industrial robot according to monitoring data of the industrial robot, which are acquired in advance, based on a preset knowledge base and a rule engine;

and the artificial intelligent unit is used for training a preset machine learning model by adopting the pre-acquired historical monitoring data of the industrial robot and the corresponding historical fault prediction result so as to generate an industrial robot fault prediction model for performing fault prediction on the monitoring data of the industrial robot.

In some embodiments of the present application, the service sub-system includes:

the database is in communication connection with the industrial Internet of things platform and is used for storing the monitoring data of each industrial robot acquired by the industrial Internet of things platform;

And the big data processing platform is respectively connected with the database, the knowledge base, the rule unit and the artificial intelligent unit in a communication way and is used for carrying out fault prediction on the monitoring data of each industrial robot extracted from the database by adopting the predictive maintenance rule obtained from the knowledge base and the rule unit and/or the industrial robot fault prediction model obtained from the artificial intelligent unit so as to obtain the current industrial robot fault prediction result aiming at the automobile manufacturing site.

In some embodiments of the present application, the client sub-system further comprises:

the mobile terminal application is respectively connected with the database and the big data processing platform in a communication way and is used for acquiring and displaying monitoring data of each industrial robot and industrial robot fault prediction results aiming at the automobile manufacturing site in real time;

the desktop application is respectively connected with the database and the big data processing platform in a communication way, and is used for acquiring monitoring data of each industrial robot and industrial robot fault prediction results aiming at the automobile manufacturing site in real time, generating corresponding charts according to the monitoring data of each industrial robot and displaying the charts, and generating corresponding early warning information according to the industrial robot fault prediction results and displaying the corresponding early warning information.

In some embodiments of the present application, the mobile terminal application is provided in an industrial wireless PAD terminal.

In some embodiments of the present application, the service sub-system further comprises:

the access service module is respectively in communication connection with the large data processing platform, the enterprise application of each type corresponding to the automobile manufacturing enterprise and the client subsystem, and is used for sending the industrial robot fault prediction result generated by the large data processing platform to at least one of the enterprise application, the mobile terminal application and the desktop application of each type;

correspondingly, the mobile terminal application and the desktop application are respectively connected with the database and the big data processing platform based on the communication between the access service module and the big data processing platform.

the safety management module is respectively in communication connection with the access service module and the client terminal system, and is used for carrying out identity verification and data access permission verification on the client terminal system when the client terminal system requests to access the database and the big data processing platform through the access service module, and establishing access connection of the client terminal system to the database and the big data processing platform through the access service module after the identity verification and the data access permission verification are passed.

the distributed data module is respectively in communication connection with the big data processing platform and the access service module, and is used for carrying out distributed storage on the industrial robot fault prediction result in the big data processing platform and sending the industrial robot fault prediction result stored in a distributed mode to the client terminal system through the access service module; the distributed data module is further used for receiving maintenance record data for the industrial robot sent by the client subsystem through the access service module and performing distributed storage on the maintenance record data.

In some embodiments of the present application, the integrated application subsystem further comprises:

the resource management platform is used for storing non-monitoring data of each industrial robot, wherein the non-monitoring data comprises: operating environment data, maintenance record data, and spare part inventory data for the industrial robot.

The application provides an industrial robot predictive maintenance system based on enterprise application integration, which comprises: the integrated application terminal system is used for respectively acquiring monitoring data of each industrial robot and industrial robot fault prediction tool data in the automobile manufacturing site from different types of enterprise applications corresponding to the automobile manufacturing enterprises; the service terminal system is in communication connection with the integrated application terminal system, and is used for storing the monitoring data acquired by the integrated application terminal system and generating an industrial robot fault prediction result aiming at the automobile manufacturing site based on the industrial robot fault prediction tool data and the monitoring data; the client terminal system is in communication connection with the service terminal system, and is used for acquiring the industrial robot fault prediction result from the service terminal system, displaying the industrial robot fault prediction result, realizing predictive maintenance on the industrial robot through integrating data interaction between enterprise application and a plurality of subsystems, integrating various different systems and data, providing more comprehensive and accurate data support for predictive maintenance, and further effectively improving the efficiency and the comprehensiveness of the predictive maintenance on the industrial robot.

Additional advantages, objects, and features of the application will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the application. The objectives and other advantages of the application may be realized and attained by the structure particularly pointed out in the written description and drawings.

It will be appreciated by those skilled in the art that the objects and advantages that can be achieved with the present application are not limited to the above-detailed description, and that the above and other objects that can be achieved with the present application will be more clearly understood from the following detailed description.

Drawings

The accompanying drawings are included to provide a further understanding of the application, and are incorporated in and constitute a part of this application. The components in the figures are not necessarily to scale, emphasis instead being placed upon illustrating the principles of the application. Corresponding parts in the drawings may be exaggerated, i.e. made larger relative to other parts in an exemplary device actually manufactured according to the present application, for convenience in showing and describing some parts of the present application. In the drawings:

FIG. 1 is a schematic diagram of a first architecture of an industrial robot predictive maintenance system based on enterprise application integration in one embodiment of the present application.

FIG. 2 is a schematic diagram of a second architecture of an enterprise application integration based industrial robot predictive maintenance system in accordance with one embodiment of the present application.

FIG. 3 is a schematic diagram of a third architecture of an industrial robot predictive maintenance system based on enterprise application integration in an embodiment of the present application.

Detailed Description

For the purposes of making the objects, technical solutions and advantages of the present application more apparent, the present application will be described in further detail with reference to the embodiments and the accompanying drawings. The exemplary embodiments of the present application and their descriptions are used herein to explain the present application, but are not intended to be limiting of the present application.

It should be noted here that, in order to avoid obscuring the present application due to unnecessary details, only structures and/or processing steps closely related to the solution according to the present application are shown in the drawings, while other details not greatly related to the present application are omitted.

It should be emphasized that the term "comprises/comprising" when used herein is taken to specify the presence of stated features, elements, steps or components, but does not preclude the presence or addition of one or more other features, elements, steps or components.

It is also noted herein that the term "coupled" may refer to not only a direct connection, but also an indirect connection in which an intermediate is present, unless otherwise specified.

Hereinafter, embodiments of the present application will be described with reference to the drawings. In the drawings, the same reference numerals represent the same or similar components, or the same or similar steps.

With the development of traffic and the release of new products, the more and more complex the traffic flow, the more and more network elements are involved in the flow, the more compact the connection between different network elements, the more complex the network structure, the more and more the scale is enlarged, the faster the network internet protocol (Internet Protocol, hereinafter referred to as IP) process, and the more and more the new technology is adopted and integrated. However, the fault maintenance is still performed in a worker mode, and the quality of the maintenance work is still mainly determined by the experience of maintenance personnel. Reducing the overall equipment downtime is a key performance index for equipment maintenance, and meanwhile, the application of the industry big data analysis technology in equipment state prediction is successful in other industries, so that the aim is achieved, and the equipment availability can be remarkably improved.

In carrying out the present application, the inventors have found that the prior art has at least the following problems: maintenance personnel are not enough, and work efficiency needs to be further improved. With the rapid development of services and networks, the maintenance workload is continuously increased, and according to the requirement of intensive maintenance, maintenance personnel cannot be increased synchronously, and the knowledge of maintenance personnel with abundant prior experience cannot be rapidly converted into skills which other maintenance personnel can rapidly master under the prior mechanism, so how to further improve the maintenance efficiency is a great problem at present.

In one or more embodiments of the application examples herein, predictive maintenance (Predictive Maintenance, pdM) is maintenance performed continuously or intermittently, as determined by observed conditions, to monitor, diagnose, or predict a condition indicator of an organization, system, or component. The results of such maintenance may indicate current and future functional capabilities or the nature and schedule of scheduled maintenance.

Specifically, predictive maintenance is maintenance based on a state, and when an industrial robot is running, periodic (or continuous) state monitoring and fault diagnosis are performed on main (or required) parts of the industrial robot, the state of equipment is determined, the future development trend of the state of the equipment is predicted, a predictive maintenance plan is formulated in advance according to the state development trend of the equipment and possible fault modes, and the time, content, mode and necessary technology and material support for repairing the machine are determined. Predictive maintenance integrates backup state monitoring, fault diagnosis, fault (state) prediction, maintenance decision support and maintenance activities.

In order to improve the efficiency and the comprehensiveness of predictive maintenance for an industrial robot and to provide more reliable failure prediction results for maintenance personnel and design a complete closed-loop management manner for the predictive maintenance manner of the industrial robot to support a complex business scenario where the industrial robot is located, an embodiment of the present application provides an industrial robot predictive maintenance system based on enterprise application integration, see fig. 1, where the industrial robot predictive maintenance system based on enterprise application integration specifically includes the following contents:

The integrated application subsystem 10 is used for respectively acquiring monitoring data of each industrial robot and industrial robot fault prediction tool data in an automobile manufacturing site from different types of enterprise applications corresponding to automobile manufacturing enterprises.

And the service subsystem 20 is in communication connection with the integrated application subsystem, and is used for storing the monitoring data acquired by the integrated application subsystem and generating an industrial robot fault prediction result aiming at the automobile manufacturing site based on the industrial robot fault prediction tool data and the monitoring data.

And the client subsystem 30 is in communication connection with the service subsystem, and is used for acquiring the industrial robot fault prediction result from the service subsystem and displaying the industrial robot fault prediction result.

In one or more embodiments of the present application, the industrial robot is an articulated manipulator or a multi-degree-of-freedom machine device widely used in the industrial field, has a certain automaticity, and can implement various industrial processing and manufacturing functions by means of self power energy and control capability. Correspondingly, the industrial robot data refer to data related to self parameters, operation processes, histories and the like of the industrial robot, and the monitoring data of the industrial robot can refer to process data such as operation data, state information and the like in the industrial robot data.

The operation data and the state information in the monitoring data of the industrial robot can reflect the operation state of the robot equipment, including parameters such as current, voltage, power, temperature, speed and the like, and can be used for evaluating the performance and the health condition of the equipment; sensor data may also be included: such data is derived from sensors on the robotic device, such as photoelectric sensors, pressure sensors, acceleration sensors, etc., to sense the environment and physical quantities surrounding the device and feed it back to the control system for processing. In addition, the operation data and the state information in the monitoring data of the industrial robot may further include: image data: such data from visual sensors on robotic devices, such as industrial cameras, lidar, etc., may be used to perform image recognition, object detection, three-dimensional modeling, etc. The operation data and the state information in the monitoring data of the industrial robot may further include sound data: such data is from sound sensors such as microphones on robotic devices and can be used for tasks such as sound recognition, noise monitoring, fault diagnosis, etc.

In the integrated application terminal system 10, the integrated application terminal system 10 can collect monitoring data of all types of industrial robots in an automobile manufacturing site in a batch processing mode, so that timeliness and effectiveness of collecting monitoring data of the industrial robots can be effectively improved while comprehensiveness of monitoring data collection of the industrial robots is guaranteed, reliable data base can be provided for predictive maintenance of a subsequent big data processing platform, efficiency and reliability of predictive maintenance of the industrial robots can be improved, and convenience of predictive maintenance of the industrial robots based on enterprise application integration is improved.

In the client sub-system 30, the maintenance data generated by the predictive maintenance process of the industrial robot in the automobile manufacturing site can be uploaded by the user through the client sub-system 30, or can be uploaded by the field data recording device of the industrial robot in the automobile manufacturing site, after the uploaded maintenance data is obtained, the identification of the maintained component, the original error information of the component and the updated data of the maintained component can be extracted from the maintenance data, and then the knowledge map of the industrial robot is updated by utilizing the updated data of the component, for example, the "overcurrent" in the related entity "motor" and the "overcurrent" of the original entity "component A1" is modified to be "normal running". The industrial robot data platform can also iteratively update the industrial robot fault prediction model by taking the identification of the component and the original error reporting information of the component as new historical training data and labels.

According to the industrial robot predictive maintenance system based on enterprise application integration, predictive maintenance for an industrial robot is achieved through a data processing process between an enterprise application end, a server end and a client end, the industrial robot predictive maintenance system is an effective mode for achieving enterprise application integration, efficiency and comprehensiveness of the industrial robot predictive maintenance can be improved, various different systems and data can be integrated through the enterprise application integration, comprehensiveness and accuracy of the data are improved, and more comprehensive and accurate data support is provided for predictive maintenance. Meanwhile, the maintenance efficiency and accuracy can be improved by application integration, and the operation efficiency and competitiveness of enterprises are improved.

In order to further improve the effectiveness and reliability of collecting various types of industrial robot data in an automobile manufacturing site, in an industrial robot predictive maintenance system based on enterprise application integration provided in the embodiment of the present application, referring to fig. 2, an integrated application terminal system 10 in the industrial robot predictive maintenance system based on enterprise application integration specifically includes the following contents:

the industrial internet of things platform 11 is configured to monitor each industrial robot in the automotive manufacturing site in real time based on the industrial internet of things device, so as to collect monitoring data of each industrial robot, where the monitoring data includes: operational data and status information.

Wherein, industry thing networking equipment specifically can include: different types of sensors, industrial intelligent gateways and the like, wherein the sensors specifically can comprise: photoelectric sensors, pressure sensors, acceleration sensors, vision sensors, microphones, and the like.

In the process of carrying out industrial robot monitoring data batch processing collection by the industrial internet of things platform 11, the industrial robot monitoring data batch processing collection can be realized through a data collector, a protocol converter, a transfer server and a timer, wherein the data collector is connected with target equipment through an industrial control network, the protocol converter is respectively connected with the data collector and the transfer server, and the timer is arranged in the transfer server. According to the method, the batch processing data processing device of the automobile manufacturing equipment can automatically collect working data, the collected working data of different protocols can be converted into working data in a unified protocol format through the protocol converter, meanwhile, the data can be uploaded to the data processing system in batches through the transfer server for processing, the purpose of fully automatically collecting the working data of the target detection system is achieved, and the technical problems that the existing data collecting and analyzing device cannot collect the data systematically and cannot collect and process the data in batches are solved.

The failure prediction tool module 12 is used for storing industrial robot failure prediction tool data for predicting failure of the industrial robot.

In order to further improve the effectiveness and reliability of collecting various types of industrial robot data in an automobile manufacturing site, in an industrial robot predictive maintenance system based on enterprise application integration provided in the embodiment of the present application, referring to fig. 3, a fault prediction tool module 12 in the industrial robot predictive maintenance system based on enterprise application integration specifically includes the following contents:

the knowledge base and rule unit 121 is configured to generate a predictive maintenance rule for the industrial robot according to the monitoring data of the industrial robot acquired in advance based on a preset knowledge base and rule engine.

The artificial intelligence unit 122 is configured to train a preset machine learning model by using the previously acquired historical monitoring data of the industrial robot and the corresponding historical failure prediction result, so as to generate an industrial robot failure prediction model for performing failure prediction on the monitoring data of the industrial robot.

In the knowledge base and rule unit 121, one example of the preset knowledge base and rule engine may be an industrial robot knowledge graph, which is a knowledge graph for storing relationships between entities of an industrial robot and different entities. In one or more embodiments of the present application, the entity does not refer to each component of the industrial robot alone, but includes failure modes (also referred to as phenomena) of each component, fault handling measures (also referred to as emergency measures), and the like, and may include failure or potential failure causes of each component.

Specifically, the knowledge base and rule unit 121 may first obtain an entity recognition result corresponding to the monitoring of the industrial robot, extract a relationship between different entities from the monitoring data of the industrial robot according to the entity recognition result, and then construct or update an industrial robot knowledge graph according to the entity recognition result and the relationship between different entities.

In one specific example, the monitoring of the industrial robot is as follows: the step of extracting the corresponding entity based on the preset entity extraction rule at least comprises the following steps of: "part a", "motor" and "overcurrent", and the entity "part a" and entity "motor" of the three belong to the entity types: "parts"; the entity type to which the entity "overcurrent" belongs is: "phenomenon". Thus, if the preset query condition is "component a", after the industrial robot knowledge graph is updated, the entity "component a" and the other entities "motor" and "overcurrent" that have connection lines (association relationships) with the "component a" can be searched for, so as to form a fault prediction result for expressing the entity corresponding to the query condition and the other entities associated with the entity.

In the artificial intelligence unit 122, the industrial robot fault prediction model may be a simulation model, an artificial intelligence model, or a probabilistic statistical model for different industrial robots, respectively, where a machine learning model may be preferred in order to improve the automation degree and the intelligence degree of the industrial robot fault prediction. Specifically, decision trees, neural networks and the like can be adopted as the industrial robot fault prediction model, and can be selected according to actual application requirements, and the method is not limited. So that the subsequent service subsystem 20 may first extract the feature vectors corresponding to the monitoring data of each industrial robot, aggregate the feature vectors, and input the aggregated vectors into a preset industrial robot fault prediction model, so that the industrial robot fault prediction model outputs a corresponding fault prediction result.

Specifically, the artificial intelligence unit 122 may obtain the historical monitoring data of each type of industrial robot collected in the automobile manufacturing site and the historical fault prediction results corresponding to each of the historical monitoring data, where the historical fault prediction results include the association relationship between the historical fault information and the position data of the affiliated component; constructing a fault severity level table of the corresponding relation between each error reporting information and each severity level; training an industrial robot fault prediction model by adopting each historical monitoring data and a corresponding historical fault prediction result, so that the industrial robot fault prediction model is used for outputting a corresponding fault prediction result according to the input industrial robot data, wherein the fault prediction result comprises: and the historical monitoring data respectively correspond to the error reporting information and the association relation between the position data of the part to which the error reporting information belongs.

That is, the knowledge base and rule unit 121 and the artificial intelligence unit 122 generate the failure prediction results for the industrial robots in the automobile manufacturing site by using the knowledge map and the industrial robot failure prediction model, and output the two failure prediction results, so that the reliability and the effectiveness of the failure prediction for the industrial robots can be effectively improved, more various and reliable data bases can be provided for maintenance personnel, the comprehensiveness and the reliability of the predictive maintenance of the industrial robots can be further improved, and the comprehensiveness and the effectiveness of the predictive maintenance of the industrial robots based on enterprise application integration can be improved.

In order to further improve the effectiveness and reliability of fault prediction of each type of industrial robot in the automotive manufacturing site, in the industrial robot predictive maintenance system based on enterprise application integration provided in the embodiment of the present application, referring to fig. 2 and fig. 3, the service terminal system 20 in the industrial robot predictive maintenance system based on enterprise application integration specifically includes the following contents:

the database 21 is in communication connection with the industrial internet of things platform 11 and is used for storing monitoring data of each industrial robot acquired by the industrial internet of things platform;

The big data processing platform 22 is respectively in communication connection with the database 21, the knowledge base and rule unit 121 and the artificial intelligence unit 122, and is configured to perform fault prediction on the monitoring data of each industrial robot extracted from the database by using the predictive maintenance rules obtained from the knowledge base and rule unit and/or the industrial robot fault prediction model obtained from the artificial intelligence unit, so as to obtain a current industrial robot fault prediction result for the automobile manufacturing site.

In one or more embodiments of the present application, the big data processing platform 22 refers to a data platform for processing monitoring data of an industrial robot, where the data platform may process received mass data generated on-site based on the industrial internet of things and big data technology, so as to provide a basis for further analysis or monitoring. The general architecture consists of a perception layer, a transmission layer, a platform layer and a service layer, and corresponds to data acquisition, transmission, platform visualization and specific service providing respectively.

In order to further improve the efficiency and reliability of the predictive maintenance of each type of industrial robot in the automotive manufacturing site, in the industrial robot predictive maintenance system based on the enterprise application integration provided in the embodiment of the present application, referring to fig. 2, the client subsystem 30 in the industrial robot predictive maintenance system based on the enterprise application integration specifically includes the following contents:

And the mobile terminal application 31 is respectively connected with the database and the big data processing platform in a communication way and is used for acquiring and displaying the monitoring data of each industrial robot and the industrial robot fault prediction result aiming at the automobile manufacturing site in real time.

The desktop application 32 is respectively in communication connection with the database and the big data processing platform, and is used for acquiring monitoring data of each industrial robot and industrial robot fault prediction results aiming at the automobile manufacturing site in real time, generating corresponding charts according to the monitoring data of each industrial robot, displaying the charts, generating corresponding early warning information according to the industrial robot fault prediction results and displaying the early warning information.

The mobile terminal application 31 may be provided in an industrial wireless PAD (tablet) terminal. By developing the application program of the mobile terminal, the user can monitor the running condition of the robot at any time and any place. The method can realize real-time monitoring and alarming of the robot in the form of push messages.

In order to further improve the efficiency and reliability of the predictive maintenance of each type of industrial robot in the automotive manufacturing site, in the industrial robot predictive maintenance system based on the enterprise application integration provided in the embodiment of the present application, referring to fig. 3, the service subsystem 20 in the industrial robot predictive maintenance system based on the enterprise application integration further specifically includes the following contents:

The access service module 23 is respectively in communication connection with the big data processing platform, the enterprise application of each type corresponding to the automobile manufacturing enterprise and the client subsystem, and is used for sending the industrial robot fault prediction result generated by the big data processing platform to at least one of the enterprise application, the mobile terminal application and the desktop application of each type;

correspondingly, the mobile terminal application 31 and the desktop application 32 are respectively connected with the database 21 and the big data processing platform 22 based on the communication between the access service module 23 and the big data processing platform 22.

The access service module 23 may employ Web services and APIs: and through Web services and APIs, the data and functions of the robot predictive maintenance are opened to other application programs or third-party application programs in the enterprise for use, so that integration and sharing among systems are realized.

The security management module 24 is respectively in communication connection with the access service module 23 and the client subsystem 30, and is configured to perform identity verification and data access permission verification on the client subsystem when the client subsystem requests to access the database and the big data processing platform through the access service module, and establish access connection between the client subsystem and the database and the big data processing platform through the access service module after the identity verification and the data access permission verification are passed.

the distributed data module 25 is respectively in communication connection with the big data processing platform 22 and the access service module 23, and is used for performing distributed storage on the industrial robot fault prediction result in the big data processing platform, and sending the industrial robot fault prediction result stored in a distributed manner to the client subsystem through the access service module; the distributed data module is further used for receiving maintenance record data for the industrial robot sent by the client subsystem through the access service module and performing distributed storage on the maintenance record data.

In order to further improve the comprehensiveness and reliability of collecting various types of industrial robot data in an automotive manufacturing site, in an industrial robot predictive maintenance system based on enterprise application integration provided in the embodiments of the present application, referring to fig. 3, the integrated application subsystem 10 in the industrial robot predictive maintenance system based on enterprise application integration further specifically includes the following contents:

a resource management platform 13, configured to store non-monitoring data of each of the industrial robots, where the non-monitoring data includes: operating environment data, maintenance record data, and spare part inventory data for the industrial robot.

In order to further explain the scheme, the application further provides a specific application example of the industrial robot predictive maintenance method based on the enterprise application integration, which is realized by applying the industrial robot predictive maintenance system based on the enterprise application integration, is an effective way for realizing the enterprise application integration, can improve the efficiency and the comprehensiveness of the industrial robot predictive maintenance, can integrate various different systems and data by realizing the enterprise application integration, improves the comprehensiveness and the accuracy of the data, and provides more comprehensive and accurate data support for the predictive maintenance. Meanwhile, the maintenance efficiency and accuracy can be improved by application integration, and the operation efficiency and competitiveness of enterprises are improved. The following specific implementation modes are as follows:

a) Establishing a data integration platform: and integrating and processing data from different sources to construct a complete data integration platform, so as to provide more comprehensive and accurate data support for predictive maintenance.

b) Establishing a business process integration platform: and integrating and optimizing the service flow of the industrial robot predictive maintenance, establishing a set of comprehensive service flow integration platform, and improving the maintenance efficiency and accuracy.

c) Application integration: various application systems and tools are integrated on a platform, so that more comprehensive and accurate data analysis and prediction support is provided for predictive maintenance of the industrial robot.

d) And (3) equipment integration: the industrial robot equipment is integrated with other equipment, data sharing and interaction between the equipment are realized through the technology of the Internet of things, and more comprehensive and accurate data support is provided for predictive maintenance.

e) Personnel integration: different functional departments and personnel are integrated to cooperatively complete the task of predictive maintenance, so that the maintenance efficiency and accuracy are improved.

To achieve predictive maintenance for industrial robots, operation data of the robots need to be acquired, analyzed and processed through data processing procedures among enterprise application terminals, server terminals and client terminals, so as to conduct prediction and diagnosis. Predictive maintenance of the industrial robot can be achieved through the data processing process among the enterprise application end, the server end and the client end, reliability and stability of the robot are improved, and maintenance cost and shutdown loss of an enterprise are reduced.

The following is the main process of the industrial robot predictive maintenance method based on enterprise application integration, which is realized by applying the industrial robot predictive maintenance system based on enterprise application integration, and comprises the following steps:

step one, data acquisition: the enterprise application end obtains monitoring data of the industrial robot through the data acquisition system, wherein the monitoring data can comprise various data such as operation data, model data, maintenance data and process data of the robot.

Step two, data transmission: and transmitting the collected data to a server side at each layer of the generic model through MSB (manufacturing service bus), and monitoring the running condition of the industrial robot in real time.

Step three, data processing: the server side processes and analyzes the collected data through preprocessing, cleaning and other technologies, extracts characteristics and key indexes, establishes a machine learning model, and predicts and diagnoses the running state of the robot.

Step four, data visualization: through a data visualization technology, the processed data are presented in the forms of charts, curves and the like, so that engineers and technicians can more intuitively know the running state and abnormal condition of the robot, and the method is convenient and quick to decide.

Step five, alarming and early warning: the client monitors the running state of the robot in real time by receiving the data sent by the server, and performs early warning and alarming according to the set rules and models so as to timely treat the faults and abnormal conditions of the robot.

Enterprise application(s) (integrated application subsystem 10 as described above)

The enterprise application end refers to an application program used for managing predictive maintenance of the industrial robot in an enterprise, and the robot can be managed and monitored more carefully through the enterprise application end. The implementation mode of the enterprise application end can be selected and combined according to specific requirements and conditions in an enterprise, and the predictive maintenance of the robot can be comprehensively managed and monitored through the implementation of the enterprise application end, so that the maintenance efficiency and quality are improved.

The following is a specific implementation manner of the enterprise application end:

a) Industrial internet of things platform, MSB (manufacturing service bus) above: real-time monitoring and early warning of the robot can be realized through the industrial Internet of things platform, operation data and state information of the robot are collected through the sensor and the Internet of things equipment, analysis and processing are carried out, and the efficiency and the accuracy of predictive maintenance are improved.

b) Data warehouse and data analysis platform: by establishing a data warehouse and a data analysis platform, the operation data of the robot can be stored and processed, and the faults and the anomalies of the robot can be predicted and early-warned by the technologies of data mining, machine learning and the like.

c) A resource management platform: through the resource management platform, the running environment, maintenance records, spare part inventory and the like of the robot can be managed and maintained, and the reliability and stability of the robot are improved.

d) Knowledge base and rules engine: by establishing a knowledge base and a rule engine, the operation data and the history of the robot can be analyzed and summarized, and predictive maintenance rules suitable for the robot are formulated through the technologies of an expert system and the like, so that the maintenance efficiency and accuracy are improved.

e) Artificial intelligence and machine learning algorithms: through artificial intelligence and a machine learning algorithm, operation data of the robot can be analyzed and processed, faults and anomalies of the robot are predicted, and reliability and stability of the robot are improved.

(II) server side (the aforementioned service terminal System 20)

The server side is a server system for processing, storing, analyzing and managing the predictive maintenance data of the industrial robot, and can realize the automatic management and the real-time monitoring of the predictive maintenance of the robot. The implementation mode of the server side can be selected and combined according to the specific requirements and conditions in an enterprise, and the automation management and the real-time monitoring of the predictive maintenance of the robot can be realized through the implementation of the server side, so that the maintenance efficiency and the maintenance quality are improved.

The following is a specific implementation manner of the server side:

a) Database system: the running data and the state information of the robot are stored and managed through the database system, so that the running state of the robot is monitored and maintained in real time.

b) Big data processing platform: the operation data of the robot is processed and analyzed through the big data processing platform, valuable information is extracted, and data support and decision basis are provided for predictive maintenance of the robot.

c) Cloud platform: and through resources and services provided by the cloud platform, the automatic management and real-time monitoring of the predictive maintenance of the robot are realized.

d) Distributed system: the distributed storage and processing of the predictive maintenance data of the robot are realized through the distributed system, and the stability and performance of the system are improved.

e) Web (global wide area network) services and APIs (application programming interfaces): and through Web services and APIs, the data and functions of the robot predictive maintenance are opened to other application programs or third-party application programs in the enterprise for use, so that integration and sharing among systems are realized.

f) A security management system: the safety protection and access control of the predictive maintenance data of the robot are realized through the safety management system, so that the running data of the robot cannot be accessed or tampered maliciously.

g) Intelligent algorithm: and the operation data of the robot is processed and analyzed through an intelligent algorithm, faults and anomalies of the robot are predicted, and the reliability and stability of the robot are improved.

(II) client (client subsystem 30 described above)

The client is an important component in the industrial robot predictive maintenance system, can help a user to know the running state and abnormal condition of the robot in time, and can perform early warning and alarming according to set rules and models to process the faults and abnormal conditions of the robot in time.

The following is a specific implementation of the client:

a) Mobile terminal application: in combination with the improvement point 5, the PAD terminal (intelligent mobile device) based on industrial wireless, a user can monitor the running condition of the robot anytime and anywhere by developing the application program of the mobile terminal. The method can realize real-time monitoring and alarming of the robot in the form of push messages.

b) Desktop application: through Web application, the running data of the robot can be presented in the forms of charts, curves and the like, so that the real-time monitoring and early warning of the robot are realized. The user can access the Web pages through the browser, and view the running state and abnormal conditions of the robot at any time.

The PAD terminal (intelligent mobile device) based on industrial wireless can be used for realizing predictive maintenance on the industrial robot, so that the maintenance efficiency and quality are improved, meanwhile, the intelligent mobile device based on industrial wireless can enable maintenance personnel to monitor and maintain the robot anytime and anywhere, time and space limitation of a traditional maintenance method is avoided, and the maintenance method is flexible and efficient. The method can be realized by the following innovative method:

step one, a wireless sensor network: wireless sensor networks are deployed to collect real-time operational data of robots, such as temperature, vibration, sound, current, voltage, etc., which are used to predict faults and anomalies of the robots.

Step two, intelligent algorithm based on cloud computing: and analyzing and processing the real-time operation data of the robot by using the high-performance computing capacity and the big data processing capacity of cloud computing and adopting an intelligent algorithm, so as to realize predictive maintenance of the robot.

Step three, moving the maintenance device: and developing a maintenance application program based on the mobile terminal equipment in a software layer, and applying a PAD terminal (intelligent mobile device) based on industrial wireless. And at the hardware level, a temperature and humidity detection sensor (infrared) and a vibration sensor (magnetic force is adsorbed on equipment) are integrated on the mobile maintenance device. The robot is monitored and maintained in real time through the mobile device, so that the convenience and timeliness of operation are improved, and the maintenance efficiency and quality are guaranteed.

Step four, artificial intelligence technology: and analyzing and processing the real-time operation data of the robot by utilizing an artificial intelligence technology, automatically identifying faults and anomalies of the robot, and providing corresponding predictive maintenance suggestions.

(IV) application project overview of industrial robot predictive maintenance method in certain automobile manufacturing enterprises based on enterprise application integration

The application project of the application enables big data thinking for the first time to establish a data model; and (3) running an algorithm on the big data platform, performing data mining and analysis, and supporting a machine learning mode. The data is stored on a transfer server through extraction and conversion of machine data and log files; cleaning and processing DATA through a DATA LAKE (DATA LAKE) of the big DATA platform to form an intermediate table which can be identified by R language; the final prediction result is output and fed back to a field maintenance engineer through modeling and analysis of the R language; and the maintenance engineer returns the prediction result to the system accurately according to the prediction result to the field maintenance, and the system continuously optimizes the model and algorithm in a machine learning mode, so that the prediction accuracy is improved. The predictive maintenance project realizes the innovation of technology through the innovation practice of a big data platform, and also enables automobile manufacturing enterprises to start the era of big data analysis. The concrete explanation is as follows:

1. Maintenance flow optimization

The traditional mode mainly comprises regular inspection, emergency maintenance and preventive maintenance, and is incapable of predicting faults in advance and production stopping risks caused by the emergency maintenance. Preventative maintenance is through all activities performed by systematic inspection, equipment testing, and replacement of equipment to prevent malfunction from occurring, keeping it in a prescribed state. It may include adjustment, lubrication, periodic inspection, and the like. Mainly for products whose consequences of failure can jeopardize safety and affect task completion, or result in large economic losses. The purpose of preventive maintenance is to reduce the probability of product failure or prevent functional degradation. It performs maintenance at predetermined time intervals or on prescribed criteria, typically including maintenance, operator monitoring, usage inspection, function detection, timed repair, timed discard, and like types of maintenance work.

Predictive maintenance is state-based maintenance in which, during operation of the machine, its primary (or required) location is subjected to periodic (or continuous) state monitoring and fault diagnosis, the state of the equipment is determined, the future development trend of the state of the equipment is predicted, and a predictive maintenance plan is formulated in advance according to the state development trend of the equipment and the possible fault modes to determine the time, content, manner and necessary technical and material support that the machine should repair. The predictive maintenance integrated with state monitoring, fault diagnosis, fault (state) prediction, maintenance decision support and maintenance activities is an emerging maintenance mode

According to four stages of maintenance development, the project can be expected to achieve the goal of predictive maintenance stage after being put on line, namely, predictive maintenance is realized through analysis of big data. The maintenance flow is also optimized to be a mode that maintenance personnel receives early warning of machine faults and cooperates with the inspection by the original regular inspection and emergency maintenance, and after a certain time of verification and model optimization, future expectations can be completely realized by the early warning mode, spare parts are replaced before the faults occur, so that the production stopping risk is greatly reduced, and the production efficiency is improved.

The maintenance flow after the project is on line is as follows: the maintenance engineer logs in the working platform before working every day, checks real-time fault prediction results, checks working areas and machines with early warning, and makes a maintenance and inspection plan of the same day, if early warning occurs in the working of the same day, corresponding information is sent to the maintenance engineer, the maintenance engineer performs emergency treatment, and the maintenance engineer repairs the equipment before the equipment fails as much as possible.

2. ESB-based predictive maintenance method

In order to integrate application integration technology and improve operation efficiency, the application example builds a data platform MSB (Manufacture Service Bus, manufacturing service bus) based on an MQTT (message queue telemetry transport) protocol, and the MSB can provide a universal connection platform for field process equipment data so as to optimize a service mode.

A data platform based on an MQTT protocol is built, and field level devices send real-time data through MSB (manufacturing bus). The field level device sends real-time data to the MSB via the MQTT protocol. Under the architecture, thousands of industrial robots, process equipment, sensors and other data are collected into enterprise cloud, and aggregation of data, intelligent display of data, advanced prediction of faults, intelligent terminal data support, provider cloud application and auxiliary management decision making are realized through big data processing and analysis technology. The MSB big data platform becomes an infrastructure for digitalization of automobile manufacturing enterprises. The front end can directly monitor real-time data through a data billboard to acquire equipment state information, and can also call historical data of a large data center to conduct intelligent algorithm research and development. And the cloud platform is cooperatively interconnected with a third party cloud platform in the future, so that higher data value is driven.

The automobile manufacturing enterprise joint equipment manufacturer upgrades the equipment function so that the equipment has the capability of sending MQTT information. After the performance is stable through multiple rounds of tests, the upgrade program is promoted and installed on a large scale, and a foundation is laid for data collection. On the other hand, since the use of equipment is numerous, for the inability to develop MQTT equipment, automobile manufacturers have adopted information transformation schemes. And the local data of the equipment controller is acquired by using a programming tool Node-Red data flow tool based on the flow, and then the MQTT message is sent out, so that the information conversion is successfully realized.

The equipment used by the automobile manufacturing enterprises is various, and the equipment transmits massive various logs and alarm information, so that the traditional relational database cannot support the time-dependent analysis of the log information. The Elastomer Search (ES) can be used as a large distributed cluster (hundreds of servers) technology to process PB-level data. The method combines full text retrieval, data analysis and distributed technology, can search and analyze mass data in near real-time (NRT), and controls the response time consumption at the second level. The automobile manufacturing enterprises adopt the ES as a real-time database, and Kibana under the ES architecture is used as a visualization tool for front-end real-time billboard development. The quantity of connected equipment reaches 4000+, and various equipment monitoring visual signboards are 70, so that powerful support is provided for intelligent management and operation and maintenance of equipment.

Taking an industrial robot state monitoring system as an example, the robot is connected with a manufacturing service bus platform (MSB) of an automobile manufacturing enterprise through an MQTT protocol, and data such as current state, asset information and the like are uploaded in real time. Through customizing the state monitoring information of the developed robot of the automobile manufacturing enterprise, a worker can see the current state of a certain robot after selecting the robot, for example, the real-time state information such as the running program, running speed, CPU (central processing unit) load and the like; at the same time, the asset information of robot IP (Internet protocol) address, serial number, total running time, system version, robot program package version and robot equipment type can be seen. Through the analysis and processing of the alarm information of the robot in the past half year, the system can also score the whole health state of the robot automatically and display the whole health state in a radar chart form, so that the whole state of the equipment can be displayed intuitively.

The main implementation scheme of the project is to adopt a big data platform to carry out modeling analysis on various state data and logs of the machine and push early warning to appointed maintenance personnel. The first step of the project is to collect data of robots and other devices on site, including device data of library cards (KUKA) and Bosch (Bosch) and log files, convert the original data format into CSV (comma separated value file format) format through scripts and software, and transmit the CSV format to a transfer server; then, the CSV file is transmitted to a big data platform through a timed task; then the big data platform converts the data format into a data warehouse table (Hive table) for storage and conversion, and carries out modeling analysis through R language to form a final prediction result; and finally, pushing the prediction result to a UI (software interface) of a foreground, and returning to a big data platform to correct data and optimize an algorithm.

3. Predictive maintenance flow of enterprise management series software SAP (application and data processing) based on German development

At present, big data model analysis is already applied to actual maintenance scenes, and plays a positive role in improving equipment availability, shortening maintenance working hours and the like.

After the development, deployment and initial verification of the predictive maintenance project are completed, the organization plans the online preparation and test operation, compiles a predictive maintenance test operation scheme, and makes clear the system test operation time, data preparation, business preparation, problem processing flow after online and key users and responsibility division workers of online support. All relevant maintenance personnel are trained for use prior to online. After the test run starts, the problems found during the online test run are recorded, including whether the fault prediction is timely and accurate, the optimization opinion and the like, and the project group continues the optimization algorithm according to the feedback opinion, so that the problems are solved, and the fault prediction timeliness and accuracy are improved.

And selecting a part of the project group in a certain assembly welding workshop for spot welding and a gluing robot for verification. Taking maintenance of the gumming machine as an example, real-time data is analyzed on the basis of big data modeling, early warning is carried out in advance, maintenance is assisted, and shutdown frequency is reduced.

The electric welding faults of the cases can be predicted 8 hours in advance with the accuracy of 80%, the glue leakage of the glue spreader of the case 3 can be predicted the faults 24 hours in advance with the accuracy of 78%, the bus burnout of the other case is low in prediction efficiency of the existing model due to the limited data quantity and the insufficient quantity of faults, and a more effective model is still sought at present, but the expected effect can be achieved by the current achievement in view of the prospective and risk of the innovative project.

On the basis of simulation by utilizing big data analysis and an algorithm model, the predictive maintenance project realizes real-time prediction of faults, helps maintenance personnel to early warn the possible positions and time of the faults in advance, greatly improves maintenance efficiency, improves equipment availability and product quality, reduces maintenance working hours, and finally realizes improvement of production efficiency.

The embodiments of the present application also provide an electronic device (i.e., an electronic device) that may include a processor, a memory, a receiver, and a transmitter, where the processor is configured to execute the industrial robot predictive maintenance system based on enterprise application integration mentioned in the foregoing embodiments, and the processor and the memory may be connected by a bus or other means, for example, by a bus connection. The receiver may be connected to the processor, memory, by wire or wirelessly. The electronic device may receive real-time motion data from a sensor in the wireless multimedia sensor network and receive an original video sequence from the video acquisition device.

The processor may be a central processing unit (Central Processing Unit, CPU). The processor may also be any other general purpose processor, digital signal processor (Digital Signal Processor, DSP), application specific integrated circuit (Application Specific Integrated Circuit, ASIC), field programmable gate array (Field-ProgrammableGate Array, FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof.

The memory, as a non-transitory computer readable storage medium, may be used to store non-transitory software programs, non-transitory computer executable programs, and modules, such as program instructions/modules corresponding to the industrial robot predictive maintenance system based on enterprise application integration in the embodiments of the present application. The processor executes the various functional applications and data processing of the processor by running non-transitory software programs, instructions and modules stored in the memory, i.e., implementing the enterprise application integration-based industrial robot predictive maintenance system in the above-described method embodiments.

The memory may include a memory program area and a memory data area, wherein the memory program area may store an operating system, at least one application program required for a function; the storage data area may store data created by the processor, etc. In addition, the memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory may optionally include memory located remotely from the processor, the remote memory being connectable to the processor through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The one or more modules are stored in the memory that, when executed by the processor, perform the enterprise application integration-based industrial robot predictive maintenance system of an embodiment.

In some embodiments of the present application, the user equipment may include a processor, a memory, and a transceiver unit, where the transceiver unit may include a receiver and a transmitter, and the processor, the memory, the receiver, and the transmitter may be connected by a bus system, the memory storing computer instructions, and the processor executing the computer instructions stored in the memory to control the transceiver unit to transmit and receive signals.

As an implementation manner, the functions of the receiver and the transmitter in the present application may be considered to be implemented by a transceiver circuit or a dedicated chip for transceiver, and the processor may be considered to be implemented by a dedicated processing chip, a processing circuit or a general-purpose chip.

As another implementation manner, a manner of using a general-purpose computer may be considered to implement the server provided in the embodiments of the present application. I.e. program code for implementing the functions of the processor, the receiver and the transmitter are stored in the memory, and the general purpose processor implements the functions of the processor, the receiver and the transmitter by executing the code in the memory.

Embodiments of the present application also provide a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the aforementioned enterprise application integration-based industrial robot predictive maintenance system. The computer readable storage medium may be a tangible storage medium such as Random Access Memory (RAM), memory, read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, floppy disks, hard disk, a removable memory disk, a CD-ROM, or any other form of storage medium known in the art.

Those of ordinary skill in the art will appreciate that the various illustrative components, systems, and methods described in connection with the embodiments disclosed herein can be implemented as hardware, software, or a combination of both. The particular implementation is hardware or software dependent on the specific application of the solution and the design constraints. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application. When implemented in hardware, it may be, for example, an electronic circuit, an Application Specific Integrated Circuit (ASIC), suitable firmware, a plug-in, a function card, or the like. When implemented in software, the elements of the present application are the programs or code segments used to perform the required tasks. The program or code segments may be stored in a machine readable medium or transmitted over transmission media or communication links by a data signal carried in a carrier wave.

It should be clear that the present application is not limited to the particular arrangements and processes described above and illustrated in the drawings. For the sake of brevity, a detailed description of known methods is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method processes of the present application are not limited to the specific steps described and illustrated, and those skilled in the art can make various changes, modifications, and additions, or change the order between steps, after appreciating the spirit of the present application.

The features described and/or illustrated in this application for one embodiment may be used in the same way or in a similar way in one or more other embodiments and/or in combination with or instead of the features of the other embodiments.

The foregoing description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and variations may be made to the embodiment of the present application by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. An industrial robot predictive maintenance system based on enterprise application integration, comprising:

the client terminal system is in communication connection with the service terminal system and is used for acquiring the industrial robot fault prediction result from the service terminal system and displaying the industrial robot fault prediction result;

the integrated application subsystem includes:

The fault prediction tool module is used for storing industrial robot fault prediction tool data for carrying out fault prediction on the industrial robot;

the fault prediction tool module includes:

the artificial intelligent unit is used for training a preset machine learning model by adopting the previously acquired historical monitoring data of the industrial robot and the corresponding historical fault prediction result so as to generate an industrial robot fault prediction model for performing fault prediction on the monitoring data of the industrial robot;

the service sub-system includes:

2. The enterprise application integration-based industrial robot predictive maintenance system of claim 1, wherein the client sub-system further comprises:

3. The enterprise application integration based industrial robot predictive maintenance system of claim 2, wherein the mobile terminal application is implemented in an industrial wireless PAD terminal.

4. The enterprise application integration-based industrial robot predictive maintenance system of claim 2, wherein the service sub-system further comprises:

5. The enterprise application integration-based industrial robot predictive maintenance system of claim 4, wherein the service sub-system further comprises:

6. The enterprise application integration-based industrial robot predictive maintenance system of claim 4, wherein the service sub-system further comprises:

7. The enterprise application integration-based industrial robot predictive maintenance system of claim 1, wherein the integrated application subsystem further comprises: