KR102334965B1 - Predictive maintenance system for efficient management of factory automation equipment and productivity improvement - Google Patents

Abstract

The present invention relates to a predictive maintenance system for efficient management of factory automation equipment and improvement of productivity. The present invention provides a predictive maintenance system (1) for efficient management and productivity improvement of factory automation equipment including 0), wireless AP (200), network (300) and mobile terminal (600), APDM equipment (100) ) is a device based on wired communication (serial, Ethernet) and wireless communication (Wi-Fi, LTE), and consists of a control unit, wired/wireless communication modem, and storage medium. It provides a program to check data through short-distance wireless communication with the mobile terminal 600, and stores data including operation, production, status, tool, change point, and alarm of the factory automation facility 10 into the database 130 ) may be characterized in that it is stored in
As a result, systemic management through predictive maintenance technology of APDM equipment can significantly reduce equipment downtime such as consumables management, equipment history management, and failure prediction, which can help improve product (product, parts) productivity and equipment lifespan. provides an effect.

Description

Predictive maintenance system for efficient management of factory automation equipment and productivity improvement

The present invention relates to a predictive maintenance system for efficient management and productivity improvement of factory automation facilities, and more specifically, to a systematic management through predictive maintenance technology of APDM equipment, such as consumables management, equipment history management, failure prediction, etc. It relates to a predictive maintenance system for efficient management of factory automation equipment and productivity improvement to help improve product (product, part) productivity and equipment life by significantly lowering downtime.

The management of factory automation equipment, which corresponds to the existing CNC (Computer Numerical Control) machine, has a structural problem in that it is highly dependent on the know-how of the operator (person in charge), and loss occurs due to negligence in management due to disorganization.

More specifically, most of the equipment connected to the factory automation equipment can monitor the equipment status in real time, but it is difficult to analyze the collected data of the module device provided by default.

In particular, users are highly fatigued from continuous inspection and response, and they tend to depend on the know-how of skilled workers, and it is difficult to analyze the cause immediately due to various causes of failure. As the downtime increases, the production rate decreases.

Accordingly, in the field of technology, technology development for predictive management by providing judgment on abnormalities by analyzing the data accumulated on factory automation facilities and deriving the causes of equipment failures and quality defects, etc. this is being requested

Republic of Korea Patent Application No. 10-2017-0021709 (2017.02.17) "SYSTEM FOR MONITORING SPOT EQUIPMENT USING CONTROL APPARATUS BASED ON WIRELESS COMMUNICATION AND METHOD THEREFOR" Republic of Korea Patent Application No. 10-2018-0027776 (2018.03.09) "Distributed data acquisition and distributed control command system for factory automation, and distributed data acquisition and distributed control command system for the same for factory automation, and Distributed data collection and distributed control method for the same)"

The present invention is to solve the above problems, and through systematic management through predictive maintenance technology of APDM equipment, significantly lowers equipment downtime such as consumables management, equipment history management, and failure prediction to improve productivity of products (products, parts) This is to provide a predictive maintenance system for efficient management of factory automation facilities and improvement of productivity to help the equipment life cycle.

In addition, the present invention relates to analysis information such as failure time of factory automation equipment, other failure types MTTR (Mean Time To Repair) / MTBF (Mean Time Between Failures), production efficiency, etc. Since it is possible to acquire comprehensively, it is to provide a predictive maintenance system for efficient management of factory automation facilities and productivity improvement to improve production quality and price competitiveness through predictive maintenance.

In addition, the present invention provides a predictive maintenance system of APDM equipment to the manager with a failure prediction and cause and result for the part that takes 1 to 2 weeks and financial loss has occurred when analyzing the failure measures of factory automation equipment through a professional manpower. This is to provide a predictive maintenance system for efficient management of factory automation facilities and productivity improvement to minimize

However, the objects of the present invention are not limited to the above-mentioned objects, and other objects not mentioned will be clearly understood by those skilled in the art from the following description.

In order to achieve the above object, a predictive maintenance system for efficient management and productivity improvement of factory automation facilities according to an embodiment of the present invention is an Active Predictive Maintenance (hereinafter, APDM) equipment 100 installed in each factory automation facility 10 . ), in the predictive maintenance system (1) for efficient management and productivity improvement of factory automation facilities including the wireless AP (200), the network (300), and the mobile terminal (600), the APDM equipment (100) includes a wired communication As a device based on (serial, Ethernet) and wireless communication (Wi-Fi, LTE), it consists of a control unit, a wired/wireless communication modem, and a storage medium. ) to provide a program to check data through short-distance wireless communication, and to store data including operation, production, status, tool, change point, and alarm of the factory automation facility 10 in the database 130 can be characterized.

At this time, the APDM equipment 100 reflects the stored data in real time to provide a real-time monitoring screen, and through integrated analysis between the information of each data, predicts quality and alarm abnormalities in advance and derives the result information for factory automation equipment ( 10) may be characterized in that it is provided to the user.

In addition, the APDM equipment 100 controls the factory automation equipment 10 automatically and manually by deriving the factory automation equipment 10 control parameter values from the analyzed data, so that the factory automation equipment 10 management and product It provides quality status, and all data collected by the APDM device 100 is transmitted to the central control server 400 or the cloud server through the network 300 through the wireless AP 200 to enable an integrated solution. can be characterized as

The predictive maintenance system for efficient management and productivity improvement of factory automation equipment according to an embodiment of the present invention provides systematic management through the predictive maintenance technology of APDM equipment. It provides an effect that can help improve productivity of products (products, parts) and the lifespan of equipment by significantly lowering the

In addition, the predictive maintenance system for efficient management and productivity improvement of factory automation equipment according to another embodiment of the present invention, the failure time of the factory automation equipment, and other failure types MTTR (Mean Time To Repair) / MTBF Analysis information such as (mean time between failures) and overall production efficiency can also be acquired comprehensively, so it provides the effect of improving production quality and enhancing price competitiveness through predictive maintenance.

In addition, the predictive maintenance system for efficient management and productivity improvement of factory automation facilities according to another embodiment of the present invention takes 1 to 2 weeks and reduces financial loss when analyzing the failure measures of factory automation facilities through a professional manpower. It is possible to provide the effect of minimizing the loss by providing the failure prediction and cause and result to the manager as a predictive maintenance system of APDM equipment.

1 is a diagram showing a predictive maintenance system 1 according to an embodiment of the present invention.
2 is a block diagram showing the components of the APDM equipment 100 in the predictive maintenance system 1 according to an embodiment of the present invention.
3 is a diagram illustrating a UI screen provided by the data collection module 121 of the APDM equipment 100 of the predictive maintenance system 1 according to an embodiment of the present invention.
4 is a diagram illustrating a UI screen provided by the data providing module 122 of the APDM equipment 100 of the predictive maintenance system 1 according to an embodiment of the present invention.
5 is a diagram illustrating an artificial neural network structure for analyzing tool exchange detected by the tool management module 124 among the APDM equipment 100 of the predictive maintenance system 1 according to an embodiment of the present invention.

Hereinafter, a detailed description of a preferred embodiment of the present invention will be described with reference to the accompanying drawings. In the following description of the present invention, if it is determined that a detailed description of a related well-known function or configuration may unnecessarily obscure the gist of the present invention, the detailed description thereof will be omitted.

In the present specification, when any one component 'transmits' data or signal to another component, the component may directly transmit the data or signal to another component, and through at least one other component This means that data or signals can be transmitted to other components.

1 is a diagram illustrating a predictive maintenance system (hereinafter, referred to as a predictive maintenance system) 1 for efficient management of factory automation equipment and improvement of productivity according to an embodiment of the present invention. 1, the preliminary maintenance system (1) is installed in each factory automation equipment (10) Active Predictive Maintenance (hereinafter, APDM) equipment (100), wireless AP (200), network (300), central control server ( 400), the AI server 500, the big data server 500a, and at least one mobile terminal 600 for managing each APDM device 100 may be included.

The APDM equipment 100 is a wired communication (serial, Ethernet) and wireless communication (Wi-Fi, LTE)-based device, and is composed of a control unit, a wired/wireless communication modem, a storage medium, etc. It is possible to provide a program so that the user can check the data through its own display screen and the mobile terminal 600 through short-range wireless communication.

APDM equipment 100 may store data such as operation, production, state, tool, change point, alarm, etc. in the database 130 through each collection module.

In particular, the APDM equipment 100 reflects the stored data in real time to provide a real-time monitoring screen, and through integrated analysis between the information of each data, predicts quality and alarm abnormalities in advance and derives the result information for the factory automation facility (10) ) can be provided to users of

In addition, the APDM equipment 100 can provide the best facility management and product quality status by automatically/manually controlling the factory automation facility 10 by deriving the best facility control parameter value with the analyzed data. All data collected by the APDM device 100 may be transmitted to the central control server 400 or the cloud server to support an integrated solution.

The network 300 is a high-speed backbone network of a large-scale communication network capable of large-capacity, long-distance voice and data services, and may be a next-generation wired or wireless network for providing the Internet or high-speed multimedia services. When the network 300 is a mobile communication network, it may be a synchronous mobile communication network or an asynchronous mobile communication network. As an example of the asynchronous mobile communication network, there may be a wideband code division multiple access (WCDMA) type communication network. In this case, although not shown in the drawing, the network 300 may include a Radio Network Controller (RNC). Meanwhile, although the WCDMA network is taken as an example, it may be a 3G LTE network, a 4G network, other next-generation communication networks such as 5G, and other IP-based IP networks. The network 300 serves to mutually transmit signals and data between the wireless AP 200 , the central control server 400 and the AI server 500 , the big data server 500a , the mobile terminal 600 , and other systems. do

2 is a block diagram showing the components of the APDM equipment 100 in the predictive maintenance system 1 according to an embodiment of the present invention. 3 is a diagram illustrating a UI screen provided by the data collection module 121 of the APDM equipment 100 of the predictive maintenance system 1 according to an embodiment of the present invention. 4 is a diagram illustrating a UI screen provided by the data providing module 122 of the APDM equipment 100 of the predictive maintenance system 1 according to an embodiment of the present invention. 5 is a diagram illustrating an artificial neural network structure for analyzing tool exchange detected by the tool management module 124 among the APDM equipment 100 of the predictive maintenance system 1 according to an embodiment of the present invention.

First, referring to FIG. 2 , the APDM device 100 includes a transceiver 110 , a control unit 120 , a database 130 , and a display unit 140 , and the control unit 120 includes a data collection module 121 . , including a data providing module 122, a data analysis module 123, a tool management module 124, a safety detection module 125, a predictive maintenance module 126, a tracking module 127, and a mobile providing module 128 can do.

The data collection module 121 collects and stores the processing data of the factory automation facility 10 and manages the result information through data standardization on the database 130, and collects the collected data and result information as shown in FIG. It can be provided to the central control server 400 and the mobile terminal 600 through the network 300 through output to the display unit 140 or transmission to the wireless AP 200 .

In the present invention, the factory automation facility 10 may be a facility for manufacturing a CNC (Computer Numerical Control) machine-based product. Accordingly, the data collection module 121 determines the coordinates of each drive shaft and the load amount of the drive shaft measured through a line sensor and a measuring encoder attached to a gantry loader among each component of the factory automation facility 10, and a feeder. FEED (length of feeding the cutting tool in the cutting direction for each revolution of the workpiece for production) and spindle motor load measured through a line sensor attached to and a measuring encoder attached to the feeder, respectively, with a measuring encoder attached to the feeder; Transmitting/receiving unit 110 to receive status data including the FEED RPM and FAN speed measured through the measuring encoder attached to the fan as parameter information and the ID of each factory automation facility 10 as metadata can be controlled

The data collection module 121 formally determines whether each parameter information constituting the received state data is within an effective range for each parameter pre-stored in the database 130 and transmits the analysis result information to the display unit 140 . In addition to output, when out of the effective range, the central control server 400 and the manager through the network 300 connected to the wireless AP 200 by the mobile providing module 128 according to the request for the mobile providing module 128 may control the transceiver 110 to transmit to the mobile terminal 600 operated by .

In addition, the data collection module 121 stores time-series data for each parameter information constituting the state data for each factory automation facility 10 according to a timeline on the database 130, and stores the stored time-series data It can be output to the display unit 140 as history management information in the form of graphs, or provided to the central control server 400 and the mobile terminal 600 through the network 300 through transmission to the wireless AP 200 . .

The data providing module 122 provides "analysis data" such as production performance, facility status, tool replacement time, change point, and alarm in addition to "result information" and "history management information" analyzed for each factory automation facility 10. Real-time data (result information; analysis data) and the historical data collected for data for a preset time are provided to the display unit 140 by itself as shown in FIG. 4 or transmitted to the wireless AP 200 through the network 300 through the central control server ( 400) and by controlling the transceiver 110 to provide to the mobile terminal 600, the central control server 400 and the mobile terminal 600 can provide access to intuitive information obtained by digitizing each analysis data. .

The data analysis module 123 is a wireless AP ( 200) and the analysis algorithm provided by the AI server 500 for the profile data accumulated on the big data server 500a as well as big data through the network 300 connected to the big data server 500a predicts the failure time of each electrical component through , to the mobile terminal 600 corresponding to the terminal identification number stored by matching with each factory automation facility 10, the transceiver 110 to transmit the solution direction information along with the possibility of a failure before a preset time from the failure point of each electrical component ) can be controlled.

As an example of a more specific analysis algorithm, the AI server 500 is a “first change factor” (of each electrical device) divided into a quantitative numerical range in which parameter information of each electrical device constituting the state data stored in the big data server 500a is accumulated. Information about the change that occurs over time according to the numerical range for each parameter), and the “second change factor” over time after the initial replacement for each electronic component (according to the passage of time for each electronic component parameter) After extracting the information about the change that occurs) on the big data server 500a, by inputting the first and second change factors extracted with a machine learning algorithm that utilizes the basic image of each electric device, the first and second change factors After generating image information to which the first and second results calculated by multiplexing the set first and second weights are applied for each period (week, month, year, season, etc.), the generated image information is By controlling the transceiver 110 to transmit to the mobile terminal 600 through the network 300, it is possible to provide the manager with realistic image information according to the use and time of each electrical equipment that is generated over time. .

Here, the image information generated by the analysis algorithm of the AI server 500 increases in each stage in a state where the first result value and the second result value for each electric device are divided by the first to nth stages (n is a natural number equal to or greater than 2). As image or video image information indicating that the traces of use for each electric device matched to the first and second result values gradually increase as the number increases, each mobile by the data analysis module 123 as metadata for each generated image information The data analysis module 123 arranges the matching degree of the actual failure point among the feedback information by the terminal 600 in descending order, and sets the image information having the matching degree corresponding to the largest quantitative value as the failure point image information. It may be provided by the AI server 500 .

Here, the first weight may be a value proportional to the total sum of the first change factors for at least one or more tools interlocked with each electrical component, and the second weight may be a value proportional to the total value of at least one or more tools interlocked with each electrical component. It may be a value proportional to the total sum of the two change factors, and the proportional constant is feedback information from the mobile terminal 600 to the data analysis module 123 through the network 300 at the actual failure point according to the generated image information. When provided as , the data analysis module 123 may adjust to match the actual failure time point among the feedback information on the image information for each electronic component divided.

To this end, the analysis algorithm of the AI server 500 is a machine learning algorithm, and may be one of a decision tree (DT) classification algorithm, a random forest classification algorithm, and a support vector machine (SVM) classification algorithm.

The tool management module 124 not only grasps the lifespan and correction value (replacement time) of tools including electrical components in each factory automation facility 10 in real time, but also records the exchange of each tool stored on the big data server 500a. tool change detection can be effectively managed. To this end, the manager who manages each factory automation facility 10 transmits the tool replacement time through the network 300 or the wireless AP 200 to the tool management module 124 through the mobile terminal 600 to the database. 130, or through detection through a sensor method such as barcode detection for a tool that is itself replaced on each factory automation facility 10, the detection information is transferred to the tool management module ( 124) may be provided.

That is, when the tool management module 124 is an electrical component of the factory automation facility 10, the data analysis module 123 matches the image information through the image information obtained from the AI server 500, such as lifespan, replacement time, cause, etc. After controlling the transceiver 110 to receive through a request to the big data server 500a connected to the network 300 through the wireless AP 200 , it is transmitted to the mobile terminal 600 through the network 300 . The transceiver 410 may be controlled to do so, or the transceiver 410 may be controlled to transmit to the mobile terminal 600 in a short-range wireless communication method through the wireless AP 200 .

In addition, if the tool management module 124 is a tool such as a consumable for simple replacement rather than an electrical component of the factory automation facility 10, the self-replacement time for each tool is extracted from the time of tool replacement on the database 130, and then the remaining lifespan , to control the transceiver 410 to transmit the replacement time to the mobile terminal 600 through the network 300 or to transmit to the mobile terminal 600 through the wireless AP 200 in a short-range wireless communication method ( 410) can be controlled.

On the other hand, the tool exchange detected by the tool management module 124 is tool breakage through a request to the AI server 500 connected to the network 300 through the wireless AP 200 providing an artificial neural network structure as shown in FIG. 5 . can be analyzed.

Referring to FIG. 5 , the artificial neural network structure is composed of an input layer, an intermediate layer, and an output layer. The input layer may undergo a collection data input process in which state data such as the above-described spindle motor load, servo motor load, current, frequency, RPM, and FEED, which are parameters for monitoring the tool breakage state, are received as inputs.

In the middle layer, the neural network activation function operation according to the artificial neural network weight input can be performed in such a way that each factor received from the input layer is calculated with a neural network function by varying the weight according to the degree of closeness of tool breakage.

In the output layer, it is possible to perform a normal data comparison analysis and normal failure determination process to determine whether or not a tool is damaged or a sign of tool damage according to the calculated result according to the operation of the artificial neural network activation function in the middle layer.

The safety detection module 125 is a mobile terminal 600 operated by a manager set for each factory automation facility 10, and always monitors the same safety as the situation in which the manager opens the safety door for each factory automation facility 10 and works function can be provided.

That is, the safety detection module 125 is the mobile terminal 600 through the network 300 connected to the wireless AP 200 or through the wireless AP 200, Transmits risk element information that comprehensively analyzes failure point prediction information, solution direction information (early replacement, replacement of consumables, etc.) By controlling the transceiver 110 to do so, it is possible to provide a safety detection function capable of confirming and monitoring safety for each factory automation facility 10 .

More specifically, the safety detection module 125 generates internal two-dimensional modeling information by combining the image information generated by the data analysis module 123 for the components of each factory automation facility 10 centered on each electric device. can do.

Here, in the two-dimensional modeling information, an expected replacement time of each electrical component and consumable may be stored as metadata.

On the other hand, the safety detection module 125 is connected to the network 300 through the wireless AP 200, the information on the components that increase the risk when two or more of the replacement time has arrived in the image information for each electrical component. After receiving through the request to the big data server 500a, each replacement time information for the components with increased risk is transmitted through the network 300 connected to the wireless AP 200 or directly through the network 300 connected to the wireless AP 200 along with a warning message ( 200 , the transceiver 110 may be controlled to transmit to the mobile terminal 600 .

The predictive maintenance module 126 may provide a predictive maintenance data providing function for each factory automation facility 10 .

More specifically, the predictive maintenance module 126 performs the correlation analysis between the parameter information of the above-described state data including the load of the spindle motor for each factory automation facility 10 and the quality of the product, each factory automation facility 10 By preventing the occurrence of failures in advance, it is possible to improve the lifespan of equipment and product quality. To this end, each factory automation facility 10 transmits a quality level information request for a product according to the parameter information of each state data to the wireless AP 200 through short-range wireless communication, and the wireless AP 200 is connected to the network 300 The quality level information is input on the mobile terminal 600 by sending a quality level information request to the mobile terminal 600 through or directly through short-range wireless communication, and the quality level information can be provided to the predictive maintenance module 126. have.

In addition, the predictive maintenance module 126 detects the state of various electrical components required for driving by each factory automation facility 10 such as an amplifier, encoder, fan, and battery for each factory automation facility 10. Through the comparison between the sensing information and the basic information stored in the database 130 through monitoring, when an abnormality occurs, the mobile terminal through the network 300 connected to the wireless AP 200 or through short-range wireless communication by the wireless AP 200 By controlling the transceiver 110 to transmit to 600, a notification to the manager is performed, and in the event of a failure, the electrical components fatal to the facility life of each factory automation facility 10 are preemptively maintained by predictive maintenance of each factory automation facility (10). Lifespan can be improved.

In addition, the predictive maintenance module 126 detects the number of drives and transfers of the gantry loader for each factory automation facility 10, and analyzes the gear wear according to the moving distance, the drive shaft load, etc., and provides monitoring information. By controlling the transceiver 110 to transmit to the mobile terminal 600 through the network 300 connected to the wireless AP 200 or through the wireless AP 200, anomaly detection and facility life of the gantry loader are improved. can do it

On the other hand, the predictive maintenance module 126 displays the data in which the administrator sets a specific condition and an offset condition based on the standardized data in order to provide the predictive maintenance data to the mobile terminal 600 on the display unit 140 and provides it And, by analyzing the accumulated data and quality data of the facility predictive maintenance data for each factory automation facility 10, it is possible to provide data predicting a symptom of quality abnormality.

To this end, the predictive maintenance module 126 applies the same production time point as an analysis method to obtain the data when the product is of poor quality and the equipment predictive maintenance data for each factory automation facility 10 using a deep learning algorithm. By calculating data through learning and analysis, the data predicted quality abnormality by comparing the standardized data with real-time quality data is transmitted through the network 300 connected to the wireless AP 200 or the mobile terminal through the wireless AP 200 (600) can be provided.

The tracking module 127 may provide a change point management and abnormal cause tracking function for each factory automation facility 10 .

That is, the tracking module 127 detects the wireless AP 200 through the network 300 connected to the wireless AP 200 by each mobile terminal 600 for each factory automation facility 10 or by a short-range wireless communication method. After controlling the transceiver 110 to receive a request for arbitrary change to the processed data, which is each parameter information constituting the state data, the change point is recorded on the big data server 500a, so that it is managed in a general purpose machine and a dedicated machine You can manage the point of change for all the data that should be.

The tracking module 127 records the state change of each item corresponding to the change point, and when an abnormality occurs with respect to the component where the change point occurs, the cause of the occurrence is traced back through history analysis in consideration of the change point. It can provide "change point management and abnormal cause tracking function" that solves problems and prevents the recurrence of the same problem.

The mobile providing module 128 provides a mobile multi-platform environment through a network 300 connected to the wireless AP 200 by a mobile terminal 600 corresponding to a tablet or a smartphone, etc., regardless of location, or through a wireless AP A real-time monitoring function can be provided according to direct access through 200, and for this purpose, data is shared using a cloud-based basis for all data collected in the database 130, and when an alarm occurs, the mobile terminal 600 ) can be notified to the manager who operates the

In addition, the mobile providing module 128 is each factory by each mobile terminal 600 for remote control of the mobile terminal 600 via the network 300 connected to the wireless AP 200 or via the wireless AP 200 . By controlling the transceiver 110 to transmit the component and state information according to the circuit diagram of each factory automation facility 10 to the mobile terminal 600 to relay access to the automation facility 10, each factory automation facility (10) may provide a function to enable confirmation and correction.

Through this configuration, the APDM equipment 100 significantly reduces equipment downtime such as consumables management, equipment history management, and failure prediction through systematic management through predictive maintenance systems and methods, helping to improve product (parts) productivity and improve equipment life. Through predictive maintenance by acquiring analysis information such as failure types of factory automation equipment, MTTR (Mean Time To Repair) / MTBF (Mean Time Between Failures), and overall production efficiency It can improve production quality and increase price competitiveness.

The present invention can also be implemented as computer-readable codes on a computer-readable recording medium. The computer-readable recording medium includes all types of recording devices in which data readable by a computer system is stored.

Examples of computer-readable recording media include ROM, RAM, CD-ROM, magnetic tape, floppy disk, optical data storage device, etc. also includes

In addition, the computer-readable recording medium is distributed in network-connected computer systems, and computer-readable codes can be stored and executed in a distributed manner. In addition, functional programs, codes, and code segments for implementing the present invention can be easily inferred by programmers in the technical field to which the present invention pertains.

As described above, preferred embodiments of the present invention have been disclosed in the present specification and drawings, and although specific terms are used, these are only used in a general sense to easily explain the technical content of the present invention and help the understanding of the present invention. , it is not intended to limit the scope of the present invention. It will be apparent to those of ordinary skill in the art to which the present invention pertains that other modifications based on the technical spirit of the present invention can be implemented in addition to the embodiments disclosed herein.

1: Predictive maintenance system for efficient management of factory automation facilities and improvement of productivity
100: APDM (Active Predictive Maintenance) equipment
110: transceiver
120: control unit
121: data acquisition module
122: data providing module
123: data analysis module
124: tool management module
125: safety detection module
126: predictive maintenance module
127: tracking module
128: mobile providing module
130: database
140: display unit
200: wireless AP
300: network
400: central control server
500 : AI Server
500a: Big Data Server

Claims (3)

Active Predictive Maintenance (hereinafter, APDM) equipment (100), wireless AP (200), network (300), central control server (400), AI server (500), big data server ( 500a), including at least one or more mobile terminals 600 for managing each APDM equipment 100,
APDM equipment 100,
It is a device based on wired communication (serial, Ethernet) and wireless communication (Wi-Fi, LTE) and consists of a control unit, wired/wireless communication modem, and storage medium. A program is provided to the terminal 600 to check data through short-range wireless communication, and data including operation, production, status, tool, change point, and alarm are stored in the database 130 through each collection module. , reflects the stored data in real time to provide a real-time monitoring screen, and through integrated analysis between the information of each data, predicts quality and alarm anomalies in advance and derives the result information and provides it to the users of the factory automation facility 10, By deriving the value of the facility control parameter with the analyzed data and automatically/manually controlling the factory automation facility 10, the best facility management and product quality status are provided, and all data collected by the APDM equipment 100 is It is transmitted to the central control server 400 or cloud server to support an integrated solution,
The factory automation facility 10 is a facility for manufacturing CNC (Computer Numerical Control) machine-based products,
The APDM device 100 includes a transceiver unit 110 , a control unit 120 , a database 130 , and a display unit 140 , and the control unit 120 includes a data collection module 121 and a data providing module 122 . , including a data analysis module 123, a tool management module 124, a safety detection module 125, a predictive maintenance module 126, a tracking module 127, and a mobile provision module 128,
The data collection module 121 is
Together with the processing data collection and storage of the factory automation facility 10, the result information is managed on the database 130 through data standardization, and the collected data and result information are output to the display unit 140 or wireless AP ( 200) through the network 300 and provided to the central control server 400 and the mobile terminal 600,
Of each component of the factory automation facility 10, each drive shaft coordinate and the load amount of the drive shaft measured through the line sensor and measuring encoder attached to the gantry loader, the line sensor attached to the feeder, and the line sensor attached to the feeder FEED (length of feeding the cutting tool in the cutting direction for each revolution of the workpiece for production) and spindle motor load measured through a measuring encoder, respectively, measured through a measuring encoder attached to the feeder and a measuring encoder attached to the fan Controls the transceiver 110 to receive the state data including the FEED RPM and the FAN speed as parameter information and the state data with the ID of each factory automation facility 10 as metadata,
For each parameter information constituting the received state data, it is determined whether it is within the effective range for each parameter pre-stored in the database 130 and not only outputs the analysis result information to the display unit 140, but also when it is out of the effective range In response to a request for the mobile providing module 128, the central control server 400 and the mobile terminal 600 operated by the manager are transmitted through the network 300 connected to the wireless AP 200 by the mobile providing module 128. Controls the transceiver 110 so as to
As time-series data for each parameter information constituting the state data for each factory automation facility 10, it is stored according to a timeline on the database 130, and the stored time-series data is converted to history management information in the form of graphs, etc. It is output to the display unit 140 or provided to the central control server 400 and the mobile terminal 600 through the network 300 through transmission to the wireless AP 200,
The data providing module 122,
In addition to the "result information" and "history management information" analyzed for each factory automation facility 10, the wireless AP 200 and Real-time data (including result information and analysis data) and preset data according to a data request according to access of the mobile terminal 600 through the connected network 300 or access through the wireless AP 200 through short-range wireless communication The historical data collected for data over time is provided to the display unit 140 by itself or to the central control server 400 and the mobile terminal 600 through the network 300 through transmission to the wireless AP 200 . By controlling the transceiver 110 to do so, the central control server 400 and the mobile terminal 600 provide access to intuitive information that digitizes each analysis data,
The data analysis module 123 is
Network 300 connected to wireless AP 200 by profiling a plurality of parameters including load amount for each process for electric equipment including gantry loader, feeder, spindle motor load, and fan among components of each factory automation facility 10 Not only can it be made into big data on the big data server 500a through the Predict and output the predicted failure time to the display unit 140 or through the network 300 connected to the wireless AP 200 or a short-distance wireless communication method through the wireless AP 200, each factory automation facility (10) Controls the transceiver 110 to transmit solution direction information along with the possibility of a failure before a preset time from the failure point of each electrical component to the mobile terminal 600 corresponding to the terminal identification number that is matched and stored,
The tool management module 124 includes:
In each factory automation facility 10, the tool life and correction values (replacement timing) of tools including electrical components are grasped in real time, and tool exchange detection is effectively detected through the exchange records of each tool stored in the big data server 500a. manage,
In the case of an electrical device of the factory automation facility 10, the data analysis module 123 uses the image information acquired from the AI server 500 to determine the life span, replacement time, and cause matching the image information through the wireless AP 200 through the network ( After controlling the transceiver 110 to receive through a request to the big data server 500a connected to 300 ), the transceiver 410 is controlled or wirelessly transmitted to the mobile terminal 600 through the network 300 . Controls the transceiver 410 to transmit to the mobile terminal 600 in a short-range wireless communication method through the AP 200,
In the case of a tool such as a consumable for simple replacement rather than an electrical component of the factory automation facility 10, the self-replacement time for each tool is extracted from the time of tool replacement on the database 130, and the remaining life and replacement time are stored in the network 300. Controls the transceiver 410 to transmit to the mobile terminal 600 through, or controls the transceiver 410 to transmit to the mobile terminal 600 in a short-range wireless communication method through the wireless AP 200,
The safety detection module 125 is
A mobile terminal 600 operated by a manager set for each factory automation facility 10 provides a constant monitoring function for the same safety as the situation in which the manager opens each safety door of the factory automation facility 10 and works,
Through the network 300 connected to the wireless AP 200 or through the wireless AP 200 to the mobile terminal 600, the failure point prediction information of each electrical component analyzed by the data analysis module 123, solution direction information ( By controlling the transceiver 110 to transmit the information on whether or not the tool is damaged or the information on the sign of tool breakage analyzed by the tool management module 124 and the information on the risk factor component comprehensively analyzed (including early replacement and replacement of consumables), , provides a safety detection function that can check and monitor safety for each factory automation facility (10),
By combining the image information generated by the data analysis module 123 for the components of each factory automation facility 10 centered on each electrical component, internal 2D modeling information is generated, and the 2D modeling information includes each electrical component and consumables. The expected replacement time is stored as metadata,
A request to the big data server 500a connected to the network 300 through the wireless AP 200 for information on the components that increase the risk when two or more of the video information for each electrical equipment have arrived at the replacement time After receiving through the mobile terminal 600 through the network 300 connected to the wireless AP 200 or directly through the wireless AP 200 with a warning message for each replacement time information for components with increased risk Controls the transceiver 110 to transmit to
Predictive maintenance module 126,
Provides predictive maintenance data provision function for each factory automation facility (10),
By analyzing the correlation between the parameter information of the state data including the load of the spindle motor for each factory automation facility 10 and the quality of the product, the occurrence of a failure is prevented in advance for each factory automation facility 10,
Each factory automation facility 10 transmits a request for quality level information on a product according to the parameter information of each state data to the wireless AP 200 through short-range wireless communication, and the wireless AP 200 communicates with the network 300 through the network 300 . or directly transmits a quality level information request to the mobile terminal 600 through short-range wireless communication so that the quality level information is input on the mobile terminal 600, and when the quality level information is provided to the predictive maintenance module 126,
Predictive maintenance module 126,
For each factory automation facility (10), the state of various electrical components required for operation of each factory automation facility (10) of the amplifier, encoder, fan, and battery is monitored through the sensing unit formed in each electrical component, sensing information and database (130) Transceiver 110 to transmit to the mobile terminal 600 through the network 300 connected to the wireless AP 200 or through short-range wireless communication by the wireless AP 200 when an abnormal symptom occurs through comparison between the basic information stored on the ) to control
By detecting the number of driving and transport of the gantry loader for each factory automation facility 10, the gear wear according to the moving distance and the driving shaft load are identified and analyzed, and the monitoring information is transmitted to the wireless AP 200 and the network (300) connected to the network (300). ) to control the transceiver 110 to transmit to the mobile terminal 600 through or through the wireless AP 200,
In order to provide predictive maintenance data to the mobile terminal 600, data in which the administrator sets specific conditions and offset conditions on the basis of standardized data is displayed on the display unit 140 and provided, and each factory automation facility (10) By analyzing the accumulated data and quality data of predictive maintenance data for each facility, we provide data that predicts symptoms of quality abnormalities,
The tracking module 127 is
It provides a change point management and abnormal cause tracking function for each factory automation facility 10, and through the network 300 connected to the wireless AP 200 by each mobile terminal 600 for each factory automation facility 10, or After controlling the transceiver 110 to receive a request for arbitrary change to processed data, which is each parameter information constituting the state data through the wireless AP 200 according to the short-range wireless communication method, the transceiver 110 is controlled, and the point of change is set to the big data server 500a ) is recorded on the
By recording the change in status of each item corresponding to the point of change, when an abnormality occurs in the component where the point of change occurs, the cause of the occurrence is traced back through history analysis taking the point of change into account. It provides "change point management and abnormal cause tracking function" that plays a role in preventing the recurrence of problems.
The mobile providing module 128,
In order to provide a mobile multi-platform environment, direct access through the wireless AP 200 and the network 300 connected to the wireless AP 200 by the mobile terminal 600 corresponding to a tablet or a smartphone, etc., regardless of location, or through the wireless AP 200 Provides real-time monitoring function according to
Access to each factory automation facility 10 by each mobile terminal 600 for remote control of the mobile terminal 600 via the network 300 connected to the wireless AP 200 or via the wireless AP 200 Efficient management and productivity improvement of factory automation facilities, characterized in that the transceiver 110 is controlled to transmit the components and status information according to the circuit diagram of each factory automation facility 10 to the mobile terminal 600 for relay Predictive conservation systems for
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