KR102563344B1 - PREVENTIVE MAINTENANCE MANAGEMENT SERVER OF IoT SENSOR BASED PRODUCTION PROCESS - Google Patents

Abstract

It relates to a predictive maintenance management server for an IoT sensor-based production process of the present invention. A facility detection unit that collects and manages at a predetermined time period, a state diagnosis unit that analyzes the equipment detection information and diagnoses the state of the equipment, and a process control unit that controls the schedule of the production process using the state information of the equipment includes

Description

Predictive maintenance management server of IoT sensor-based production process {PREVENTIVE MAINTENANCE MANAGEMENT SERVER OF IoT SENSOR BASED PRODUCTION PROCESS}

The present invention relates to a predictive maintenance management server of an IoT sensor-based production process, and more particularly, a technology for monitoring failures or safety accidents using IoT sensors installed in facilities or workers in a production process is disclosed.

In general, motors and mechanical devices in a workplace such as a factory are exposed to a lot of vibration during work. Most of them generate vibration of a predetermined form within a certain error range, but vibration outside the predetermined shape is generated due to various factors such as malfunction in operation or external shock.

By detecting and analyzing the vibration state of motors or mechanical devices in these facilities, it is possible to minimize production problems due to process stoppage due to abnormal occurrences, time and cost required for replacement and repair of abnormal motors or mechanical devices. there is. However, in order to build a detection system, there is a problem that excessive installation costs are required and it must be optimized for the production process environment.

Among the conventional technologies, Korean Patent Registration No. 10-1962739 (published on March 28, 2019) relates to a failure prediction analysis system and method for mechanical equipment using big data analysis, and data from mechanical equipment installed throughout subway stations. It collects and monitors the condition of mechanical equipment in real time, detects abnormal signs of mechanical equipment to prevent failure in advance, saves manpower and costs due to post-repair, and prompt action can be taken even in case of mechanical equipment failure. It is a technical feature that makes this possible.

However, the above conventional technology simply predicts and analyzes failures of mechanical equipment, and has limitations in that it is impossible to control the amount of production according to failures of equipment in the production process, and reflects the biometric information of workers in the production process. Therefore, there is a limitation that the schedule of the production process cannot be adjusted.

A technical problem to be solved by the present invention is to provide a predictive maintenance management server of an IoT sensor-based production process that can adjust the schedule of the entire production process by analyzing the operation status according to the sensing information of the facility.

In addition, it is to provide a predictive maintenance management server of the IoT sensor-based production process that can adjust the schedule of the entire production process by analyzing the status information according to the biometric information of workers participating in the production process.

In addition, it is to provide a predictive maintenance management server for the IoT sensor-based production process that the manager can intuitively analyze by visualizing the sensing information in the entire production process and changing the connection network through grouping according to the sensing information.

In addition, it is to provide a predictive maintenance management server for the IoT sensor-based production process that can enhance security by managing the control information of the production process through blockchain technology.

A predictive maintenance management server for an IoT sensor-based production process according to an embodiment of the present invention includes a facility detection unit that collects and manages facility detection information from a first sensor attached to facilities in a production process at a predetermined time period, and the facility detection information. and a process control unit for controlling the schedule of the production process using the state information of the equipment and a state diagnosis unit for diagnosing the state of the equipment by analyzing the equipment.

In addition, it further includes a worker sensing unit that collects and manages biosensing information from a second sensor attached to the operator in the production process at a predetermined time period, and the condition diagnosis unit analyzes the biosensing information to diagnose the condition of the worker. Thus, when the predetermined normal range is exceeded, an alarm signal may be transmitted to the worker and manager.

In addition, the process control unit compares the first state information according to the facility sensing information with the second state information according to the biometric sensing information, and when the risk level of the second state information exceeds the risk level of the first state information, the The production process may be stopped.

Accordingly, it is possible to adjust the schedule of the entire production process by analyzing the operating state according to the sensing information of the facility.

In addition, the schedule of the entire production process can be adjusted by analyzing status information according to the biometric information of workers participating in the production process.

In addition, by visualizing the sensing information in the entire production process and changing the network through grouping according to the sensing information, the manager can intuitively analyze it.

In addition, security can be strengthened by managing the control information of the production process through blockchain technology.

1 is a configuration diagram of a predictive maintenance system.
2 is a block diagram of a predictive maintenance management server of an IoT sensor-based production process according to an embodiment of the present invention.
FIG. 3 is an exemplary diagram for explaining that a manager inspects and authenticates facilities in a facility inspection unit among predictive maintenance management servers of the IoT sensor-based production process according to FIG. 2 .
FIG. 4 is an exemplary diagram for explaining the prediction of a power amount difference when a facility is replaced in a condition diagnosis unit of a predictive maintenance management server of an IoT sensor-based production process according to FIG. 2 .
5 is an exemplary diagram for explaining transmission of an alarm signal from a worker sensing unit to a manager terminal among predictive maintenance management servers of the IoT sensor-based production process according to FIG. 2 .
6 to 9 are exemplary diagrams for explaining visualization of a connection network according to attribute information in a connection network provision unit among predictive maintenance management servers of an IoT sensor-based production process according to FIG. 2 .

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings. The terms used are terms selected in consideration of functions in the embodiment, and the meaning of the terms may vary depending on the intention of the guardian or operator or precedent. Therefore, the meaning of terms used in the embodiments to be described later, when specifically defined in the present specification, follows the definition, and when there is no specific definition, it should be interpreted as a meaning generally recognized by those skilled in the art.

1 is a block diagram of a predictive maintenance system.

Referring to FIG. 1 , the predictive maintenance management system includes a first sensor 10 , a second sensor 20 and a predictive maintenance management server 100 .

The first sensor 10 is an IoT (Internet of Things) sensor installed in the facility 1 of the production process. The first sensor 10 is connected to the management server 100 through a network and transmits facility detection information. In this case, it is preferable that a repeater for relaying communication is installed around the facility 1. The first sensor 10 is used as a general meaning of sensors capable of detecting vibration, temperature, current/voltage, humidity, fine dust, noise, gas, and the like, respectively. Several first sensors 10 may be installed in one facility 1 . In this case, the installation location may vary depending on the type of sensor. The first sensor 10 transmits facility detection information to the management server 100 at a predetermined time period.

The second sensor 20 is an IoT (Internet of Things) sensor installed on the worker 2 participating in the production process. The second sensor 20 is connected to the management server 100 through a network and transmits biosensing information. In this case, it is preferable that a repeater for relaying communication is installed around the facility 1. The second sensor 20 is used as a general meaning of sensors capable of detecting temperature, heart rate, blood pressure, location, motion, oxygen saturation, and the like. A plurality of second sensors 20 may be installed in one worker 2 . The second sensor 20 allows the worker 2 to determine how the worker 2 is moving in the production process and the current body condition.

The predictive maintenance management server 100 is connected to the first sensor 10, collects facility detection information from the first sensor 10, and diagnoses the state of the facility 1 based on this. The predictive maintenance management server 100 can receive and monitor equipment detection information in real time, and it is also possible to predict a failure situation in advance. When a failure of the facility 1 is expected, the predictive maintenance management server 100 may alert the administrator to induce diagnosis or facility replacement by the administrator in advance. The predictive maintenance management server 100 may adjust and manage the schedule for the production process. In this case, the predictive maintenance management server 100 may adjust or stop the schedule of the production process according to the status information of the facility 1 .

In addition, the predictive maintenance management server 100 is connected to the second sensor 20 to collect operator detection information from the second sensor 20 and diagnoses the state of the operator 2 based on this. The predictive maintenance management server 100 may receive and monitor operator sensing information in real time. The predictive maintenance management server 100 can predict in advance whether or not the health condition of the worker 2 is dangerous, and if the risk is expected, the worker 2 or the manager is notified in advance for a health checkup or replacement of the worker 2. can induce The predictive maintenance management server 100 may adjust or stop the schedule of the production process according to the state information of the operator 2 .

2 is a block diagram of a predictive maintenance management server of an IoT sensor-based production process according to an embodiment of the present invention, and FIG. 3 is a configuration diagram of a predictive maintenance management server of an IoT sensor-based production process according to FIG. FIG. 4 is an exemplary diagram for explaining that the state diagnosis unit of the predictive maintenance management server of the IoT sensor-based production process according to FIG. 2 predicts a difference in power consumption when replacing equipment .

1 to 4, the predictive maintenance management server 100 of the IoT sensor-based production process according to an embodiment of the present invention includes a facility detection unit 110, a state diagnosis unit 120, and a process control unit 130. include

The facility detection unit 110 collects and manages facility detection information from the first sensor 10 installed in the facility 1 of the production process. Here, the first sensor 10 may have a built-in communication module capable of remotely communicating with the management server 100 . A plurality of first sensors 10 may be installed in the facility 1 . In this case, the first sensor 10 is used as a general meaning of sensors capable of sensing vibration, temperature, current/voltage, humidity, fine dust, noise, gas, and the like, respectively. For example, vibration of the facility 1 may be sensed using a 6-axis sensor. In the case of a temperature sensor, the surface temperature of the facility 1 or the temperature of a specific area can be sensed. In the case of a current-voltage sensor, the current or voltage state of the facility 1 can be sensed.

In addition, the facility detection unit 110 may alert a manager when facility detection information is not received from the first sensor 10 or equipment detection information exceeding a normal operating range is transmitted. This is to determine that the first sensor 10 is detached from the facility 1 or fails to properly generate facility detection information due to a failure, so that the manager takes action. The facility detection unit 110 checks whether facility detection information is received from the first sensor 10 within a certain time after the inspection of the first sensor 10 is completed from the manager terminal 30, and the failure action is completed. judge whether it is Accordingly, the failure of the first sensor 10 itself can be more clearly identified and taken.

Meanwhile, the state diagnosis unit 120 diagnoses the state of the facility 1 by analyzing facility detection information. For example, the state diagnosis unit 120 diagnoses the state of the facility 1 by comparing it with facility detection information based on a preset normal range. For example, if the vibration of the facility 1 exceeds a preset normal range, it can be diagnosed as a failure state. In this case, the condition diagnosis unit 120 can also diagnose by subdividing into a normal range, an abnormal range, and a failure range in relation to the operation of the facility 1. In this case, the abnormal range refers to a state in which an abnormal symptom appears and is not a failure but is highly likely to fail. Therefore, by diagnosing the real-time status of the equipment 1, it is possible to quickly check whether or not there is a failure.

For example, the condition diagnosis unit 120 analyzes vibration, temperature, and current data, which are time-series data, using LSTM (Long Short Term Memory) among deep learning of artificial intelligence to detect failure of the facility 1 It is also possible to predict, but is not necessarily limited thereto. The status diagnosis unit 120 may also set and manage a usable period for each facility 1 according to the analyzed state information of the facility 1 . The condition diagnosis unit 120 may increase the degree of risk when each facility 1 has passed the predicted usable period.

In addition, the status diagnosis unit 120 may transmit status information of the facility 1 to the manager terminal 30 to notify the manager of inspection of the facility 1 with a problem. In this case, the manager may inspect the facility 1 in the production process and transmit inspection completion information to the predictive maintenance management server 100 . The status diagnosis unit 120 may compare status information of the facility 1 before and after the inspection completion information to determine whether a failure or abnormality of the facility 1 according to the inspection has been resolved. Accordingly, it is possible to check whether the facility 1 is inspected and manage the inspection history.

In addition, the status diagnosis unit 120 may analyze status information about the facility 1 after replacement or repair of the facility 1 occurs. In this case, the condition diagnosis unit 120 may transmit improvement information to the manager terminal 30 by analyzing how much the expiration date or performance is improved due to replacement or repair of the facility 1 . This is to index the degree of performance improvement of the facility 1 due to replacement or repair of the facility 1 so that the manager can easily check it. Such replacement information or repair information can be used as judgment data for determining replacement when an abnormal symptom occurs in the facility 1 later.

On the other hand, the process control unit 130 controls the schedule of the production process using the state information of the facility (1). The process control unit 130 controls the production process of the entire facility (1). In this case, the process control unit 130 may change the schedule of the production process according to the state information of the facility 1 . For example, if the facility (1) has abnormal symptoms, it can be controlled to reduce production or stop production until replacement with other facilities. Accordingly, the production process can be independently controlled and the production volume can be adjusted according to the change in the state of the facility 1 even remotely.

In addition, the process control unit 130 may provide schedule information in conjunction with a manufacturing execution system (MES) for the production process. This is to enable independent control by supplying control information for controlling the schedule of the production process in the process control unit 130 to a separate manufacturing execution system (MES) for each module. When the manufacturing execution system (MES) is introduced into the production process, the process control unit 130 provides control information according to each facility 1 so that the manager can control the schedule for the desired production process.

5 is an exemplary diagram for explaining transmission of an alarm signal from a worker sensing unit to a manager terminal among predictive maintenance management servers of the IoT sensor-based production process according to FIG. 2 .

1 to 5, the predictive maintenance management server 100 of the IoT sensor-based production process according to an embodiment of the present invention may further include an operator detection unit 140.

The worker sensing unit 140 collects and manages biosensing information from the second sensor 20 attached to the worker 2 in the production process at a predetermined time period. Here, the second sensor 20 is used as a general meaning of sensors capable of sensing temperature, heart rate, blood pressure, location, motion, oxygen saturation, and the like. The second sensor 20 may be attached to the worker 2's work cap, work shoes, or clothing, or may directly contact the body in the form of a smart watch or neckband. This is to prevent safety accidents by frequently detecting the health status of the worker 2 participating in the production process.

For example, the operator sensing unit 140 may detect a position through a GPS sensor and motion through a gyro sensor. The worker sensing unit 140 may detect temperature, heart rate, blood pressure, oxygen saturation, and the like of the worker 2 through a biosensor. The operator sensing unit 140 outputs biometric information to the state diagnosis unit 120 . When the second sensor 20 is implemented as a camera, the operator detecting unit 140 may also receive an image of the field. This is to check the working environment of the operator 2 remotely.

In this case, the condition diagnosis unit 120 analyzes the biosensing information to diagnose the condition of the operator 2 and transmits an alarm signal to the operator 2 and the manager when the condition exceeds a predetermined normal range. For example, the status diagnosis unit 120 may transmit a notification signal through the smart phone of the worker 2 or manager. This is to prevent safety accidents by alarming the worker 2 or the manager in advance before the worker 2 reaches a dangerous situation according to the biometric information of the worker 2 . The state diagnosis unit 120 may transmit the state information of the worker 2 to an external control center or manager at predetermined time intervals to manage the worker 2 .

In addition, the process control unit 130 may change the schedule of the production process according to the status information of the worker 2 . This is to reduce or stop the workload of the process when the fatigue or risk of the worker 2 on site increases in addition to the failure of the facility 1. The process control unit 130 may collect state information of the equipment 1 and the state of the worker 2 as big data, and may set an optimal schedule. This is to ensure optimal production efficiency by efficiently distributing the workload of the operator 2 and minimizing the failure of the equipment 1 in the production process.

In addition, the process control unit 130 may compare first state information based on facility sensing information with second state information based on biological sensing information. In this case, the process control unit 130 may calculate the degree of risk according to the first state information and calculate the degree of risk according to the second state information. It is desirable to calculate this risk by comparing it with a pre-established standard risk. The process control unit 130 may stop the production process when the risk level of the second status information exceeds the risk level of the first status information. This is to secure the safety of the worker 2 by stopping the production process when the worker 2 participating in the production process determines that it is dangerous.

6 to 9 are exemplary diagrams for explaining visualization of a connection network according to attribute information in a connection network provision unit among predictive maintenance management servers of an IoT sensor-based production process according to FIG. 2 .

1 to 9, the predictive maintenance management server 100 of the IoT sensor-based production process according to an embodiment of the present invention may further include a connection network providing unit 150.

The connection network providing unit 150 provides network information represented graphically by using a plurality of first sensors 10 installed in the facility 1 or a plurality of second sensors 20 installed in the operator 2 . For example, the connection network provider 150 displays a plurality of first sensors 10 or second sensors 20 having common attribute information. In this case, the attribute information may include, but is not necessarily limited to, the type of sensor, state information of the installed facility 1 or operator 2, expiration date, and the like. The connection network providing unit 150 groups the sensors to form a sensor group based on a common item selected from among a plurality of sensors by a manager's selection. The connection network providing unit 150 forms a connection network that changes the sensor group when a common item is changed by a manager's selection.

In addition, the connection network providing unit 150 may change and display the size and color of the sensor group according to the attribute information. For example, if the attribute information is vibration, the size of the sensor group may be varied and divided according to the strength section of the vibration. Accordingly, the worker 2 checks each other's connection network graphically based on the equipment detection information or biometric information collected from the first sensor 10 or the second sensor 20, thereby ensuring that the equipment 1 or It is possible to more efficiently and intuitively analyze information about the relation to the operator 2 .

For example, in FIGS. 6 and 7, a plurality of sensor groups 151 using the temperature information of the facility 1 as attribute information are shown as a network, and in FIGS. 8 and 9, the voltage information of the facility 1 is an attribute. A plurality of sensor groups 151 as information is represented as a network. Each sensor group 151 may be set as a sensor group 151 based on an average value of the first sensor 10 or the second sensor 20 within a preset condition range. Accordingly, the manager selects the desired attribute information and intuitively grasps the state of the facility 1 or the operator 2 based on the detection information of the first sensor 10 or the second sensor 20 corresponding thereto. can

Meanwhile, the predictive maintenance management server 100 of the IoT sensor-based production process according to an embodiment of the present invention may further include a blockchain unit 160.

The blockchain unit 160 may encrypt and manage sensing information, status information, and control information of the facility 1 or the operator 2. This is to maintain the supplementation of the sensing information, which is big data detected from the equipment 1 or the worker 2 included in the production process, the analyzed status information, and the control information of the production process. In this case, the encrypted information can be managed by differentiated use such as reading, modifying/reading according to the administrator's authority. This is to prevent a third party from arbitrarily manipulating primitive sensing information, status information, and control information.

In addition, the block chain unit 160 can encrypt by combining the administrator's identification information with primitive data such as sensing information, status information, and control information. This is to prevent a third party other than the administrator from arbitrarily viewing or modifying the data. In this case, the identification information may include the administrator's biometric information or unique password information. The block chain unit 160 enables the identification of sensing information, status information, and control information, which are primitive data, when decoding is performed using encrypted information as identification information. The blockchain unit 160 can additionally encrypt and manage related information when transmitting and duplicating encrypted data. This is to prevent unauthorized distribution of encrypted data to third parties without permission.

In the above, the present invention has been described with reference to the preferred embodiments described with reference to the drawings, but is not limited thereto. Therefore, the present invention should be interpreted by the description of the claims intended to cover obvious modifications that can be derived from the described examples.

100: management server
110: facility detection unit
120: condition diagnosis unit
130: process control unit
140: operator detection unit
150: connection network provider
151: sensor group
160: blockchain unit
10: first sensor
20: second sensor
30: manager terminal

Claims (3)

A facility detection unit that collects and manages facility detection information from a first sensor attached to facilities in a production process at a predetermined time period;
a state diagnosis unit for diagnosing the state of the facility by analyzing the facility detection information;
a process control unit controlling a schedule of the production process using the state information of the facility;
a worker sensing unit that collects and manages biosensing information from a second sensor attached to the worker in the production process at a predetermined time period; and
Providing network information graphically representing the first sensor and the second sensor having common attribute information, forming a sensor group based on a selected common item, and changing the sensor group when the common item is changed Including the provision of a connection network,
The state diagnosis unit,
The status information of the facility is transmitted to the manager terminal, and when inspection completion information is received from the manager terminal, the status information of the facility before and after the inspection completion information is compared to confirm whether or not the inspection has been performed, and the biometric information is analyzed. A predictive maintenance management server of an IoT sensor-based production process that diagnoses the worker's condition and transmits an alarm signal to the worker and manager when it exceeds a predetermined normal range. delete According to claim 1,
The process control unit,
The first state information according to the facility sensing information is compared with the second state information according to the biometric sensing information, and when the risk level of the second state information exceeds the risk level of the first state information, the IoT stops the production process. Predictive maintenance management server for sensor-based production processes.