KR102247013B1 - Machine parts failure prediction and life prediction system and b2b distribution service system based on the same - Google Patents

Abstract

The present invention provides a system for predicting failure of mechanical parts and life prediction, and a B2B distribution service system based on the system. The machine part failure prediction and life prediction system includes an information collection device for collecting measurement information related to a machine part to be diagnosed; A PHM (prognostics and health management) server that performs a condition diagnosis of the machine part, predicts a failure or lifespan of the machine part, and calculates a replacement time of the machine part; And receiving measurement information of the machine part from the information collecting device, transmitting an analysis request for requesting analysis of the machine part to the PHM server, and receiving a replacement timing of the part of the machine part from the PHM server. It includes a B2B trading platform server that notifies the manufacturer of the parts of the replacement timing.

Description

Machine parts failure prediction and life prediction system, and B2B distribution service system based on it {MACHINE PARTS FAILURE PREDICTION AND LIFE PREDICTION SYSTEM AND B2B DISTRIBUTION SERVICE SYSTEM BASED ON THE SAME}

The present invention relates to a machine parts failure prediction and life prediction system, and a B2B distribution service system based thereon.

The machinery industry is the generation of power (primary motor), movement of humans and cargo (cars, ships, aircraft, etc.), manufacturing of products (machine tools, textile machinery, etc.), cultivation and processing of food and beverages (farm machinery, food and beverage processing machinery, etc.), It spans the entire human life such as exploration to the unknown (space ships, deep sea ships, etc.), diagnosis and treatment, and control/measurement/measurement (medical machinery, measuring instruments, etc.), and plays a key role in the transition period of human development such as industrialization. .

For example, there are power transmission devices used in the machinery industry.

A power transmission device refers to a facility used to transmit power (energy that can be used directly for mechanical work) generated by the engine to the working part. The power transmission device functions as power supply and transmission, rotation speed control, and the like, and is used alone or in combination to form a power transmission system.

Such a power transmission device may generally include a chain type device, an electric motor type device, a reduction gear type device, and a bearing type device.

A power transmission device is an essential device used in an industrial field that requires power, and needs to be continuously diagnosed and repaired, and parts of the power transmission device are worn out and must be replaced periodically.

Therefore, the facility operating entity operating the power transmission device, the entity that maintains or maintains the power transmission device, or the entity that constructs the power transmission device, purchases new parts when parts of the power transmission device break down or end of life. Used for delivery devices.

1 is a view showing a distribution process of parts of a conventional power transmission device.

Referring to FIG. 1, parts of the power transmission device are manufactured by the manufacturer 10. Parts of the power transmission device are sold to customers 18 through distributors 12, local distributors 14 and distributors 16. The customer 18 purchases parts from the dealer 16 when the power transmission device breaks down or a problem occurs. At this time, if the dealer 16 does not have the desired parts, the customer 18 must purchase the parts from the local distributor 14 or distributor 12. If the desired part is not in the local distributor 14 or distributor 12, it was necessary to contact or order the manufacturer 10 of the corresponding part.

In particular, in the case of a smart production system in a manufacturing plant, in case of downtime due to a failure, not only the maintenance cost due to replacement of parts, but also the loss opportunity cost caused by the failure to produce the product during the repair period can be quite fatal.

Therefore, it is necessary to predict the failure of the component or predict the remaining useful life before the component fails or is not used due to the end of its service life. In addition, conventionally, there has been a problem in that the price of parts increases through the distribution of several stages.

The present invention is to solve the problems of the prior art to diagnose the state of the machine parts, to provide a machine parts failure prediction and life prediction system that can predict the demand of the machine parts in advance, and a B2B distribution service system based thereon. The purpose.

According to an embodiment of the present invention, a machine part failure prediction and life prediction system includes an information collection device for collecting measurement information related to a machine part to be diagnosed, a condition diagnosis of the machine part, and a failure or lifetime of the machine part. The PHM (prognostics and health management) server that predicts and calculates the replacement timing of the mechanical parts, and receives measurement information of the mechanical parts from the information collection device, and sends an analysis request requesting analysis of the mechanical parts to the PHM And a B2B (Business to Business) trading platform server that transmits to a server and receives the replacement timing of the parts of the mechanical parts from the PHM server and informs the manufacturer of the parts of the replacement timing.

The information collection device is installed at a plurality of locations of the machine part to be diagnosed, a plurality of Internet Of Things (IOT) sensors respectively acquiring measurement data at the plurality of locations, and noise generated from the machine part to be diagnosed And detecting a vibration, determining an abnormality of the machine part to be diagnosed based on the sensed noise and vibration, and when it is determined that an abnormality has occurred in the machine part to be diagnosed, wake-up of the plurality of IOT sensors. signal) and receiving a plurality of measurement data from the plurality of IOT sensors.

The master device may separate the fricative sound between mechanical parts from the noise and determine whether the parts are abnormal based on the fricative sound.

The machine part failure prediction and life prediction system may further include a terminal receiving measurement information related to the machine part from the information collecting device and transmitting it to the PHM server.

When the information collection device receives a plurality of IoT (Internet Of Things) sensors each installed at a plurality of locations of the diagnostic target machine part and respectively obtaining measurement data at the plurality of locations, and an information collection request from the terminal A master device that outputs a wake-up signal to the plurality of IOT sensors and receives measurement data from the plurality of IOT sensors may be included.

The analysis request may include IDs of the plurality of IOT sensors, measurement data, and a specification of a component in which the plurality of IOT sensors are installed.

The B2B trading platform server communicates with the PHM server to receive a condition diagnosis and lifetime prediction result of the device from the PHM server, and generates or updates a view table for a monitoring service, and the view table. It may contain a database to store.

The PHM server may store an algorithm for diagnosing the condition of the machine part and predicting a lifespan.

According to an embodiment of the present invention, a B2B (Business to Business) distribution service system is an information collection device that collects measurement information related to a machine part to be diagnosed, and performs a condition diagnosis of the machine part, and When receiving an analysis request requesting an analysis request for a PHM (prognostics and health management) server that predicts and calculates the replacement timing of the mechanical parts, and transmits the analysis request to the PHM server, from the PHM server A B2B trading platform server that receives the replacement timing of the parts of the machine parts and notifies the manufacturer of the parts of the replacement time, performs overall management of the machine parts, and receives measurement information related to the machine parts from the information collection device. Then, a machine parts management server that transmits an analysis request including the measurement information to the B2B market platform server, and receives a parts order related to the machine parts from the B2B market platform server, manages the production of parts, and releases the ordered parts. It includes a manufacturer server that notifies the delivery of parts to the machine parts management server or B2B market platform server.

According to the embodiments of the present invention described above, since it is possible to monitor machine parts in real time and predict the demand of machine parts in advance, not only maintenance costs due to parts replacement, but also losses caused by failure to produce products during the repair period. Can be prevented.

1 is a view showing a distribution process of parts of a conventional power transmission device.
2 is a diagram showing a system for predicting a failure of a machine part and predicting a lifespan according to an embodiment of the present invention.
3 is a block diagram of an information collection device according to an embodiment of the present invention.
4 is a block diagram showing the configuration of a B2B market platform server and a PHM server according to an embodiment of the present invention.
5 is a diagram illustrating a signal flow between an information collection device and a PHM server according to an embodiment of the present invention.
6 is a diagram illustrating a signal flow between an information collection device and a PHM server according to another embodiment of the present invention.
7 is a diagram illustrating a message flow in a system for predicting a failure of a machine part and predicting a life span according to an embodiment of the present invention.
8 is a diagram illustrating a flow of diagnosing and ordering machine parts in a B2B distribution service system according to an embodiment of the present invention.

Hereinafter, embodiments of the present invention will be described in detail through exemplary drawings. In adding reference numerals to elements of each drawing, it should be noted that the same elements are assigned the same numerals as possible, even if they are indicated on different drawings. In addition, in describing an embodiment of the present invention, when it is determined that a detailed description of a related known configuration or function interferes with an understanding of an embodiment of the present invention, a detailed description thereof will be omitted.

In addition, terms such as first, second, A, B, (a), and (b) may be used in describing the constituent elements of the embodiment of the present invention. These terms are only for distinguishing the component from other components, and the nature, order, or order of the component is not limited by the term. When a component is described as being "connected", "coupled" or "connected" to another component, the component may be directly connected or connected to that other component, but another component between each component It should be understood that may be “connected”, “coupled” or “connected”.

2 is a diagram showing a system for predicting a failure of a machine part and predicting a lifespan according to an embodiment of the present invention.

The machine component failure prediction and life prediction system uses sensors to monitor the condition of machine parts and detect signs of failure, diagnostics, and remaining useful life (RUL) predictions and effective health. Employ health management.

2, the machine parts failure prediction and life prediction system includes an information collection device 200, a B2B (Business to Business) market platform server 410, and a PHM (prognostics and health management) server 430. I can. The B2B market platform server 410 and the PHM server 430 may be implemented as one server 400.

The information collection device 200 measures and collects data representing changes in the state of machine parts.

The information collection device 200 collects measurement information related to the machine part 100 to be diagnosed. The information collection device 200 may transmit the measurement information to the B2B market platform server 410 or the PHM server 430.

The B2B market platform server 410 transmits an analysis request to the PHM server 430 upon receiving measurement information of machine parts from the information collection device 200.

When the PHM server 430 receives a request for analysis of a machine part from the B2B market platform server 410, the PHM server 430 diagnoses the condition of the machine part 100 and predicts the remaining useful life. The PHM server 430 transmits, to the B2B market platform server 410, an analysis result including a diagnosis result for the corresponding machine part 100 and a predicted remaining useful life.

The B2B market platform server 410 may provide demand prediction information for the machine part to the manufacturer 500 of the machine part 100 based on the analysis result. Alternatively, the PHM server 430 may transmit the demand prediction information of the machine part to the manufacturer 500.

Meanwhile, the terminal 300 may request measurement data for the machine part 100 from the information collection device 200. The terminal 330 belongs to a company or a person related to the machine part 100, for example. As described above, in relation to the machine part 100, there is a facility operating entity that operates a power transmission device, a body that maintains or maintains the power transmission device, or a entity that constructs the power transmission device. All of these require information about diagnosing the condition of the machine parts or predicting demand.

Accordingly, the terminal 300 may be configured to request measurement data or measurement information of a machine part from the information collection device 200 through a predetermined application. In this case, the terminal 300 may transmit measurement data or measurement information from the information collection device 200 to the B2B market platform server 410.

3 is a block diagram of an information collection device 200 according to an embodiment of the present invention.

Referring to FIG. 3, the information collection device 200 according to an embodiment of the present invention is installed at a plurality of positions of the machine part 100, respectively, and a plurality of devices for acquiring measurement data at the plurality of positions, respectively. The IOT sensor unit 210 including Internet Of Things (IOT) sensors 210-1, 210-2,..., 210-n and the master device 220 receiving a plurality of measurement data from the IOT sensor unit 210 ) Can be included.

The IOT sensor unit 210 includes a first IOT sensor 210-1, a second IOT sensor 210-2, ... , May include an nth IOT sensor 210-n. The plurality of IOT sensors 210-1, 210-2,..., 210-n may include a vibration sensor, an ultrasonic noise sensor, an infrared temperature sensor, a tribology-related sensor, a motor current detector, and the like. The tribology-related sensor may include a friction force/friction coefficient meter, a friction sound meter, a bearing sound tester, a waviness meter, a grease tester, and the like.

A certain sensor among the plurality of IOT sensors 210-1, 210-2,..., 210-n can perform measurement only when a specific condition is satisfied. In this case, a certain sensor may perform measurement when a specific condition is satisfied, store the measurement data, and transmit the measurement data to the master device 220 when the master device 220 requests the measurement data.

Each of the plurality of IOT sensors 210-1, 210-2,..., 210-n may be installed on a plurality of parts to be diagnosed to measure data necessary for diagnosis or life prediction from the plurality of parts. Measurement data may include vibration, noise, ultrasonic noise, infrared temperature, tribology, and motor current.

The master device 220 may detect noise or vibration generated from the machine part 100 and determine an abnormality of the machine part 100 based on the detected noise or vibration.

The abnormality of the mechanical part 100 determined by the master device 220 does not strictly determine whether an abnormality occurs in the mechanical part 100, and the change in the state of the mechanical part 100 is determined as an abnormality of the mechanical part. I can. The master device 220 outputs a wake-up signal to the plurality of IOT sensors 210-1, 210-2, ..., 210-n when it is determined that an abnormality has occurred in the mechanical part.

When determining the abnormality of the mechanical part as noise, the master device 220 may separate the fricative sound between the machine parts to be diagnosed from the noise and determine whether or not the parts are abnormal based on the fricative sound.

According to another embodiment, the master device 220 may receive a measurement request. The measurement request may be generated by a predetermined terminal. Upon receiving the measurement request, the master device 220 may output a wake-up signal to the plurality of IOT sensors 210-1, 210-2, ..., 210-n.

On the other hand, a plurality of IOT sensors (210-1, 210-2, ..., 210-n) upon receiving the wake-up signal from the master device 220, wake up from the standby mode and perform measurement for a predetermined time to obtain measurement data. do. The plurality of IOT sensors 210-1, 210-2, …, 210-n performs measurement and then enters the standby mode again to use the minimum power.

According to another embodiment, the plurality of IOT sensors 210-1, 210-2, ..., 210-n acquire measurement data by performing measurements in real time or periodically. For example, the plurality of IOT sensors 210-1, 210-2, ..., 210-n may wake up from the standby mode at a predetermined period, perform measurement for a predetermined time, and then return to the standby mode again. Alternatively, the plurality of IOT sensors 210-1, 210-2, …, 210-n may perform measurement in real time.

The plurality of IOT sensors 210-1, 210-2, ..., 210-n transmits the measurement data to the master device 220 after performing the measurement. In this case, the plurality of IOT sensors 210-1, 210-2, ..., 210-n transmit the sensor ID together with the measurement data to the master device 220.

The master device 220 may receive measurement data from a plurality of IOT sensors 210-1, 210-2, ..., 210-n. The master device 220 may generate measurement information according to measurement data and sensor IDs received from the plurality of IOT sensors 210-1, 210-2, ..., 210-n. The measurement information may include measurement data, sensor ID, machine part identification information, workplace identification information, and the like.

The master device 220 may transmit measurement information to the B2B market platform server 410. When the B2B market platform server 410 receives measurement information from the master device 220 of the information collection device 200, it requests the PHM server 430 to diagnose the condition of the corresponding machine part and analyze the life expectancy.

When the PHM server 430 receives a request from the B2B market platform server 410 for failure diagnosis and life prediction for a predetermined machine part, the PHM server 430 diagnoses the condition of the machine part and predicts the failure or life of the machine part.

Hereinafter, a B2B market platform server and a PHM server will be described in detail with reference to FIG. 4.

4 is a block diagram showing the configuration of a B2B market platform server and a PHM server according to an embodiment of the present invention.

Referring to FIG. 4, the B2B market platform server 410 is configured to interwork with the PHM server 430.

The B2B market platform server 410 includes a PHM server interworking module 412, a user interface 414, machine parts related information 418a, 418b, and one or more view tables 416a, 416b, 416c. The machine part-related information 418a and 418b includes transaction detail information 418a and parts standard information 418a.

When the PHM server interworking module 412 receives measurement information from the master device 220 of the information collection device 200, an analysis requesting the PHM server 430 to diagnose the condition and predict the lifespan of the corresponding machine part. Send the request. The analysis request may include the ID of the IOT sensor of the corresponding machine part, measurement data, and the specification of the corresponding machine part.

In addition, the PHM server interworking module 412 may receive an analysis result obtained by performing an analysis for diagnosing a condition of a corresponding machine part and predicting a lifespan from the PHM server 430. The analysis result may include a condition diagnosis result and a predicted life of the corresponding machine part. The PHM server interworking module 412 may store the analysis result in a memory or a database (not shown).

In addition, the PHM server interworking module 412 may create a view table for a monitoring service or update a previously stored view table. The view table may include a view table 416a for manufacturers of machine parts, a view table 416b for maintenance companies of machine parts, and a view table 416c for facility operators of machine parts. View tables can be transmitted to manufacturers, maintenance companies or facility operators via wired or wireless communication. The view table may be output through the user interface 414. The user interface 414 may output text, images, and the like to the user, and may receive an input from the user.

The user interface 414 may include input means and output means. The input means may include a key button, and may include a mouse, a joystick, a jog shuttle, a stylus pen, and the like. In addition, the input means may include a soft key implemented on the display. The output means may include a display, and may include an audio output means such as a speaker. In this case, when a touch sensor such as a touch film, a touch sheet, or a touch pad is provided on the display, the display operates as a touch screen, and the input unit and the output unit may be integrated.

At this time, the display is a liquid crystal display (LCD), a thin film transistor liquid crystal display (TFT LCD), an organic light-emitting diode (OLED), and a flexible display. , It may include at least one of a field emission display (FED) and a 3D display.

In addition, the B2B market platform server 410 may store information on machine parts, such as transaction details 418a of the corresponding machine part and a part standard 418b of the corresponding machine part.

The analysis result of the corresponding machine part provided from the PHM server 430 may include a condition diagnosis result of the machine parts, a remaining useful life, and the like.

When the PHM server 430 receives a request from the B2B market platform server 410 for failure diagnosis and life prediction for a predetermined machine part, it diagnoses the condition of the machine part and predicts the failure or life of the machine part.

The PHM server 430 includes engines for predicting failure or life of mechanical parts. Specifically, the PHM server 430 may include a life prediction engine 432, a condition diagnosis engine 434, and noise and vibration big data 436.

The life prediction engine 432 or the condition diagnosis engine 434 may process big data on measurement information related to a machine part, and determine a maintenance timing and a range.

For example, the life prediction engine 432 uses the noise and vibration big data 436 to predict the life of the machine parts. According to the method of predicting the lifespan, it can be roughly divided into two methods. The data-driven approach uses machine learning techniques to train the relationship between input and damage and predict future failures. Machine learning is a field of artificial intelligence that develops algorithms and technologies that enable computers to learn, and is used to predict future states based on known past trends through training data. In the case of the physical model-based method, the physical model varies depending on the application target, but it is possible to predict relatively accurate damage behavior over a longer period of time. In this case, the physical model must be verified, and statistical methods such as a statistical hypothesis test or a Bayesian method are used.

The life prediction engine 432 or the condition diagnosis engine 434 may monitor the condition of the machine part in real time through the information collection device 200.

The life prediction engine 432 or the condition diagnosis engine 434 is a prognostics technique after extracting an appropriate health index through big data processing techniques such as signal processing, machine learning, and statistical analysis. Can be used to predict the remaining useful life (RUL) and to determine the optimal maintenance interval and range. This allows users to perform overall health management.

Each of the life prediction engine 432 and the condition diagnosis engine 434 may detect an abnormal symptom based on measurement information and diagnose a defect or a cause of the defect.

The condition diagnosis engine 434 may determine the condition of the machine part as normal/abnormal based on the measurement data, compare it with reference data, and determine the presence or absence of a defect. The reference data may be measured data in a normal state.

When there is a defect in a mechanical part, the condition diagnosis engine 434 may determine and classify the severity of the defect. Accordingly, the condition diagnosis engine 434 may calculate the replacement timing of at least one machine part. The condition diagnosis engine 434 transmits the analysis result of measurement information related to the machine part to the B2B market platform server 410. .

The life prediction engine 432 may predict the remaining useful life of the machine part based on the condition of the machine part.

As described above, the PHM server 430 diagnoses the state of the mechanical parts and predicts the life, and then stores the part life data 438a and the part state data 438b.

In FIG. 4, the B2B market platform server 410 and the PHM server 430 are shown as separate configurations, but it is obvious to those skilled in the art that they may be integrated and configured.

5 is a diagram illustrating a signal flow between an information collection device and a PHM server according to an embodiment of the present invention.

The information collection device 200 transmits measurement information to the PHM server 430 through the B2B market platform server 410 as described above.

As described above, the information collection device 200 includes a plurality of IOT sensors, a plurality of IOT sensors 210-1, 210-2, ..., 210-n, and a master device 220.

Referring to FIG. 5, first, the master device 220 of the information collecting device 200 monitors the machine part in step 502. For example, the master device 220 may detect a state of a mechanical part such as noise or vibration generated from the mechanical part.

The master device 220 determines whether there is an abnormality in the machine part based on the state of the machine part detected in step 504. As described above, the abnormality of the machine part determined by the master device 220 does not strictly determine whether an abnormality occurs in the machine part, and a change in the state of the machine part may be determined as an abnormality of the machine part.

Accordingly, when there is a change in the state of the machine part, the master device 220 makes a wake-up signal to the first IOT sensor 210-1 and the second IOT sensor 210-2 in steps 506 and 508. To transmit.

When the first IOT sensor 210-1 and the second IOT sensor 210-2 receive a wake-up signal from the master device 220, they wake up from the standby mode in steps 510 and 511 and perform measurement for a predetermined time. Acquire the data. The first and second IOT sensors 210-1 and 210-2 perform measurement and then enter the standby mode again to use the minimum power.

After measuring the state of the machine part, the first IOT sensor 210-1 transmits the measurement data to the master device 220 in step 512, and the second IOT sensor 210-1 measures the state of the machine part. In step 512, the measurement data is transmitted to the master device 220.

When the master device 220 receives measurement data from the first IOT sensor 210-1 and the second IOT sensor 210-2, the PHM server 430 generates measurement information through the B2B market platform server 410. Can be sent to.

Accordingly, the PHM server 430 analyzes the machine part based on the measurement information of the machine part in step 518, diagnoses the state of the machine part, and predicts the remaining useful life (RUL).

6 is a diagram illustrating a signal flow between an information collection device and a PHM server according to another embodiment of the present invention.

Referring to FIG. 6, the first IOT sensor 210-1 and the second IOT sensor 210-2 perform measurements in real time or periodically in steps 532 and 533 to obtain measurement data. For example, the plurality of IOT sensors 210-1, 210-2, ..., 210-n may wake up from the standby mode at a predetermined period, perform measurement for a predetermined time, and then return to the standby mode again. Alternatively, the plurality of IOT sensors 210-1, 210-2, …, 210-n may perform measurement in real time.

According to an embodiment, in the case of periodic measurement, although not shown in FIG. 6, the first IOT sensor 210-1 and the second IOT sensor 210-2 determine whether a predetermined period has arrived and the predetermined period has arrived. If yes, measurement is performed in steps 532 and 533.

According to another embodiment, in the case of real-time measurement, the first IOT sensor 210-1 and the second IOT sensor 210-2 may perform measurements in real time in steps 532 and 533 and store the measurement data. . In this case, real-time is an application-related time unit in which the computer must respond quickly as required by the user and as quickly as the control process needs. Accordingly, when the unit of real time is set in units of 10 minutes, the first IOT sensor 210-1 and the second IOT sensor 210-2 may perform measurement in units of 10 minutes. In addition, the first IOT sensor 210-1 and the second IOT sensor 210-2 may be transmitted to the master device 220 in real time in steps 534 and 536.

When the master device 220 receives measurement data from the first IOT sensor 210-1 and the second IOT sensor 210-2, it may generate measurement information in step 538 and transmit it to the PHM server 430.

According to an embodiment of the present invention, a real-time unit of measurement and a real-time unit of measurement data transmission may be different. For example, the first IOT sensor 210-1 and the second IOT sensor 210-2 may perform measurement every 10 minutes and transmit measurement information to the PHM server 340 every 20 minutes.

The PHM server 430 analyzes the machine part based on the measurement information of the machine part in step 540, diagnoses the state of the machine part, and predicts a remaining useful life (RUL).

7 is a diagram illustrating a message flow in a system for predicting a failure of a machine part and predicting a life span according to an embodiment of the present invention.

Referring to FIG. 7, first, the information collection device 200 collects and stores measurement data in step 602. The terminal 300 may request measurement data from the information collection device 200 in step 604.

The information collection device 200 transmits the measurement data to the terminal 300 in response to the request for measurement data in step 606.

The terminal 300 may transmit measurement information to the B2B market platform server 410 in step 608. When the B2B market platform server 410 receives the measurement information of the machine part from the terminal 300, it transmits an analysis request to the PHM server 430 in step 610.

When the PHM server 430 receives a request for analysis of a machine part from the B2B market platform server 410, the PHM server 430 diagnoses the condition of the machine part and predicts the remaining useful life in steps 612 and 614. The PHM server 430 transmits the analysis result including the diagnosis result and the predicted remaining useful life for the corresponding machine part to the B2B market platform server 410 in step 616.

Upon receiving the analysis result, the B2B market platform server 410 may update the analysis result stored in relation to the corresponding machine part in step 618 and display the analysis result as a view table in step 620. As described above, in relation to machine parts, there are a facility operating entity that operates a power transmission device, a entity that maintains or maintains a power transmission device, or a entity that constructs a power transmission device. All of these require information about diagnosing the condition of the machine parts or predicting demand.

The view table may include a view table for a manufacturer of machine parts, a view table for a maintenance company of machine parts, and a view table for a facility operator of machine parts.

Subsequently, in steps 622 and 624, the B2B market platform server 410 may provide relative diagnostic information and demand prediction information for the machine parts to the terminal 300 and the manufacturer 500 of the machine parts or related companies. In this case, a different view table may be transmitted for each company.

Meanwhile, according to an embodiment of the present invention, it is possible to provide a B2B distribution service system that provides a service for distributing machine parts based on a machine part failure prediction and life prediction system. The B2B distribution service system may include a machine parts management server that manages machine parts, and a manufacturer server that is provided in a manufacturer and manages production, inventory management, and delivery of machine parts.

8 is a diagram illustrating a flow of diagnosing and ordering machine parts in a B2B distribution service system according to another embodiment of the present invention.

Referring to FIG. 8, the B2B distribution service system may include a machine parts management server 110 that manages machine parts. The machine parts management server 110 performs overall management of machine parts, and manages the state and replacement timing of consumable machine parts.

In addition, the B2B distribution service system receives an order for machine parts from the B2B market platform server 410 or PHM server 430, manages the production of parts, and when the order parts are shipped, the machine parts management server 110 or B2B market platform The server 410 or the PHM server 430 may include a manufacturer server 510 that notifies the delivery of parts.

The master device 220 of the information collecting device 200 transmits a wake-up signal to the IOT sensor unit 210 when it detects an abnormality in the machine part, that is, a change in the state of the machine part. Each of the plurality of IOT sensors included in the IOT sensor unit 210 may be installed on, for example, a plurality of machine parts to be diagnosed to measure data necessary for diagnosis or life prediction from the plurality of parts. Measurement data may include vibration, noise, ultrasonic noise, infrared temperature, tribology, and motor current.

The plurality of IOT sensors of the IOT sensor unit 210 performs measurement and then provides measurement data to the master device 220. The master device 220 transmits measurement information including measurement data and information related to machine parts to the machine parts management server 110.

When the machine parts management server 110 receives the measurement information from the information collection device 200, it may transmit an analysis request including the measurement information to the B2B market platform server 410. When the B2B market platform server 410 receives an analysis request from the machine parts management server 110, the PHM server 430 requests an analysis for condition diagnosis and life prediction of the corresponding machine part.

When the PHM server 430 receives a request from the B2B market platform server 410 for failure diagnosis and life prediction for a predetermined machine part, the PHM server 430 diagnoses the condition of the machine part and predicts the failure or life of the machine part.

The PHM server 430 transmits the analysis result to the B2B market platform server 410 after completing the analysis of the machine parts. When the B2B market platform server 410 receives the analysis result related to the machine part from the PHM server 430, the B2B market platform server 410 transmits the analysis result to the machine parts management server 110.

When the machine parts management server 110 receives the analysis result related to the machine parts from the B2B market platform server 410, the machine parts management server 110 determines a device or part that is predicted to fail in relation to the corresponding machine part, or determines a device or part that needs replacement. I can. If there is a device or part that needs replacement, the machine parts management server 110 orders the part from the B2B market platform server 410.

When the B2B market platform server 410 receives an order for a device or a part from the machine parts management server 110, the B2B market platform server 410 places an order for the part to the manufacturer server 510 of the part. When the manufacturer server 501 receives an order for a predetermined machine part from the B2B market platform server 410, the manufacturer server 510 determines whether the device or part is in stock, and if the device or part is not in stock, the device or Let the manufacture or production of parts take place.

Subsequently, the manufacturer server 510 delivers the device or part to the ordering place when the corresponding device or part is supplied. In this case, the ordering place may be the machine parts management server 110 or the B2B market platform server 410. When the device or component is shipped, the manufacturer server 510 may notify the machine parts management server 110 or the B2B market platform server 410 that the device or the device is shipped.

According to the embodiments of the present invention described above, since it is possible to monitor machine parts in real time and predict the demand of machine parts in advance, not only maintenance costs due to parts replacement, but also losses caused by failure to produce products during the repair period. Can be prevented.

In the above, even if all the constituent elements constituting the embodiments of the present invention have been described as being combined into one or operating in combination, the present invention is not necessarily limited to these embodiments. That is, within the scope of the object of the present invention, all of the constituent elements may be selectively combined and operated in one or more. In addition, terms such as "include", "consist of" or "have" described above mean that the corresponding component may be present unless otherwise stated, excluding other components. It should not be construed as being able to further include other components. All terms, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art, unless otherwise defined. Terms generally used, such as terms defined in the dictionary, should be interpreted as being consistent with the meaning of the context of the related technology, and are not interpreted as ideal or excessively formal meanings unless explicitly defined in the present invention.

The above description is merely illustrative of the technical idea of the present invention, and those of ordinary skill in the art to which the present invention pertains will be able to make various modifications and variations without departing from the essential characteristics of the present invention. Accordingly, the embodiments disclosed in the present invention are not intended to limit the technical idea of the present invention, but to explain the technical idea, and the scope of the technical idea of the present invention is not limited by these embodiments. The scope of protection of the present invention should be construed by the following claims, and all technical ideas within the scope equivalent thereto should be construed as being included in the scope of the present invention.

100: mechanical parts
200: information collecting device
410: B2B market platform server
430: PHM server

Claims (14)

In the machine part failure prediction and life prediction system,
An information collection device that collects measurement information related to a machine part to be diagnosed;
A PHM (prognostics and health management) server that performs a condition diagnosis of the machine part, predicts a failure or lifespan of the machine part, and calculates a replacement time of the machine part; And
Receives measurement information of the machine part from the information collection device, transmits an analysis request for requesting analysis of the machine part to the PHM server, receives the replacement timing of the part of the machine part from the PHM server, and the It includes a B2B (Business to Business) trading platform server that notifies the manufacturer of the parts of the replacement timing,
The information collection device
A plurality of IOT (Internet Of Things) sensors respectively installed at a plurality of locations of the diagnostic target machine part and each obtaining measurement data at the plurality of locations,
Detects noise and vibration generated from the machine part to be diagnosed, determines an abnormality of the machine part to be diagnosed based on the detected noise and vibration, and when it is determined that an abnormality has occurred in the machine part to be diagnosed, the plurality of IOTs A machine component failure prediction and life prediction system comprising a master device that outputs a wake-up signal to sensors and receives a plurality of measurement data from the plurality of IOT sensors.
delete The system of claim 1, wherein the master device separates the fricative sound between the mechanical parts from the noise and determines whether the parts are abnormal based on the fricative sound.
The method of claim 1,
Machine parts failure prediction and life prediction system further comprising a terminal for receiving the measurement information related to the machine parts from the information collecting device and transmitting to the PHM server.
The method of claim 1,
The plurality of IOT sensors perform measurement when certain conditions are satisfied, store the measurement data, and transmit the measurement data to the master device when the master device requests the measurement data. .
The method of claim 1 or 5,
The analysis request is a machine parts failure prediction and life prediction system including the ID of the plurality of IOT sensors, measurement data, and the specifications of the parts in which the plurality of IOT sensors are installed.
The method of claim 1,
The B2B trading platform server
A PHM server interworking module that communicates with the PHM server to receive a condition diagnosis and lifetime prediction result of the device from the PHM server, and generates or updates a view table for a monitoring service;
Machine parts failure prediction and life prediction system including a database storing the view table.
The method of claim 1,
The PHM server is a machine parts failure prediction and life prediction system for storing the condition diagnosis and life prediction algorithm of the machine parts.
In the B2B (Business to Business) distribution service system,
An information collection device that collects measurement information related to a machine part to be diagnosed;
A PHM (prognostics and health management) server that performs a condition diagnosis of the machine part, predicts a failure or lifespan of the machine part, and calculates a replacement time of the machine part;
Upon receiving an analysis request requesting analysis of the mechanical parts, the analysis request is transmitted to the PHM server, and the replacement timing of the parts of the mechanical parts is received from the PHM server to inform the manufacturer of the parts of the replacement timing. B2B trading platform server;
A machine parts management server that performs overall management of the machine parts and transmits an analysis request including the measurement information to the B2B market platform server when measurement information related to the machine part is received from the information collection device; And
It includes a manufacturer server that receives an order for parts related to the machine parts from the B2B market platform server, manages the production of parts, and notifies the release of parts to the machine parts management server or B2B market platform server when ordered parts are released,
The information collection device
A plurality of IOT (Internet Of Things) sensors respectively installed at a plurality of locations of the diagnostic target machine part and each obtaining measurement data at the plurality of locations,
Detects noise and vibration generated from the machine part to be diagnosed, determines an abnormality of the machine part to be diagnosed based on the detected noise and vibration, and when it is determined that an abnormality has occurred in the machine part to be diagnosed, the plurality of IOTs B2B distribution service system comprising a master device that outputs a wake-up signal to sensors and receives a plurality of measurement data from the plurality of IOT sensors.
delete The B2B distribution service system of claim 9, wherein the master device separates the fricative sound between mechanical parts from the noise and determines whether the parts are abnormal based on the fricative sound.
The method of claim 9,
B2B distribution service system further comprising a terminal for receiving the measurement information related to the machine part from the information collecting device and transmitting to the PHM server.
The method of claim 9,
The plurality of IOT sensors perform measurement when a specific condition is satisfied, store measurement data, and transmit the measurement data to the master device when the master device requests the measurement data.
The method of claim 9 or 13,
The analysis request is a B2B distribution service system including IDs of the plurality of IOT sensors, measurement data, and a standard of a part in which the plurality of IOT sensors are installed.

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