KR20240165161A - Integrated facility management system and its method - Google Patents

Abstract

The present invention relates to an integrated facility management system and a method thereof, and an integrated facility management system is introduced, which includes: a work data collection unit that collects work data of a worker for a facility to be diagnosed; a data learning unit that learns the work data of the worker collected in the work data collection unit, diagnostic data of the facility according to the worker's work, and an abnormality type of the facility calculated through the work data and the diagnostic data; and an external server that performs real-time facility status diagnosis based on the work data, diagnostic data, and abnormality type of the facility learned in the data learning unit, predicts the abnormality type when an abnormality occurs in the facility as a result of the diagnosis, and outputs the predicted abnormality type and the worker's work data matching the abnormality type to the outside.

Description

INTEGRATED FACILITY MANAGEMENT SYSTEM AND ITS METHOD

The present invention relates to an integrated facility management system and method thereof, and more specifically, to an integrated facility management system and method thereof for shortening the time for predicting abnormality types and the work time of workers when diagnosing facilities on an external server based on previously learned work data of workers and diagnostic data of facilities.

In the facility field, there is a trend toward applying prognostics and health management (PHM) to diagnose the current status of the facility and predict failures. PHM is a technology that collects status information on machinery, facilities, aircraft, and power plants to detect abnormal situations in the system, and optimizes facility management through advance prediction of failure points through analysis and predictive diagnosis. PHM is growing rapidly to realize cost-effective maintenance without downtime, while also reducing costs and ensuring stable system operation.

Meanwhile, in general industrial sites, workers depend on tacit knowledge and a large amount of manual books when working on equipment, so each worker's work data is different and there is a large difference in the diagnosis time for the type of abnormality. In addition, because the types of equipment, equipment parts, and types of abnormalities are very diverse, there is a limitation that workers cannot take quick action for each equipment to be diagnosed.

These problems can eventually increase the amount of labor required for equipment maintenance by workers, and increase the amount of time workers spend learning tacit knowledge and a large volume of manual books. In addition, as equipment diagnosis time increases, the equipment line itself can also be brought to a halt.

Accordingly, a method is needed to shorten the abnormal type prediction time and the worker's working time.

The matters described as background technology above are only intended to enhance understanding of the background of the present invention, and should not be taken as an acknowledgment that they correspond to prior art already known to those skilled in the art.

KR 10-2021-0115440 A

The present invention provides an integrated facility management system and method for reducing the time required to predict abnormality types and the work time of workers when diagnosing facilities on an external server based on previously learned worker work data and facility diagnostic data.

The technical problems to be achieved in the present invention are not limited to the technical problems mentioned above, and other technical problems not mentioned will be clearly understood by a person having ordinary skill in the technical field to which the present invention belongs from the description below.

As a means for solving the above technical problem, the present invention configures an integrated facility management system including: a work data collection unit that collects work data of a worker for a facility to be diagnosed; a data learning unit that learns diagnostic data of the facility according to the worker's work and an abnormality type of the facility calculated through the work data and the diagnostic data; and an external server that performs real-time facility status diagnosis based on the work data, diagnostic data, and abnormality type of the facility learned in the data learning unit, predicts the abnormality type when an abnormality occurs in the facility as a result of the diagnosis, and outputs the predicted abnormality type and the worker's work data matching the abnormality type to the outside.

For example, the work data collection unit can collect work data including the worker's work history and work video of the worker.

For example, the work data collection unit can collect video of the worker's work through an eye tracker that tracks the user's eye movements.

For example, the work data collection unit can set the worker's start and end movements, and the start and end times of the work video can be determined based on the time when the worker's start and end movements are detected through the eye tracker.

For example, when multiple work images are collected, the work data collection unit can sequentially connect the multiple work images to obtain continuous work images and learn the obtained continuous work images.

For example, the data learning unit can learn equipment diagnostic data including equipment performance data, temperature data inside the equipment, and vibration data.

For example, an external server can set a threshold value for each diagnostic data value based on the work data, diagnostic data, and equipment abnormality types learned in the data learning unit, and perform equipment status diagnosis by determining whether each measured diagnostic data value is higher than the preset threshold value.

For example, the external server may include a Prognostics and Health Management (PHM) server that performs equipment condition diagnostics to predict equipment failure or lifespan and to calculate when to replace the equipment.

For example, an external server can send predicted abnormality types and worker work data corresponding to the abnormality types to the data learning unit.

For example, the method may further include a display unit that receives and displays abnormal types output from an external server and work data of workers matching the abnormal types.

For example, the work data collection unit can collect work data of a worker performing work based on information reflecting the abnormal type displayed on the display unit and the work data of the worker matching the abnormal type.

As a method for solving the above technical problem, the present invention configures an integrated facility management method including a step of collecting a worker's work data for a facility to be diagnosed from a work data collection unit; a step of learning, from a data learning unit, the worker's work data collected from the work data collection unit, diagnostic data of the facility according to the worker's work, and an abnormality type of the facility calculated through the work data and the diagnostic data; a step of performing a real-time facility status diagnosis based on the work data, diagnostic data, and abnormality type of the facility learned from the data learning unit; a step of predicting the abnormality type when an abnormality occurs in the facility as a result of the diagnosis; and a step of externally outputting the predicted abnormality type and the worker's work data matching the abnormality type.

For example, a step of performing real-time equipment status diagnosis may include a step of setting a threshold value for each diagnostic data value based on previously learned work data and diagnostic data; and a step of performing equipment status diagnosis by determining whether each measured diagnostic data value is equal to or greater than the previously set threshold value.

For example, the method may further include a step of receiving and displaying an abnormal type output to the outside and a worker's work data matching the abnormal type.

According to the integrated facility management system and method of the present invention, the time for predicting the type of abnormality and the worker's work time can be shortened when diagnosing facilities on an external server based on previously learned worker's work data and facility diagnosis data.

The effects obtainable from the present invention are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art to which the present invention belongs from the description below.

FIG. 1 is a block diagram showing an integrated facility management system according to one embodiment of the present invention.
FIG. 2 is a drawing showing in detail the configuration of an integrated facility management system according to one embodiment of the present invention.
FIG. 3 is a flowchart showing the recording and editing process for a worker's work video according to one embodiment of the present invention.
Figure 4 is a flowchart showing the operation of an integrated facility management system according to one embodiment of the present invention.

Hereinafter, embodiments disclosed in this specification will be described in detail with reference to the attached drawings. Regardless of the drawing symbols, identical or similar components will be given the same reference numerals and redundant descriptions thereof will be omitted.

The suffixes "module" and "part" used for components in the following description are given or used interchangeably only for the convenience of writing the specification, and do not have distinct meanings or roles in themselves.

In describing the embodiments disclosed in this specification, if it is determined that a specific description of a related known technology may obscure the gist of the embodiments disclosed in this specification, the detailed description thereof will be omitted. In addition, the attached drawings are only intended to facilitate easy understanding of the embodiments disclosed in this specification, and the technical ideas disclosed in this specification are not limited by the attached drawings, and should be understood to include all modifications, equivalents, and substitutes included in the spirit and technical scope of the present invention. Terms that include ordinal numbers, such as first, second, etc., may be used to describe various components, but the components are not limited by the terms. The terms are used only for the purpose of distinguishing one component from another.

When it is said that a component is "connected" or "connected" to another component, it should be understood that it may be directly connected or connected to that other component, but that there may be other components in between. On the other hand, when it is said that a component is "directly connected" or "directly connected" to another component, it should be understood that there are no other components in between.

Singular expressions include plural expressions unless the context clearly indicates otherwise.

In this specification, it should be understood that the terms “comprises” or “has” and the like are intended to specify the presence of a feature, number, step, operation, component, part or combination thereof described in the specification, but do not exclude in advance the possibility of the presence or addition of one or more other features, numbers, steps, operations, components, parts or combinations thereof.

FIG. 1 is a block diagram showing an integrated facility management system according to one embodiment of the present invention. FIG. 1 mainly shows components related to this embodiment, and it is obvious that the implementation of an actual integrated facility management system may include fewer or more components than this.

Referring to FIG. 1, an integrated facility management system (100) according to one embodiment may include a work data collection unit (110), a data learning unit (120), an external server (130), and a display unit (140).

First, when a worker starts working on the equipment to be diagnosed, the work data collection unit (110) can collect the worker's work data on the equipment to be diagnosed. Here, the worker's work data includes the worker's work history and the worker's work video. The worker's work history includes the time the worker took to work on the equipment, the number of parts consumed in the work, the worker's actions, etc., and the work video includes a video taken while the worker is performing the work. The work video can be collected through an eye tracker. Eye tracking is a technology that tracks the movement of the worker's eyes to determine exactly where the worker's gaze is staying, and can be utilized when analyzing the time spent in the center of the field of vision or the pattern of processing information through the gaze path.

In addition, the data learning unit (120) can learn the worker's work data collected in the work data collection unit (110), the equipment's diagnostic data according to the worker's work, and the equipment's abnormality type calculated through the work data and the diagnostic data. Here, the equipment's diagnostic data means data indicating the equipment's status diagnosed as a result of the worker's equipment work. For example, the equipment's diagnostic data may include equipment's performance data, temperature data inside the equipment, and vibration data. The equipment's abnormality type is calculated through the worker's work data and the equipment's diagnostic data, and the calculated abnormality type is stored in the data learning unit (120) and can be utilized as a database (DB) for predicting the abnormality type of an external server.

In this way, the data learning unit (120) stores each data, and by organizing the stored data, the time required to determine the type of equipment abnormality and the cause of the abnormality type of the external server can be greatly shortened. Accordingly, the downtime of the equipment can be minimized.

In addition, the external server (130) can perform real-time equipment status diagnosis based on the work data, diagnostic data, and equipment abnormality types learned in the data learning unit (120). Here, the external server (130) is preferably a PHM server. The PHM server performs equipment status diagnosis by predicting equipment failure or lifespan and calculating the equipment replacement time.

In the present invention, the external server (130) predicts the abnormality type when an abnormality occurs in the equipment as a result of the diagnosis, and outputs the predicted abnormality type and the worker's work data matching the abnormality type to the outside. This enables the worker to check the work data including the previous work video when performing the equipment work by matching the work data of the worker corresponding to the abnormality type. This greatly reduces the amount of work required to ask colleagues about countermeasures or to search through thick manuals one by one. In addition, there is an effect that can lead to the effects of reducing the work time and minimizing downtime by intuitively understanding the work content while watching the video of the work that the worker directly performed.

More specifically, the external server (130) sets a threshold value for each diagnostic data value based on the work data, diagnostic data, and abnormality types of the equipment learned in the data learning unit (120), and determines whether each measured diagnostic data value is equal to or greater than the preset threshold value, thereby performing equipment status diagnosis. For example, when a vibration data value among the equipment diagnostic data is detected to be above the threshold value, the equipment can be predicted to be abnormal and broken.

In addition, the external server (130) can output the predicted abnormal type and the worker's work data corresponding to the abnormal type to the outside, as well as transmit them to the data learning unit (120) or the display unit (140). The data learning unit (120) receives and stores the predicted abnormal type and the worker's work data corresponding to the abnormal type, and uses them as data when diagnosing the equipment status of the external server (130) in the future.

In addition, the display unit (140) can receive and display the abnormality type outputted externally from the external server (130) and the worker's work data matching the abnormality type. Here, the display unit (140) may be performed by the work data collection unit (110) or the data learning unit (120). The display unit (140) is displayed so that the worker can recognize the received data while carrying a mobile device such as a tablet. When an abnormality occurs in the external server (130), the worker can perform the work based on the abnormality type displayed through the display unit (140), so that the worker's work time can be shortened.

Accordingly, the work data collection unit (110) collects the work data of the worker working based on the information reflecting the abnormal type displayed on the display unit (140) and the work data of the worker matching the abnormal type, so that the time for predicting the abnormal type when diagnosing equipment of the external server (130) can be significantly shortened as described above.

Each configuration will be described in more detail with reference to Fig. 2.

Figure 2 is a drawing showing in detail the configuration of an integrated facility management system (100) according to one embodiment of the present invention. It will be apparent to those skilled in the art that the configurations performing each function can be integrated or separated.

Referring to FIG. 2, the system (100) according to the present invention may include a memory device (101), a communication unit (102), and a power unit (103). The worker may provide work data including a work image to the system (100) according to the present invention. The display unit (140) may classify the image based on the received real-time work image of the worker, prediction information received from an external server (130), and ETS (Eye Tracking System) information (141) collected through an eye tracker. This may be accomplished through an AI image classification module (141). In addition, the display unit (140) may display an abnormality type outputted externally from an external server (130) and the worker's work data matching the abnormality type through a user interface (145). The system (100) transmits diagnostic data of equipment according to the worker's work, and the work data collection unit (110) can map work data (111) including work images by abnormality type (113). In addition, the work data collection unit (110) can edit and record (115) work images through the collected work data.

In addition, the data learning unit (120) may be ERP (Enterprise Resource Planning). ERP is a software system (100) that allows an organization to automate and manage core business processes for optimal performance. It learns the worker's work data collected by the work data collection unit (110) and the equipment's diagnostic data (base information, 121) according to the worker's work and transmits them to an external server (130). The equipment's diagnostic data may be processed and transmitted from the work site (factory, 200) through the Industrial Internet of Things (IIOT, 210).

In addition, the external server (130) receives the worker's work data and the equipment's diagnostic data according to the worker's work from the system (100) (131), performs real-time equipment status diagnosis, and can predict the equipment's abnormality type through the abnormality prediction module (133).

FIG. 3 is a flowchart showing the recording and editing process for a worker's work video according to one embodiment of the present invention.

Referring to FIG. 3, first, the work data collection unit (110) can receive a work image through an eye tracker worn by a worker (S301). Thereafter, the work data collection unit (110) plays the work image (S303) and determines whether all work images are finished (S305). If the work image is finished, the work data collection unit (110) can check all recorded images (S307) and determine whether the number of recorded work images of the worker exceeds 1 (S309). If it is determined that the number of recorded images exceeds 1 (YES of S309), the work data collection unit (110) sequentially connects multiple work images to obtain a continuous work image if multiple work images are collected (S311), and the obtained continuous work images are stored (S313). If the work image is not finished, the work data collection unit (110) can determine the start time and the end time based on the start and end actions of the worker described above.

Before explaining this, the work data collection unit (110) can set the start and end movements of the worker. This is a task for selecting a valid part excluding unnecessary parts from one work image, and the start and end movements of the worker can be detected through an eye tracker (S315, S317). Based on the time at which the start and end movements of the worker are detected, the work data collection unit (110) can determine the start and end times of the work image. When the start movement is detected, recording of the work image is performed (S317), and when the end movement is detected, recording of the work image is ended and playback of the work image is continued (S321).

Based on the configuration of the integrated facility management system (100) described above, an integrated facility management method according to an embodiment is described with reference to FIG. 4.

Figure 4 is a flow chart showing the operation of an integrated facility management system (100) according to one embodiment of the present invention.

Referring to Fig. 4, first, a worker performs a task for diagnosing the equipment (S401). Thereafter, the worker wears an eye tracker (S403) and can transmit the collected work data from the work data collection unit (110) to the data learning unit (120) in real time (S405). At the same time, the worker checks the information displayed through the display unit (140) and diagnoses the equipment (S407). Thereafter, the data learning unit (120) receives the diagnostic data of the equipment according to the worker's work (S413) and learns the work data of the worker collected in the work data collection unit (110) and the diagnostic data of the equipment according to the worker's work (S409, S415). The data learning unit (120) transmits the learned work data to an external server (130) and the maintenance procedure is completed (S411).

Thereafter, the external server (130) performs real-time equipment status diagnosis based on the work data including the worker's work video, the diagnostic data, and the equipment's abnormality type in the work data collection unit (110) (S431). The external server (130) can predict the abnormality type when an abnormality occurs in the equipment based on the diagnosis results and match the corresponding work data of the worker (S433).

In addition, the external server (130) can perform step S420. Step S420 consists of steps S421 and S423. Even if the data predicted by the external server (130) is the same or similar, the type of defect that occurred in the equipment and the status at the time may be different, so the image of the equipment analysis and action process is referenced together with the predicted data, and the 'a' information is updated and output (S421). Through this, since more accurate and detailed work data can be confirmed together, it can be used as a reference for maintenance work.

The external server (130) can transmit related images by referring to the worker's work data collected in the pre-learned work data collection unit (110) based on the predicted data, the equipment diagnostic data according to the worker's work, and the equipment's abnormality type (S423). Through this work, when the same or similar data occurs, the worker's abnormality analysis process and know-how can be confirmed by referring to the worker's eye movement path and chronological order, so that the equipment's abnormality type analysis can be performed quickly and accurately. Thereafter, the external server (130) transmits the aforementioned 'a' information to the display unit (140) (S435), and the work image classification model can be continuously improved through additional learning considering the 'a' information (S437).

According to the embodiments of the present invention described so far, the time for predicting the type of abnormality and the work time of the worker can be shortened when diagnosing equipment on an external server based on the previously learned work data of the worker and the diagnostic data of the equipment.

While the present invention has been illustrated and described with respect to specific embodiments thereof, it will be apparent to those skilled in the art that the present invention may be variously improved and modified without departing from the technical spirit of the invention as defined by the following claims.

100: Integrated facility management system
110: Work data collection unit
120: Data Learning Department
130 : External server
140 : Display section

Claims (14)

A work data collection unit that collects the worker's work data for the equipment to be diagnosed;
A data learning unit that learns the worker's work data collected in the work data collection unit, the equipment's diagnostic data according to the worker's work, and the equipment's abnormality type calculated through the work data and diagnostic data; and
An integrated facility management system including an external server that performs real-time facility status diagnosis based on work data, diagnostic data, and facility abnormality types learned in the data learning unit, predicts the abnormality type when an abnormality occurs in the facility as a result of the diagnosis, and outputs the predicted abnormality type and the worker's work data matching the abnormality type to the outside. In claim 1,
An integrated facility management system characterized in that the work data collection unit collects work data including the worker's work history and work video of the worker. In claim 2,
An integrated facility management system characterized by a work data collection unit that collects images of workers' work through an eye tracker that tracks the user's eye movements. In claim 3,
An integrated facility management system characterized in that the work data collection unit sets the start and end movements of a worker, and the start and end times of a work video are determined based on the time at which the start and end movements of the worker are detected through an eye tracker. In claim 4,
An integrated facility management system characterized in that, when multiple work images are collected, the work data collection unit sequentially connects multiple work images to obtain continuous work images, and learns the obtained continuous work images. In claim 1,
An integrated facility management system characterized by a data learning unit that learns facility diagnostic data including facility performance data, facility internal temperature data, and facility vibration data. In claim 6,
An integrated facility management system characterized in that the external server sets a threshold value for each diagnostic data value based on the work data, diagnostic data, and facility abnormality types learned in the data learning unit, and performs facility status diagnosis by determining whether each measured diagnostic data value is higher than the preset threshold value. In claim 1,
An integrated facility management system characterized in that the external server includes a PHM (Prognostics and Health Management) server that performs facility status diagnosis in a way that predicts facility failure or lifespan and calculates the time of facility replacement. In claim 1,
An integrated facility management system characterized in that the external server transmits predicted abnormality types and worker work data corresponding to the abnormality types to the data learning unit. In claim 1,
An integrated facility management system, characterized by further comprising a display unit that receives and displays abnormal types output from an external server and work data of workers matching the abnormal types. In claim 10,
An integrated facility management system characterized in that the work data collection unit collects the work data of a worker performing work based on information reflecting the abnormal type displayed on the display unit and the work data of the worker matching the abnormal type. A step of collecting the worker's work data for the equipment to be diagnosed in the work data collection unit;
A step of learning the worker's work data collected in the work data collection unit from the data learning unit, the equipment's diagnostic data according to the worker's work, and the equipment's abnormality type calculated through the work data and diagnostic data;
A step of performing real-time equipment status diagnosis based on work data, diagnostic data, and equipment abnormality types learned in the data learning department;
A step for predicting the type of abnormality when an abnormality occurs in the equipment as a result of the diagnosis; and
An integrated facility management method, comprising: a step of externally outputting predicted abnormal types and work data of workers matching the abnormal types. In claim 12,
The steps to perform real-time equipment status diagnosis are:
A step of setting a threshold value for each diagnostic data value based on learned work data and diagnostic data; and
An integrated facility management method, characterized by including a step of performing facility status diagnosis by determining whether each measured diagnostic data value is greater than or equal to a preset threshold value. In claim 12,
An integrated facility management method, characterized in that it further includes a step of receiving and displaying an abnormal type output to the outside and a worker's work data matching the abnormal type.

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