KR102588234B1 - Machine facility performance inspection system - Google Patents

Abstract

The present invention relates to a mechanical equipment performance inspection system that can prevent facility failure and improve operating efficiency by checking the operating status of facilities installed in a building.

Description

Machine facility performance inspection system

The present invention relates to a mechanical equipment performance inspection system, and more specifically, to a mechanical equipment performance inspection system that can prevent facility failures in advance and improve operating efficiency by checking the operating status of facilities installed in a building.

For assets such as buildings with a long period of use, the initial investment cost as well as maintenance cost account for a significant portion of the construction project cost, and when designing large building construction, design VE (Design VE) is considered from the perspective of life cycle cost (LCC). Value Engineering) review work must be carried out compulsorily.

In addition, when bidding on design and construction lump sum construction and alternative bidding construction, the economic feasibility review of the design must be submitted along with the bidding documents, and the economic feasibility review includes LCC analysis. The elements that make up this LCC include initial investment costs, repair and replacement costs, energy costs, and dismantling/disposal costs. As the advancement and informatization of the construction industry progresses rapidly, the economic feasibility review is faced with the problem of having to process the vast amount of information in the construction industry.

Inspecting building facilities is important not only in terms of safety but also efficiency. As building facilities become more diverse and their roles become more important, the need to secure expertise in facility inspection is emerging. In addition, in order for the various facilities installed in the building to maintain and demonstrate their original performance in various installation environments and operating conditions, appropriate inspection and maintenance activities must be carried out. To achieve this, the maintainer must have a good understanding of the characteristics and operation tips of each facility. There is a need for a plan to carry out systematic inspection and maintenance work.

Meanwhile, the above-mentioned background technology is technical information that the inventor possessed for deriving the present invention or acquired in the process of deriving the present invention, and cannot necessarily be said to be known technology disclosed to the general public before filing the application for the present invention. .

Korean Patent No. 10-230478

One aspect of the present invention provides a mechanical equipment performance inspection system that can check the operating status of facilities installed in a building to prevent facility failure in advance and improve operating efficiency.

The technical problem of the present invention is not limited to the technical problem mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art from the description below.

The mechanical facility performance inspection system according to an embodiment of the present invention includes a management server that checks the operating status of facilities installed in a building, determines whether the facility is operating normally based on the inspection results, and predicts the time of failure.

The management server is,

A data collection unit that collects measurement data generated during the inspection of the facility or inspection results recording the measurement data in the form of image data from a worker terminal held by the worker checking the operating status of the facility; and

It includes a safety evaluation unit that evaluates the safety of the facility based on the measurement data.

The safety evaluation department,

The safety index is calculated for each facility using the equation below, and facilities with a calculated safety index below the preset standard value are judged to be eligible for replacement or maintenance.

[Equation]

Here, RS is the safety index, k_p is the standard temperature set for each facility, k_n is the temperature value measured from the facility, w_p is the flow rate data exceeding the standard flow rate during the inspection section, and w_p is the flow rate measured below the standard flow rate during the inspection section. Data, w_t is the standard flow rate, a is the noise level (db), and b is a weight value set differently for each facility.

According to one aspect of the present invention described above, it is possible to check the operating status of facilities installed in a building to prevent facility failure in advance and improve operating efficiency.

1 is a diagram illustrating a schematic configuration of a mechanical equipment performance inspection system according to an embodiment of the present invention.
Figure 2 is a diagram showing the specific configuration of the management server according to the present invention.

The detailed description of the present invention described below refers to the accompanying drawings, which show by way of example specific embodiments in which the present invention may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the invention. It should be understood that the various embodiments of the present invention are different from one another but are not necessarily mutually exclusive. For example, specific shapes, structures and characteristics described herein may be implemented in one embodiment without departing from the spirit and scope of the invention. Additionally, it should be understood that the location or arrangement of individual components within each disclosed embodiment may be changed without departing from the spirit and scope of the invention. Accordingly, the detailed description that follows is not intended to be taken in a limiting sense, and the scope of the invention is limited only by the appended claims, together with all equivalents to what those claims assert, if properly described. Similar reference numbers in the drawings refer to identical or similar functions across various aspects.

Hereinafter, preferred embodiments of the present invention will be described in more detail with reference to the drawings.

1 is a diagram illustrating a schematic configuration of a mechanical equipment performance inspection system according to an embodiment of the present invention.

The mechanical equipment performance inspection system according to the present invention includes an operator terminal 100 and a management server 200.

The worker terminal 100 is an electronic device owned by a worker who checks the operating status of facilities installed in a building. It is capable of wired and wireless communication with external devices and is capable of inputting, outputting and processing information, such as a smartphone, PC, laptop PC, tablet PC, It may be in the form of a wearable device, etc.

Figures 3 and 4 are drawings showing a specific example of a checklist for each facility. The worker refers to the checklist for each facility pre-stored in the worker terminal 100, performs inspection activities appropriate for the characteristics and conditions of the facility to be inspected, and performs the inspection. The measurement data measured in the process is recorded in the worker terminal 100. The worker terminal 100 may transmit measurement data input from the worker to the management server 200.

The management server 200 checks the operating status of facilities installed in the building, determines whether the facility is operating normally based on the inspection results, and predicts the time of failure.

Figure 2 is a diagram showing the specific configuration of this management server.

Specifically, the management server includes a data collection unit 210 and a safety evaluation unit 220.

The data collection unit 210 communicates with the worker terminal 100, and receives image data from the worker terminal held by the worker checking the operating state of the facility, from the measurement data generated during the inspection of the facility or the inspection result in which the measurement data is recorded. collected in the form

The safety evaluation unit 220 evaluates the safety of each facility based on the measurement data collected by the data collection unit 210.

In one embodiment, the safety evaluation unit calculates the safety index for each facility using Equation 1 below.

[Equation 1]

Here, RS is the safety index, k_p is the standard temperature set for each facility, k_n is the temperature value measured from the facility, w_p is the flow rate data exceeding the standard flow rate during the inspection section, and w_p is the flow rate measured below the standard flow rate during the inspection section. Data, w_t is the standard flow rate, a is the noise level (db), and b is a weight value set differently for each facility.

For example, if the reference temperature is 60°C, the measured temperature is 72°C, w_p is 72psi, w_p is 36psi, w_t is 50psi, a is 56dB, and b is 0.3, the safety index can be calculated to be about 4.587.

At this time, the safety evaluation department determines that the facility whose calculated safety index is less than the preset standard value is subject to replacement or maintenance. For example, if the safety index of the facility according to the above-described example is calculated to be 4.587 and the standard value is 5, the facility is Because the calculated safety index is less than the standard value, the safety evaluation department determines that the facility is subject to replacement or maintenance.

In this way, the safety evaluation unit 220 can perform a reliable evaluation by calculating the safety index for each facility using the above-mentioned mathematical equation and determining whether the facility is safe according to the size of the calculated effective index. .

In some other embodiments, the safety evaluation unit 220 may set a placement location where a notification sign should be installed around a facility determined to be a replacement or maintenance target.

Here, a notification sign may be an installation in the form of a light, sign, speaker, etc. that informs that the facility is unsafe and requires caution.

Specifically, the safety evaluation unit 220 may collect drawing data about the location where the facility is installed and set a plurality of feature points from the collected drawing data. For example, when the facility is a fire hydrant, the safety evaluation unit 220 includes a first feature point (A) corresponding to the door connected to the emergency staircase, a second feature point (B) corresponding to the location of the fire hydrant, and an elevator installation location. A fourth feature point (D) is set to a random position within a predetermined radius around the third feature point (C) and the second feature point.

Afterwards, the safety evaluation unit calculates the appropriate separation distance between the second representative point and the fourth representative point using Equation 2 below.

[Equation 2]

Here, P is the appropriate separation distance between the second feature point and the fourth feature point, that is, the appropriate separation distance between the facility and the notification display, d_1 is the separation distance between the first feature point and the second feature point, and d_2 is the distance between the second feature point and the third feature point. d_3 is the separation distance between the first feature point and the third feature point, sinθ is the trigonometric function value with the first feature point as the vertex in the first feature triangle consisting of the first feature point, the second feature point, and the third feature point, cosθ In the first representative triangle, is a trigonometric function value with the third feature point as the vertex, σ is the standard deviation of the separation distances, α is the noise level (db), and β is a weight value set differently for each facility.

For example, if d_1 is 5.3, d_2 is 4.7, d_2 is 3, sinθ is 1, cosθ is 0.5, σ is 0.7, 56dB, and b is 0.3, the appropriate separation distance P is calculated to be about 1.5, and the worker must be checked by the safety evaluation department. A notification sign can be installed approximately 1.5m away from the fire hydrant, referring to the appropriate separation distance calculated by Accordingly, it is possible to install notification signs at effective locations by taking into account the user's frequent movement path within the building.

In some other embodiments, the data collection unit 210 extracts context information about the input data by learning training data with the Word2Vec algorithm in order to recognize measurement data in the form of text recorded on the inspection result sheet collected in the form of an image. You can build a neural network that does this.

The Word2Vec algorithm may include a neural network language model (NNLM). A neural network language model is basically a neural network consisting of an input layer, projection layer, hidden layer, and output layer. Neural network language models are used to vectorize words. Since the neural network language model is a known technology, a more detailed description will be omitted.

The Word2vec algorithm is for text mining and is an algorithm that determines proximity by looking at the front and back relationships between each word. The Word2vec algorithm is an unsupervised learning algorithm. As the name indicates, the Word2vec algorithm can be a quantitative technique that expresses the meaning of words in vector form. The Word2vec algorithm can express each word as a vector in a space of about 200 dimensions. Using the Word2vec algorithm, you can obtain the vector corresponding to the word for each word.

The Word2vec algorithm can enable dramatic improvements in precision in the field of natural language processing compared to other conventional algorithms. Word2vec can learn the meaning of words using the relationships between words and adjacent words in sentences in the input corpus. The Word2vec algorithm is based on an artificial neural network and starts from the premise that words with the same context have close meanings. The Word2vec algorithm learns through text documents, and for one word, other words that appear nearby (about 5 to 10 words before or after it) are taught to the artificial neural network as related words. Because words with related meanings are likely to appear close together in a document, two words may have increasingly closer vectors during repeated learning.

The learning methods of the Word2vec algorithm include the CBOW (Continuous Bag Of Words) method and the skip-gram method. The CBOW method predicts the target word using the context created by surrounding words. The skip-gram method predicts words that may come nearby based on one word. The skip-gram method is known to be more accurate in large datasets.

Therefore, in the embodiment of the present invention, the Word2vec algorithm using the skip-gram method is used. For example, if learning is successfully completed through the Word2vec algorithm, similar words can be located nearby in a high-dimensional space. According to the Word2vec algorithm as described above, the closer the distribution of surrounding words in a learning document is, the more similar the calculated vector values can be, and words with similar calculated vector values can be considered similar. Since the Word2vec algorithm is a known technology, detailed descriptions related to vector value calculation will be omitted.

The data collection unit 210 may input text extracted from image data received from the worker terminal 100 into a neural network to extract an evaluation result vector value representing context information.

The data collection unit 210 may calculate the similarity between the evaluation result vector value and each of the plurality of reference vector values, and extract the reference vector value with the highest similarity to the evaluation result vector value among the plurality of reference vector values. At this time, Euclidean distance, cosine similarity, Tanimoto coefficient, etc. may be adopted as similarity calculation methods.

The data collection unit 210 may extract the word corresponding to the reference vector value with the highest similarity to the evaluation result vector value as the word corresponding to the recognized text.

Additionally, the data collection unit 210 can train an artificial neural network, and can also use a trained artificial neural network. The processor can train or execute an artificial neural network stored in memory, and the memory can store a trained artificial neural network. The electronic device that trains the artificial neural network and the electronic device that uses it may be the same, but may also be separate. Artificial intelligence is a computer system that implements some of the functions of the human brain and can learn, guess, and make decisions on its own. As learning progresses, the probability of extracting an answer may increase. Artificial intelligence can be composed of learning and elemental technologies using it. Artificial intelligence learning is an algorithmic technology that classifies and learns features based on input data, and elemental technologies may be technologies that implement some of the functions of the human brain using learning algorithms.

Artificial intelligence is a technology that makes it easy to approach problems that can have multiple answers probabilistically, and can logically and probabilistically infer the optimal cycle, method, and plan according to any input data. Artificial intelligence's inference technology can include judging input data, optimization predictions, knowledge and probability-based reasoning, and preference-based planning.

Artificial neural network is one of the learning algorithms in the machine learning field and is a program that implements the connection between neurons and synapses in the brain. Artificial neural networks can be created through a program to create a neural network structure and then learn it to have the desired function. Although there may be errors, it is possible to learn based on huge data and output appropriate output data with input data. It has the advantage of being able to obtain output data with statistically good results and being similar to human reasoning.

The data collection unit 210 can build a query/metric dataset required for learning using an artificial intelligence algorithm built on big data, and may include a number of artificial neural networks that have been trained in advance for this purpose.

In this embodiment, the relay server may include a number of pre-trained artificial neural networks to perform machine learning algorithms. With machine learning, you can output output data based on input data and use the results to learn on your own, which can improve your own data processing ability. Artificial neural networks can extract features based on input data, infer regularities, and output result data. As this process accumulates, the reliability of the result data increases.

In this embodiment, the artificial neural network may be an algorithm that outputs text data from at least one of the feature data of the shape, length, number, and elevation difference of an object recognized as text. Artificial neural networks can infer the best output data using big data as input data or after going through a processing process to organize unnecessary data.

ve Bayes Classifier, Hidden Markov Model, K-Means Clustering 등이 사용될 수 있다.Depending on the type of learning, artificial intelligence machine learning models include Super Viser Learning, UnSuper Viser Learning, Semisupervised Learning, and Reinforcement Learning. And machine learning algorithms include Decision Tree, K-Nearest Nearest Bor, Artificial Neural Network, Support Vector Machine, Ensemble Learning, Gradient Descent, Na

ve Bayes Classifier, Hidden Markov Model, K-Means Clustering, etc. can be used.

The artificial neural network may be pre-trained on various input values that may be included in the input data. An artificial neural network may be an artificial neural network that is learned according to reinforcement learning, one of the learning methods. Reinforcement learning is a method of gradually increasing the probability of obtaining the correct result by setting rewards and limits. Artificial neural networks can also be modeled based on Convelutional Neural Network (CNN) or Recurrent Neural Network (RNN).

In this way, the data collection unit can extract measurement data from the inspection results collected in the form of images during the facility inspection process using big data and artificial neural networks.

As such, the technology according to the present invention may be implemented as an application or in the form of program instructions that can be executed through various computer components and recorded on a computer-readable recording medium. The computer-readable recording medium may include program instructions, data files, data structures, etc., singly or in combination.

The program instructions recorded on the computer-readable recording medium may be those specifically designed and configured for the present invention, or may be known and usable by those skilled in the computer software field.

Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks and magnetic tapes, optical recording media such as CD-ROMs and DVDs, and magneto-optical media such as floptical disks. media), and hardware devices specifically configured to store and perform program instructions, such as ROM, RAM, flash memory, etc.

Examples of program instructions include not only machine language code such as that created by a compiler, but also high-level language code that can be executed by a computer using an interpreter or the like. The hardware device may be configured to operate as one or more software modules to perform processing according to the invention and vice versa.

Although the above has been described with reference to embodiments, those skilled in the art will understand that various modifications and changes can be made to the present invention without departing from the spirit and space of the present invention as set forth in the following patent claims. You will be able to.

210: data collection unit
220: Safety evaluation department

Claims (3)

In a mechanical facility performance inspection system that includes a management server that checks the operating status of facilities installed in a building and determines whether the facility is operating normally based on the inspection results,
The management server is,
A data collection unit that collects measurement data generated during the inspection of the facility or inspection results recording the measurement data in the form of image data from a worker terminal held by the worker checking the operating status of the facility; and
Including a safety evaluation unit that evaluates the safety of the facility based on the measurement data,
The safety evaluation department,
A mechanical facility performance inspection system that calculates the safety index for each facility using the equation below, and determines that facilities with a calculated safety index below a preset standard value are eligible for replacement or maintenance.

[Equation]

Here, RS is the safety index, k_p is the standard temperature set for each facility, k_n is the temperature value measured from the facility, w_p is the flow rate data exceeding the standard flow rate during the inspection section, and w_p is the flow rate measured below the standard flow rate during the inspection section. Data, w_t is the standard flow rate, a is the noise level (db), and b is a weight value set differently for each facility.

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