Predictive detection device, predictive detection method, program

Abstract

To improve the accuracy of prioritizing maintenance work on air conditioners for which failures are predicted. [Solution] This disclosure provides a predictive maintenance device for detecting signs of failure in an air conditioning system 40 that is subject to maintenance work, the device acquires operating data from the air conditioning system 40, detects signs of failure based on the operating data acquired from the air conditioning system 40, acquires the air conditioning capacity of the air conditioning system 40, determines the priority of maintenance work for the air conditioning system in which signs of failure have been detected based on the air conditioning capacity, and outputs the priority along with the signs of failure.

Description

Technical Field

[0001] The present invention relates to a prediction detection device, a prediction detection method, and a program.

Background Art

[0002] When a failure occurs in a device such as an air conditioner (hereinafter referred to as an air conditioner), it becomes difficult to air-condition the space to be air-conditioned. Therefore, a technique is known in which a prediction of a failure is detected from the operation data of the air conditioner, and maintenance work on the air conditioner is performed before the failure occurs.

[0003] Patent Document 1 discloses a technique for detecting a plurality of predictions related to a failure in a device to be diagnosed, and determining the priority of maintenance for each of the plurality of detected predictions.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] However, in the conventional technology, there is a possibility that the accuracy of the priority of the maintenance work determined for failure prediction is low. Detecting a prediction of a failure is for performing maintenance work before the failure occurs. For example, the conventional technology identifies a component with a prediction of a failure based on operation data, and determines the priority of maintenance work based on the component. However, even if the importance of a component is high, a failure does not necessarily occur early, and the priority of maintenance work may not be high.

[0006] The present disclosure provides a technique for improving the accuracy of the priority of maintenance work performed on an air conditioner for which a failure has been predicted.

Means for Solving the Problems

[0007] The first aspect of this disclosure is, A predictive detection device for detecting signs of failure in an air conditioning system that is subject to maintenance work, It has a control unit, The control unit acquires operating data from the air conditioning system. Based on the operating data obtained from the aforementioned air conditioning system, signs of malfunction are detected. The air conditioning capacity of the aforementioned air conditioning system is obtained, The priority of maintenance work on an air conditioning system showing signs of malfunction is determined based on the air conditioning capacity. The system outputs the aforementioned priority level along with the signs of failure.

[0008] According to a first aspect of this disclosure, the accuracy of prioritizing maintenance work performed on air conditioners for which failures are predicted can be improved.

[0009] A second aspect of this disclosure is a predictive detection device as described in the first aspect, When the control unit detects signs of a malfunction, it outputs the predicted time when the malfunction is expected to occur.

[0010] A third aspect of this disclosure is a predictive detection device as described in the first aspect, The control unit outputs the probability of a failure occurring when it detects signs of a failure.

[0011] A fourth aspect of this disclosure is a predictive detection device according to the second or third aspect, The control unit outputs the predicted air conditioning capacity by inputting the operating data acquired from the air conditioning unit into a model that has learned the correspondence between the operating data acquired from the air conditioning unit and the air conditioning capacity. The actual air conditioning capacity is calculated using the operating data obtained from the aforementioned air conditioning system. If the difference between the expected air conditioning capacity and the measured air conditioning capacity is greater than the standard value, it is determined that the air conditioning capacity of the air conditioning system has decreased based on predetermined criteria.

[0012] A fifth aspect of the present disclosure is the prognostic detection device according to any one of the first to fourth aspects, wherein the prognostic detection device can communicate with a terminal operated by an operator or a user of the air conditioner via a network, when a prognostic of a failure is detected and it is determined that the air conditioning capacity of the air conditioner has decreased based on a predetermined determination criterion, the control unit outputs to the terminal a proposal for maintenance work on the air conditioner.

[0013] A sixth aspect of the present disclosure is the prognostic detection device according to any one of the first to fifth aspects, wherein when the control unit receives notification that maintenance work has been completed for an air conditioner in which a prognostic of a failure has been detected and it is determined that the air conditioning capacity of the air conditioner has decreased based on a predetermined determination criterion, the control unit calculates the air conditioning capacity of the air conditioner after the maintenance work has been completed.

[0014] A seventh aspect of the present disclosure is the prognostic detection device according to the sixth aspect, wherein it is determined that the air conditioning capacity before the maintenance work is performed has decreased based on a predetermined determination criterion, and when it is determined that the air conditioning capacity calculated after the maintenance work has been completed does not decrease based on a predetermined determination criterion, the control unit outputs information regarding the air conditioning capacity of the air conditioner.

[0015] An eighth aspect of the present disclosure is the prognostic detection device according to any one of the first to seventh aspects, wherein the control unit sets the priority of maintenance work for an air conditioner in which a prognostic of a failure has been detected and it is determined that the air conditioning capacity of the air conditioner has decreased based on a predetermined determination criterion to be higher than the priority of maintenance work for an air conditioner in which a prognostic of a failure has been detected and it is determined that the air conditioning capacity of the air conditioner has not decreased based on a predetermined determination criterion.

[0016] A ninth aspect of the present disclosure is the prediction detection device according to any one of the first to eighth aspects, wherein the control unit when it is determined that the air conditioning capacity of the air conditioner has decreased based on a predetermined determination criterion, determines the rate of decrease of the air conditioning capacity, when the rate of decrease of the air conditioning capacity is equal to or higher than a threshold value, increases the priority of the maintenance work of the air conditioner for which a sign of failure has been detected compared to when the rate of decrease of the air conditioning capacity is lower than the threshold value.

[0017] A tenth aspect of the present disclosure is a prediction detection method for a prediction detection device to detect a sign of failure of an air conditioner that is a target of maintenance work, the prediction detection device has a control unit, the control unit performs a process of acquiring operation data from the air conditioner, a process of detecting a sign of failure based on the operation data acquired from the air conditioner, a process of acquiring the air conditioning capacity of the air conditioner, a process of determining the priority of maintenance work of the air conditioner for which a sign of failure has been detected based on the air conditioning capacity, and a process of outputting the priority together with the sign of failure.

[0018] According to the tenth aspect of the present disclosure, the accuracy of the priority of maintenance work performed on the air conditioner for which a failure has been predicted can be improved.

[0019] An eleventh aspect of the present disclosure is in a prediction detection device that detects a sign of failure of an air conditioner that is a target of maintenance work, a process of acquiring operation data from the air conditioner, a process of detecting a sign of failure based on the operation data acquired from the air conditioner, a process of acquiring the air conditioning capacity of the air conditioner, a process of determining the priority of maintenance work of the air conditioner for which a sign of failure has been detected based on the air conditioning capacity, This is a program that performs the process of outputting the aforementioned priority along with the signs of failure.

[0020] According to the eleventh aspect of this disclosure, the accuracy of prioritizing maintenance work performed on air conditioners for which failures are predicted can be improved. [Brief explanation of the drawing]

[0021] [Figure 1] This diagram illustrates the process by which maintenance services detect signs of impending failure. [Figure 2] This is a scatter plot showing an example of load factor and COP. [Figure 3] This flowchart is a schematic example illustrating a process that outputs the priority of maintenance work when a failure is predicted. [Figure 4] This figure shows an example of the system configuration of a predictive maintenance system. [Figure 5] This diagram illustrates an example of the arrangement of a predictive detection device and a learning device in a predictive detection system. [Figure 6] This is an example of a hardware configuration for an edge device. [Figure 7] This is an example of a server device's hardware configuration. [Figure 8] This is an example of a functional block diagram that explains the functions of a predictive detection device in a predictive detection system by dividing them into blocks. [Figure 9] This is a functional block diagram of an example of a learning device. [Figure 10] This diagram illustrates the list of driving data included in the training data. [Figure 11] This figure shows an example of a COP normality prediction model constructed using a neural network. [Figure 12] This is an example flowchart illustrating the learning process using gradient boosting decision trees. [Figure 13] This diagram illustrates the concept of a decision tree. [Figure 14] This figure shows an example of training data to illustrate gradient boosting decision trees. [Figure 15] This diagram schematically illustrates the criteria for determining a decline in COP (Coefficient of Perception). [Figure 16] This figure shows a histogram of an example of "predicted COP - measured COP". [Figure 17] This figure shows statistical data illustrating an example of how long it takes from fault prediction to the actual occurrence of a fault. [Figure 18] This figure shows an example of a notification screen that informs the user that a malfunction has been predicted and which part it is. [Figure 19] This figure shows an example of COP before and after maintenance work. [Figure 20] This figure shows an example of the measured COP and predicted COP for a particular air conditioner over the past three months. [Figure 21] This figure shows an example of the average COP over the past three months. [Figure 22] This figure shows a comparative example of the rate of decline in COP. [Figure 23] This figure shows the correspondence between the rate of COP degradation, the timing of failure, and an example of the location of failure. [Figure 24] This is an example flowchart illustrating how to determine priority when a failure is predicted. [Figure 25] This is an example flowchart illustrating the process performed by a predictive maintenance device when a system engineer (SE) responds to a fault prediction. [Modes for carrying out the invention]

[0022] Below, we will describe an example of a form for implementing this disclosure, including a predictive detection system and a predictive detection method performed by the predictive detection system.

[0023] <Overview of Fault Prediction and Services> When an air conditioning system (hereinafter referred to as "air conditioner") malfunctions, it becomes difficult to control the temperature of the air-conditioned space. This can cause discomfort if people are living in the space, and may cause deterioration of goods stored in spaces such as warehouses. While there are methods such as duplicating the air conditioning system, this increases costs. For this reason, the predictive maintenance system provides a maintenance service that remotely monitors air conditioners 24 hours a day, 365 days a year.

[0024] Figure 1 illustrates the process by which maintenance services detect signs of failure.

[0025] (1) For example, the outdoor unit 31 is connected to the edge device 10, which will be described later, and the outdoor unit 31 transmits operating data to the edge device 10 in short cycles of minutes. Here, we assume that the two outdoor units 31 are in a state where signs of failure are detected and a state where a failure is detected.

[0026] (2) The edge device 10 transmits the operation data to the server device 60.

[0027] (3) The server device 60 analyzes the operating data to detect signs of failure (hereinafter sometimes referred to as failure prediction) or to detect a failure (the outdoor unit 31 or edge device 10 may also detect it). If the server device 60 detects a failure, it notifies the service engineer (hereinafter referred to as SE9) that it has detected a failure. If the server device 60 predicts a failure, it notifies the SE9 that it has predicted a failure. The SE9 is a worker who performs operation, maintenance, and servicing of the installed equipment or service.

[0028] (4) In the event of a malfunction, SE9 will perform maintenance work with the goal of "responding immediately." If a malfunction is predicted, SE9 will aim to "perform maintenance work within a certain period." Through this operational policy for maintenance services, the maintenance service aims to prevent serious malfunctions and ensure that the air conditioners run without interruption.

[0029] However, securing SE9 personnel is proving difficult, and a significant increase in SEs commensurate with the number of maintenance service contracts is not realistic. Therefore, an increase in the number of maintenance service contracts is expected to significantly increase the workload on the field, including SE9. For example, there may be cases where a failure is predicted, but the necessary response within two weeks is not possible, leading to a failure. If a failure occurs, SE9s must perform maintenance work within two hours, further increasing the workload on the field.

[0030] While having SE9 perform maintenance on air conditioners with the highest urgency can reduce the number of cases leading to malfunctions, currently, appropriate prioritization of maintenance work based on fault prediction is not being carried out.

[0031] Therefore, the maintenance service of this embodiment aims to reduce the number of cases leading to failure and to reduce the workload on site by assigning appropriate priorities to maintenance work in response to failure prediction. The applicant focused on an index called COP (Coefficient of Performance), which represents the operating efficiency of the air conditioner, as a method for determining priority. COP is the air conditioning capacity per unit of power.

[0032] Figure 2 is a scatter plot of load factor and COP. The COP and load factor in Figures 2(a) and (b) were calculated from operating data of the same model. Figure 2 shows that there is a correlation between a decrease in COP and air conditioner failure. Figures 2(a) and (b) show the COP 301 under normal conditions and the COP 302 three months before failure, respectively. It can be seen that the COP 302 three months before failure is decreasing compared to the COP 301 under normal conditions. Therefore, it is thought that detecting a decrease in COP can improve the accuracy of prioritizing maintenance work.

[0033] However, since COP varies greatly depending on the surrounding external environment and how the air conditioner is used, the distribution of COP differs significantly even for the same model, as shown in Figures 2(a) and (b). Furthermore, the distribution of COP302 three months before failure and COP301 under normal conditions overlap, making it difficult to determine whether COP has decreased (determining the threshold).

[0034] Therefore, in this embodiment, a regression model is created in which the learning device predicts the COP under normal conditions. When a failure is predicted and the decrease in COP is used to determine the priority of maintenance work, the decrease in COP is determined by comparing the predicted value of the COP under normal conditions output by the regression model (an example of predicted air conditioning capacity, hereinafter referred to as predicted COP) with the measured COP (an example of measured air conditioning capacity, hereinafter referred to as measured COP).

[0035] Figure 3 is a schematic flowchart illustrating the process for determining the priority of maintenance work when a failure is predicted.

[0036] Although many components are used in an air conditioner, the server device 60 can repeatedly acquire operating data output by each component. The server device 60 applies the operating data to the predictive detection logic to detect signs of failure for each component (S1). The predictive detection logic is to use a rule-based approach, but a predictive detection model may also be used.

[0037] Furthermore, the server device 60 calculates the COP when a failure is predicted (S3). The server device 60 has a COP normality judgment model, which will be described later. The COP normality judgment model is a model that predicts the COP under normal conditions. The server device 60 determines whether the COP has decreased or not by comparing the predicted COP output by the COP normality judgment model with the measured COP calculated from the operating data.

[0038] The server device 60 then uses COP to determine the priority of maintenance work for the component whose failure has been predicted (S3). For example, if the COP is degraded, the server device 60 will prioritize the maintenance work for the component whose failure has been predicted higher than if the COP were not degraded.

[0039] The server device 60 outputs basic information and priorities for air conditioners requiring maintenance (S4). For example, suppose an air conditioner belonging to customer A is predicted to fail and its COP has decreased. Suppose an air conditioner belonging to customer B is predicted to fail but its COP has not decreased. In this case, the server device 60 will set the maintenance priority for the air conditioner belonging to customer A higher than the maintenance priority for the air conditioner belonging to customer B.

[0040] This allows system engineers (SEs) to perform maintenance on air conditioners with the highest urgency first, reducing the number of cases that lead to failure. Because SEs can perform maintenance according to priority, the workload on the field is reduced.

[0041] Furthermore, the server device 60 does not determine priority solely based on whether or not the COP is degraded, but can determine multiple levels of priority depending on the degree to which the COP is degraded.

[0042] <About Terminology> A malfunction refers to a state in which equipment experiences some kind of abnormality, deterioration, or defect, resulting in a decline in the performance or quality of the equipment. A malfunction means that the air conditioner cannot be operated without maintenance work, or even if it can be operated, there is a high probability that it will soon become inoperable. Alternatively, a malfunction means that even if it is operated, it cannot perform its intended function of controlling the temperature of the space. A malfunction is sometimes referred to as an "abnormality." In contrast, a sign of a malfunction refers to a state that is different from the normal state, even though a malfunction has not yet occurred.

[0043] Maintenance work involves taking measures to return an air conditioner that has malfunctioned or is predicted to malfunction back to a state where it is not malfunctioning or predicted to malfunction. This includes tasks such as repair, maintenance, preservation, and inspection.

[0044] Air conditioning capacity refers to the amount of heat that an air conditioner can provide per unit of time during cooling or heating. In this embodiment, COP, which is the cooling or heating capacity per 1 kW of power consumption, is used as the air conditioning capacity.

[0045] A record refers to one row of data when data is arranged in a two-dimensional table. One column of data is a column. In this disclosure, different types of operational data are arranged in the row direction, and operational data is arranged chronologically in the column direction.

[0046] <System configuration of the predictive behavior detection system> Next, we will explain the system configuration of the predictive maintenance system, referring to Figure 4. Figure 4 is a diagram showing an example of the system configuration of the predictive maintenance system.

[0047] The predictive maintenance system provides a variety of IoT-based services to users, from administrators to general users, by enabling communication between various devices 30 and a cloud-based server device 60 via a network N. The edge devices 10, devices 30, sensor switches 20, and user terminals 5 are mainly located on the customer's side, while the server device 60 is located in a data center or the cloud via the internet. The user terminal 5 may be located at the maintenance service company to which the system engineer (SE) belongs, or it may be the SE's mobile terminal.

[0048] Equipment 30 refers to all devices that consume power, such as air conditioners 40, security equipment, heat source equipment, fire alarms, AHUs (air handling units), electricity meters, and lighting. Equipment 30 may also include other devices. Sensor switches 20 include various sensors, lamps, relays, etc. Equipment 30 and sensor switches 20 are connected to the edge device 10 so as to be able to communicate via a dedicated cable or a network such as a LAN. Equipment 30 and sensor switches 20 may also be connected to the edge device 10 so as to be able to communicate via wireless communication.

[0049] The equipment 30 and sensor switches 20 are controlled by the edge device 10. In other words, the edge device 10 performs the necessary operations on the equipment 30 and sensor switches 20 to suit the purpose of the equipment 30 and sensor switches 20. The content of the control varies depending on the type of equipment 30 and sensor switches 20, but for example, if the equipment 30 is an air conditioner, it may include all control related to the functions of the air conditioner, such as the cooling / heating mode, set temperature, airflow, humidity, and airflow direction that can be set on an air conditioner. The control may also include operating modes such as a pre-season inspection mode, microcontroller reset, operation stop, and function substitution.

[0050] Device 30 collects operational data specific to device 30 and transmits it to the edge device 10 mainly periodically. Periodically means, for example, once every minute, once every 10 minutes, once every 60 minutes, etc., but this can be set by the user or server device 60. Also, device 30 can transmit operational data to the edge device 10 upon request from the edge device 10 or user terminal 5. The operational data varies depending on the device 30, but for example, in the case of an air conditioner, it may include outside air temperature, load factor, outdoor unit operating time, compressor rotation speed, refrigerant high pressure pressure, low pressure pressure, refrigerant temperature, fan rotation speed, and microcontroller CPU temperature.

[0051] Furthermore, if device 30 detects an abnormality, it sends an abnormality code to the edge device 10. The abnormality detected by device 30 is one that makes it difficult to continue operation. Device 30 that detects an abnormality stops operation. The edge device 10 sends an abnormality code to the server device 60. The processing of the edge device 10 for sensor switches 20 can be the same. Sensor switches 20 mainly periodically send information about themselves to the edge device 10 and send abnormality codes. The timing of transmission is periodically, at a set date and time, during periods of low load, when the difference from the last image data exceeds a certain amount, etc.

[0052] The edge device 10 is a controller that controls the equipment 30 and sensor switches 20. The edge device 10 functions as a control device that controls the equipment 30 and sensor switches 20, an information processing device that processes operating data, etc., and a communication means for communicating with the server device 60. For example, the edge device 10 transmits various information from the equipment 30 to the server device 60 and receives instructions from the server device 60 according to the information. Alternatively, the edge device 10 can receive instructions from the server device 60 even if it does not transmit any information to the server device 60 (for example, when there are instructions from the user terminal 5 to the server device 60). The edge device 10 converts the instructions into appropriate instructions according to the models of the equipment 30 and sensor switches 20 and transmits them to the equipment 30 and sensor switches 20. The equipment 30 or sensor switches 20 may be connected to the network N without going through the edge device 10.

[0053] A server device 60 is one or more information processing units. Although Figure 4 shows one server device 60, the server devices 60 may be divided into several units according to their functions. Alternatively, the functions of the server devices 60 may be consolidated into a single information processing unit. Furthermore, multiple server devices 60 with the same functions may be provided, and these multiple server devices 60 may communicate with each other to process data, similar to a server cluster.

[0054] The server device 60 receives various information transmitted from the edge device 10 via the network N and generates necessary instructions. In this embodiment, the server device 60 detects signs of failure (fault prediction) or detects a failure based on operating data transmitted from the equipment 30 or sensor switches 20. The detection of signs of failure or failure may be performed by the edge device 10 or the equipment 30. The server device 60 determines the priority of maintenance work for the air conditioner 40 in which signs of failure have been detected. The server device 60 transmits the priority of maintenance work for the air conditioner 40 in which signs of failure have been detected to the user terminal 5. In addition, in response to an abnormal code from the equipment 30 via the edge device 10 (in response to fault detection by the equipment 30), the server device 60 instructs the edge device 10 to perform emergency operation regardless of the model of the equipment 30. The server device 60 can also transmit instructions to the equipment 30 to the edge device 10 according to the schedule and operations set by the user terminal 5.

[0055] Server device 60 also has the functionality of a web server. The web server responds to requests from client software (web clients) such as a web browser operated by the user and provides the client with screen information written in HTML files, XML, CSS files, JavaScript (registered trademark), etc. An application that uses the web mechanism in this way is called a web application.

[0056] Furthermore, it is preferable that the server device 60 supports cloud computing. Cloud computing refers to a usage model in which network resources are utilized without the user being aware of specific hardware resources. Cloud computing provides users with data and software that they previously used on their own computers, as a service via the network. By providing a web browser that runs on a personal computer or mobile device, and an internet connection environment, users can access a variety of services from any device.

[0057] The user terminal 5 is a terminal device that displays various screens provided by the server device 60. In this embodiment, the user terminal 5 may be used by a system engineer (SE), a customer administrator, or a general user. That is, the user terminal 5 may be used by a customer administrator or a general user. Alternatively, the user terminal 5 may be used by the administrator of the predictive maintenance system 100. The customer administrator is the person who performs maintenance and management that is not performed by general users who use the equipment 30 on a daily basis.

[0058] The user terminal 5 displays a variety of screens, but one example is a screen that notifies users of information about an air conditioner 40 that has shown signs of malfunction and the priority of maintenance work. Other screens include a list of equipment 30 and sensor switches 20 connected to the customer's edge device 10, an in-house map showing the locations of the equipment 30 and sensor switches 20, and operation screens for operating the equipment 30 and sensor switches 20.

[0059] User terminal 5 can be, for example, a PC (Personal Computer), smartphone, tablet, PDA (Personal Digital Assistant), or wearable PC (sunglasses type, wristwatch type, etc.). However, it only needs to have communication capabilities and be able to run a web browser. Alternatively, instead of a web browser, a dedicated native application for the predictive detection system 100 may run on user terminal 5.

[0060] <Example of placement of learning devices and predictive detection devices> Figure 5 illustrates an example of the arrangement of the predictive detection device 400 and the learning device 500 in the predictive detection system 100. The learning device 500 is an information processing device that generates a COP normal judgment model during the learning phase of machine learning. The predictive detection device 400 is an information processing device that performs fault prediction detection, judgment of COP degradation using the COP normal judgment model, and determination of maintenance work priorities.

[0061] In Figure 5(a), the learning device 500 is arranged to be connectable to the server device 60 and the edge device 10 via the network N. The predictive detection device 400 may be located on the air conditioner 40, the edge device 10, or the server device 60. The predictive detection device 400 may also be located on the network N independently of these. The functions of the predictive detection device 400 may be distributed across the air conditioner (the number 40, not shown in the figure), the edge device 10, or the server device 60.

[0062] In Figure 5(b), the learning device 500 is integrated with the server device 60. That is, the server device 60 also functions as the learning device 500. The predictive detection device 400 may be located on the air conditioner 40, the edge device 10, or the server device 60. The predictive detection device 400 may be located independently on the network N. The functions of the predictive detection device 400 may be distributed across the air conditioner 40, the edge device 10, or the server device 60.

[0063] In Figure 5(c), the learning device 500 is integrated with the edge device 10. That is, the edge device 10 also functions as the learning device 500. The predictive detection device 400 may be located on the air conditioner 40, the edge device 10, or the server device 60. The predictive detection device 400 may be located independently on the network N. The functions of the predictive detection device 400 may be distributed across the air conditioner 40, the edge device 10, or the server device 60.

[0064] In Figure 5(d), the air conditioner 40 has both a learning device 500 and a predictive detection device 400. In other words, the air conditioner 40 serves as both the learning device 500 and the predictive detection device 400. The predictive detection device 400 may be located independently of the air conditioner 40.

[0065] In all configuration examples, during the learning phase, the learning device 500 generates a COP normality judgment model using learning data (training operation data) acquired from the air conditioner 40. During the inference phase, the predictive detection device 400 predicts failures of the air conditioner 40 by, for example, applying a rule-based approach to the operation data acquired from the air conditioner 40. The predictive detection device 400 also calculates the measured COP using the COP normality judgment model and determines the priority of maintenance work by determining a decrease in COP.

[0066] <Hardware configuration of edge devices and server devices> Next, the hardware configuration of the edge device 10 will be described with reference to Figure 6. Figure 6 is a hardware configuration diagram of an example of the edge device 10. As shown in Figure 6, the edge device 10 includes a processor 201, memory 202, auxiliary storage device 203, I / F (Interface) device 204, communication module 205, and drive device 206. Each piece of hardware in the edge device 10 is interconnected via a bus 207.

[0067] The processor 201 has various computing devices such as a CPU (Central Processing Unit). The processor 201 reads various programs into memory 202 and executes them. The processor 201 corresponds to the control unit 110 that controls the entire edge device 10.

[0068] Memory 202 contains main memory devices such as ROM (Read Only Memory) and RAM (Random Access Memory). The processor 201 and memory 202 form a so-called computer, and the processor 201 executes various programs read into memory 202.

[0069] The auxiliary storage device 203 stores various programs and various data used when those programs are executed by the processor 201.

[0070] The I / F device 204 is a connection device that connects the edge device 10 to an example of an external device, such as equipment 30 and sensor switches 20.

[0071] The communication module 205 is a communication device for communicating with the server device 60 via the network N.

[0072] The drive device 206 is a device for setting the recording medium 208. The recording medium 208 here includes media that record information optically, electrically, or magnetically, such as CD-ROMs, flexible disks, and magneto-optical disks. The recording medium 208 may also include semiconductor memory that records information electrically, such as ROMs and flash memory.

[0073] The various programs to be installed on the auxiliary storage device 203 are installed, for example, when the distributed recording medium 208 is set in the drive device 206 and the various programs recorded on the recording medium 208 are read by the drive device 206. Alternatively, the various programs to be installed on the auxiliary storage device 203 may be installed by downloading them from the network N via the communication module 205.

[0074] On the other hand, Figure 7 shows an example of the hardware configuration of the server device 60. Since the hardware configuration of the server device 60 is generally the same as that of the edge device 10, this explanation will focus on the differences between the two. Furthermore, the hardware configuration of the learning device, which will be described later, will be the same as that of the server device 60.

[0075] The processor 221 reads various programs into memory 222 and executes them. The processor 221 corresponds to the control unit 210 that controls the entire server device 60.

[0076] The I / F device 224 is a connection device that connects the server device 60 to an external device, such as a display device 230 and an operating device 240. The display device 230 displays the internal status of the server device 60. The operating device 240 is used by the administrator of the server device 60 to input various instructions to the server device 60.

[0077] The communication module 225 is a communication device for communicating with the edge device 10 and the user terminal 5 via the network N.

[0078] <About the features> Next, with reference to Figure 8, the functional configuration of each device in the predictive detection system 100 will be described in detail. Figure 8 is an example of a functional block diagram that explains the functions of the predictive detection device 400 in the predictive detection system 100 by dividing them into blocks. The predictive detection device 400, user terminal 5, and learning device 500 are connected to each other via network N for communication. The user terminal 5 only needs to connect to the predictive detection device 400 when it needs to identify an air conditioner that has detected a predictive error, and may not be included in the predictive detection system 100. Also, the learning device 500 is not included in the predictive detection system 100 during the inference phase. The predictive detection device 400 may have its functions distributed and deployed on either the server device 60 or the edge device 10, or both.

[0079] <<Learning device>> The learning device 500 generates a COP normality judgment model during the learning phase of machine learning. As described above, the learning device 500 can be any information processing device. Details of the functions of the learning device 500 will be described later.

[0080] <<Predictive Warning Device>> The predictive detection device 400 includes an operation data acquisition unit 41, a predictive detection unit 42, a COP calculation unit 43, a COP reduction determination unit 44, a priority determination unit 45, a reception unit 46, and an output unit 47. Each of these parts of the predictive detection device is a function or means realized by any of the components shown in Figure 6 (or Figure 7) operating according to instructions from the processor 201 (or 221) in accordance with a program deployed from the auxiliary storage device 203 (or 223) to the memory 202 (or 222).

[0081] These components of the predictive detection device 400 are functions in the inference phase. The inference phase in this embodiment is a phase in which fault prediction is performed on the air conditioner 40 in operation. If a fault prediction is detected, the priority of maintenance work on the air conditioner 40 in which the fault prediction was detected is determined.

[0082] The operation data acquisition unit 41 repeatedly acquires operation data in real time from, for example, the air conditioner 40 while it is in operation. The acquisition period can be set according to the processing capacity of the predictive detection device 400 and the network bandwidth, for example, every minute. Furthermore, the acquisition period does not necessarily have to be regular.

[0083] The predictive detection unit 42 uses predictive detection logic (rule-based) to detect signs of failure in the air conditioner 40. When using a rule-based approach, the predictive detection unit 42 compares operating data with one or more thresholds and predicts a failure if the result is above or below the threshold. Alternatively, the predictive detection unit 42 may use a predictive detection model to detect signs of failure. In this case, the predictive detection unit 42 inputs operating data into the predictive detection model, calculates predicted values ​​under normal conditions, and determines whether the deviation between the measured values ​​and predicted values ​​included in the operating data is above a threshold. If the deviation is above a threshold, the predictive detection unit 42 predicts a failure.

[0084] The COP calculation unit 43 calculates a predicted COP using a COP normal judgment model when a fault is predicted, and calculates the actual COP using the operating data. The COP calculation unit 43 calculates the actual COP by dividing the cooling (heating) capacity calculated using the operating data by the power consumption.

[0085] The COP reduction determination unit 44 determines whether the measured COP has decreased compared to the predicted COP, based on the COP reduction determination criteria described later. If the measured COP has decreased compared to the predicted COP, it means that the likelihood of the fault-predicted component failing has increased.

[0086] The priority determination unit 45 determines the priority of maintenance work based on whether the actual COP has decreased relative to the predicted COP when a failure is predicted. In other words, the priority determination unit 45 sets a higher priority for maintenance work on an air conditioner 40 that has been predicted to fail and whose actual COP has decreased relative to the predicted COP, than for maintenance work on an air conditioner 40 whose actual COP has not decreased relative to the predicted COP.

[0087] The reception unit 46 receives confirmation from the user terminal 5 that the maintenance work has been completed when the SE (Systems Engineer) inputs this information to the user terminal 5. The reception unit 46 records the date and time the maintenance work was completed and is able to inform the SE or the customer how much the COP (Coefficient of Performance) improved before and after the maintenance work.

[0088] The output unit 47 outputs instructions for maintenance work with assigned priorities to the user terminal 5. The user terminal 5 may be a terminal device that can be viewed by the SE, or it may be a terminal device on the customer's side (in this case, the customer requests maintenance work from the SE). The output unit 47 may send the maintenance work instructions by email, or by SNS. The output unit 47 may also output the maintenance work instructions to a website displayed on the user terminal 5. Alternatively, the output unit 47 may push notifications of maintenance work instructions to applications running on the user terminal 5.

[0089] <About the functions of the learning device> Next, with reference to Figure 9, the functions of the learning device 500 will be described. Figure 9 is a functional block diagram of the learning device 500 according to one embodiment of the present disclosure. The learning device 500 generates a COP normal prediction model 12.

[0090] The learning device 500 includes a learning data acquisition unit 502, a learning data storage unit 503, and a learning unit 504. These functional units of the learning device 500 are functions or means realized by the processor 221 of the learning device 500 executing program instructions that have been loaded into the memory 222.

[0091] The learning data acquisition unit 502 acquires the learning data 501 and stores it in the learning data storage unit 503. The learning data 501 consists of one or more operating data points under normal conditions and further comprises multiple records. "Normal" means that there are no abnormalities in all thermistors, actuators, compressors, and expansion valves of the air conditioner 40 when it provides its function. Each record contains explanatory variables and objective variables for generating the COP normal prediction model 12. Details of these will be described later.

[0092] Furthermore, it is preferable for the learning data acquisition unit 502 to perform preprocessing on the learning data 501. Preprocessing includes, for example, removing transient data and operating data to which error codes are attached. Transient data refers to operating data acquired in a transient state where the room temperature of the space to be air-conditioned is not in a steady state.

[0093] The learning data storage unit 503 stores the operation data acquired by the learning data acquisition unit 502 and after preprocessing has been completed. The learning unit 504 learns from training data using various machine learning algorithms to generate a COP normal prediction model 12. The COP normal prediction model 12 is correspondence information that associates operating data corresponding to explanatory variables with COP. The COP normal prediction model 12 outputs a predicted COP for input operating data corresponding to explanatory variables. Such correspondence information can be realized by regression models. Regression models include multiple regression, gradient boosting decision trees, AdaBoost, neural networks, ridge regression, Lasso regression, and elastic network regression.

[0094] Note that the driving data includes multiple types of data, and all of them can potentially serve as the target variable.

[0095] <About the operating data> Refer to Figure 10 to explain the driving data in the training data. Figure 10(a) shows a list of driving data included in the training data.

[0096] As an example, let's assume that operating data A to Z are acquired from the air conditioner 40. Operating data A1, A2, ... are operating data A acquired in chronological order. A set of operating data collected from the air conditioner 40 at the same time is called a record. In Figure 10(a), operating data A1 to Z1 constitute one record, operating data A2 to Z2 constitute one record, ..., operating data A7 to Z7 constitute one record. In other words, one record contains multiple types of operating data. These include, for example, outside air temperature, compressor rotation speed, load factor, outdoor unit operating time, power consumption, and cooling / heating flags. In this embodiment, data calculated from operating data using formulas, such as COP and load factor, are also referred to as operating data.

[0097] Figure 10(b) shows an example of training data for generating the COP normal prediction model 12. This training data is either included in the operating data shown in Figure 10(a) or calculated based on the operating data. The dependent variable of the COP normal prediction model 12 is COP. The independent variables can be any operating data from A to Z that is suitable for explaining COP, such as the outside air temperature, compressor rotation speed, load factor, outdoor unit operating time, and cooling / heating flag shown in Figure 10(b). Other operating data may be included in the independent variables of the COP normal prediction model 12, and some independent variables may not be used.

[0098] The COP and load factor are calculated using the following formulas. COP = Cooling (or heating) capacity [kW] / Power consumption [kW] Load factor = Cooling (or heating) capacity [kW] / Rated capacity [kW] The COP and load factor may be included in the operating data transmitted by the air conditioner 40, or they may be calculated by the predictive detection device 400.

[0099] The formulas for calculating COP and load factor differ for cooling operation and heating operation, respectively, but since the explanatory variables include cooling / heating flags, the COP normal prediction model 12 is common to both cooling and heating operation. Separate COP normal prediction models 12 may be generated for cooling operation and heating operation. Preferably, the number of training data records includes more than one year's worth of data from the start of acquisition, and there are at least 1500 records for both cooling and heating operation. In addition, the COP normal prediction model 12 is generated for each system (same outdoor unit).

[0100] <Regression Model> This section describes the COP normality prediction model 12 using a regression model.

[0101] For example, in the case of multiple regression, the dependent variable and independent variables are associated as follows: y = β0 + β1x1 + β2x2 + ... + β n x n ...(1) y is the dependent variable, x1~x n These are explanatory variables. In the case of the COP normal prediction model 12, the dependent variable is COP, and the explanatory variables are outside air temperature, compressor rotation speed, load factor, outdoor unit operating time, power consumption, and cooling / heating flags. β0~β n These are the coefficients of the explanatory variables. Multiple regression analysis is performed to obtain β0~β n This is determined. The model shown in equation (1) above is the COP normal prediction model 12. Note that a predictive detection model can be generated in a similar manner.

[0102] As shown in Figure 11, the learning device 500 may generate a COP normal prediction model 12 using a neural network. Figure 11 shows a COP normal prediction model 12 composed of a neural network 170. The neural network 170 is a form of artificial intelligence (AI) model that trains a computer to process data in a way that mimics the workings of the human brain. An existing configuration will be used for the neural network 170.

[0103] In Figure 11, the inputs to the neural network are the outside air temperature, compressor rotation speed, load factor, outdoor unit operating time, power consumption, cooling / heating flag, and bias (set to 1). The output is the target variable (e.g., the predicted value of COP). The COP normal prediction model 12 has an input layer 171, a hidden layer 172, and an output layer 173. In the neural network 170 of Figure 11, three layers (excluding the input layer) are fully connected from the nodes of the input layer 171 to the nodes of the output layer 173. A neural network with deep layers is called a DNN (Deep Neural Network). The layer between the input layer 171 and the output layer 173 is called the hidden layer 172. The number of nodes in the hidden layer 172 is merely an example.

[0104] Weights are assigned to the connections between nodes, and the output from a node multiplied by its weight is passed to the node in the next layer. The node in the next layer receives the outputs of all the nodes in the previous layer, so it sums up the outputs of all the nodes in the previous layer. The node in the next layer activates the summed output with an activation function and passes it to the next node. This process is repeated until the values ​​are passed up to output layer 173.

[0105] In this embodiment, since we want to infer the predicted value of the target variable, the COP normal prediction model 12 is a regression model (a classification model is another option). Therefore, the output layer 173 is provided with one node that outputs the predicted value of the target variable.

[0106] It should be noted that the learning phase of the COP normal prediction model 12 was performed using existing methods such as backpropagation. The COP normal prediction model 12 learns the correspondence between explanatory variables (outside air temperature, compressor rotation speed, load factor, outdoor unit operating time, power consumption, and cooling / heating flag) and the target variable (COP).

[0107] In the inference phase using the COP normal prediction model 12, for example, current operating data (such as outside air temperature) transmitted from the operating air conditioner 40 is input to the input layer 171. The output layer 173 calculates (infers) a predicted value of COP. By repeatedly outputting such predicted values, a predicted value associated with time is obtained.

[0108] The neural network structure can be the same for predictive maintenance models. In predictive maintenance models, the input to the input layer is operational data B, C, D, etc., and the output is the predicted value of operational data A.

[0109] Next, we will explain the regression model using gradient boosting decision trees, referring to Figures 12 to 14. Figure 12 is a flowchart illustrating the learning process using gradient boosting decision trees. Figure 13 shows an image of the decision tree. Figure 14 is the training data used to explain the gradient boosting decision tree. In Figure 14, the target variable is, for example, COP, and explanatory variables 1 and 2 are the outside air temperature and compressor rotation speed. Other explanatory variables (load factor, outdoor unit operating time, power consumption, cooling / heating flag) have been omitted. Also, the numerical values ​​in Figure 14 are for illustrative purposes only and differ from actual values.

[0110] The learning unit 504 calculates initial values ​​from the values ​​of multiple target variables (e.g., COP) prepared as training data (S11). The initial values ​​are, for example, the mean values. For the sake of explanation, this mean value is called the predicted value 1.

[0111] Next, the learning unit 504 calculates an error of 1 for each record of the training data by subtracting the mean value from the value of the target variable (S12). An error of 1 is positive if the value of the target variable is greater than the mean, and negative if it is smaller. The reason why "the value obtained by subtracting the mean value from the value of the target variable" is used as the error is because the loss function is the squared error. The gradient obtained by differentiating the squared error is taken as the error. Alternatively, the absolute value error may be used as the error of 1.

[0112] Next, the learning unit 504 constructs a decision tree for the purpose of predicting error 1 (S13). In other words, it classifies error 1 using a decision tree. The number of nodes and hierarchy of the decision tree may be limited to a predetermined range. This decision tree is a weak classifier (boosting). The top tree in Figure 13 is an image of the decision tree that is initially created. Figure 13 is an image, but error 1 is classified into the leaves (ends of the tree) of such a decision tree. In Figure 13, errors 1 (-35, -25, -15, -5, 5, 15, 25, 35) are classified into leaves. That is, a threshold is set for the nodes of the decision tree, and error 1 is classified into leaves by comparing whether explanatory variable 1 or 2 is greater than or less than the threshold for each node. If multiple errors are classified into one leaf, the average of the errors contained in that leaf is the error of that leaf.

[0113] Let's briefly explain how decision trees are constructed. (i) The learning unit 504 processes the nodes in order from the one closest to the root. First, the learning unit 504 calculates the entropy of the training data before classification. The learning unit 504 may use the Gini coefficient instead of entropy. (ii) The learning unit 504 sorts the training data by the values ​​of an arbitrary explanatory variable. The learning unit 504 calculates the entropy of the training data classified into the two target branches, while changing the threshold for dividing the values ​​of the explanatory variable. The learning unit 504 performs this process for all attributes (explanatory variables 1 and 2). (iii) The learning unit 504 calculates the weighted average of the entropies in (ii). This weighted average may be calculated by multiplying the number of data points before classification by the ratio of the number of data points classified into each branch. (iv) The learning unit 504 adopts the attribute that maximizes the difference (gain) between (i) and (iii) as the root attribute, and also determines the threshold for each node. Thus, the attribute that maximizes the gain and the threshold are determined as the classification condition for each node. (v) The structure of the lower part of the branch to be classified is also determined by the processes (i) to (iv).

[0114] Next, the learning unit 504 calculates a new predicted value using error 1 (S14). Since error 1 is classified for each sample of the training data in the leaves of the decision tree, a predicted value 2 can be calculated using error 1 for each sample of the training data. Note that although we used "error 1" here, "error n" increases with each iteration.

[0115] If we denote the new predicted value as Predicted Value 2, then "Predicted Value 2 = Predicted Value 1 + Learning Rate * Error 1". The learning rate is a value less than 1 and is a hyperparameter that determines how much the error is corrected in a single decision tree iteration. The learning rate is determined appropriately, for example, between 0.05 and 0.3. In Figure 14, the learning rate was set to 0.1. By repeatedly performing the process of "calculating the error, multiplying it by the learning rate, and adding it up," the accuracy gradually improves.

[0116] Next, the learning unit 504 calculates the error from the value of the target variable and the predicted value 2 (S15). The learning unit 504 calculates the error between the value of the target variable and the predicted value 2 from all the prepared training data. The error calculated from the predicted value 2 is called error 2.

[0117] The learning unit 504 repeats steps S13 to S15 (S16) until it has calculated an error a certain number of times or until the error falls below a threshold. In the second tree in Figure 13, error 2 is classified. The learning unit 504 classifies the previous error using a new decision tree and calculates predicted values ​​3, 4...n and errors 3, 4...n using the errors. Therefore, in the third tree, error 3 is classified. As the error changes with each repetition of the process, the structure of the decision trees also changes automatically. The n decision trees created in this way constitute a regression model using gradient boosting decision trees.

[0118] Various frameworks for implementing gradient boosting decision trees in information processing systems are known, including LightGBM (Light Gradient Boosting Machine), XGBoost (eXtreme Gradient Boosting), and Catboost (Category Boosting). The learning unit 504 may use one of these frameworks.

[0119] This section describes inference using gradient boosting decision trees. The COP calculation unit 43 inputs operating data corresponding to explanatory variables obtained from the operating air conditioner 40 into all decision trees created in the learning phase. Therefore, the operating data is classified into one of the leaves according to the classification result of explanatory variable 1 or 2 based on the threshold set in the node. Since explanatory variables classified into the same leaf can be assumed to have similar target variables, the predicted value can be inferred from the error of the classified leaf. In Figure 13, there are three decision trees, and the operating data is classified into one leaf of each decision tree based on the value of explanatory variable 1 or 2. Each leaf stores the error with the predicted value. For example, suppose the data is classified as operating data in leaves 321 to 323, indicated by the dotted circles in Figure 13. The COP calculation unit 43 estimates the predicted value of the target variable (COP) by summing the values ​​obtained by multiplying the errors of these leaves by the learning rate against the average value calculated in the learning phase across all decision trees.

[0120] Predicted value of the target variable = Mean + Learning rate × Error of leaf 321 + Learning rate × Error of leaf 322 + Learning rate × Error of leaf 323 = 135 + 0.1 × (-5) + 0.1 × (-22.5) + 0.1 × (32) Thus, in a gradient boosting decision tree, the final predicted value is obtained by multiplying the learning rate by the error of the leaf in the classification target for each decision tree and adding it to the average value calculated during the learning phase.

[0121] <Methods for detecting signs of failure> Next, a method for detecting signs of failure will be described. In the case of failure prediction using a rule-based approach, the sign detection unit 42 compares the operation data with a threshold value, and detects a sign of failure when the value is greater than or equal to the threshold value or less than the threshold value. The sign of failure using a sign detection model is determined based on, for example, the degree of deviation between the predicted value output by the sign detection model and the measured value. The method for calculating the degree of deviation is as follows, for example. Degree of deviation = Predicted value - Measured value Degree of deviation = (Predicted value - Measured value) / Predicted value Degree of deviation = Measured value / Predicted value By quantifying the degree of deviation between the predicted value and the measured value in this way, the difference between the two can be obtained. The sign detection unit 42 detects a sign of failure when the degree of deviation becomes greater than or equal to the threshold value. The object of detection of the sign of failure is the component that outputs the measured value deviated from the predicted value.

[0122] <COP reduction judgment criteria> Next, referring to FIG. 15, the COP reduction judgment criteria will be described. FIG. 15(a) is a diagram schematically explaining the COP reduction judgment criteria. FIG. 15(b) is a specific example of the threshold value. The horizontal axis in FIG. 15(a) is the judgment period, and the predicted COP 18 and the measured COP 19 are shown on the vertical axis. First, the terms used in the conditions of the COP reduction judgment criteria will be described. (1) Judgment window frame 15 The judgment window frame 15 is a period obtained by dividing the judgment period (for example, three months) into one-week intervals, and is a unit for evaluating COP reduction. The judgment window frame 15 is, for example, N1 [days]. (2) Reduction criterion 16 The reduction criterion 16 is a reference value regarding how much the measured COP 19 has decreased as seen from the predicted COP 18. The reduction criterion 16 is represented by, for example, σ (standard deviation) × r (r is a real number). The judgment criterion is, for example, N2 × σ (N2 times the standard deviation). Details will be described in FIG. 16. (3) Reduction rate 17 The reduction rate 17 is the ratio of the number of data that satisfy the reduction criterion 16 within the judgment window frame 15. The reduction rate 17 is, for example, N3 [%].

[0123] When a fault is predicted, the COP reduction determination unit 44 acquires operating data from past determination periods and makes a determination on COP reduction. The COP reduction determination unit 44 determines that COP has decreased if there is at least one (1) determination window frame 15 that satisfies (2) reduction criteria 16 and (3) reduction ratio 17. Conversely, if there are no (1) determination window frames 15 that satisfy (2) reduction criteria 16 and (3) reduction ratio 17, it determines that the COP is normal.

[0124] By providing multiple criteria for determining COP decline, as shown in Figure 15, the COP decline determination unit 44 can also determine the degree to which COP has declined. The priority determination unit 45 may determine multiple levels of priority according to the degree of COP decline. The COP decline determination criteria can be any predetermined criteria that can determine COP decline, and are not limited to those shown in Figure 15.

[0125] Refer to Figure 16 to explain the reduction criterion 16. As described above, COP can vary greatly depending on the surrounding external environment and how the air conditioner is used, so the measured COP 19 can fluctuate even under normal conditions. For this reason, it is preferable to understand how much the measured COP 19 fluctuates and to judge that the measured COP 19 has decreased compared to the predicted COP 18 when the measured COP 19 has decreased significantly more than under normal conditions.

[0126] Figure 16 shows the histogram of "predicted COP - measured COP". Figure 16(a) is the histogram when COP is normal, and Figure 16(b) is the histogram when COP is decreased. Comparing the two, the histogram when COP is decreased is larger than the histogram when COP is normal, in the range of "0.2 to 0.7" between the predicted and measured values. Therefore, it is conceivable to use "0.2 to 0.7" as the criterion for decrease 16, but since "0.2 to 0.7" is a range of values, it is difficult to adopt it as a criterion value for measured COP 19, which fluctuates even in normal conditions. Therefore, we will use r, expressed as the standard deviation σ × r, as the criterion for decrease 16.

[0127] This standard deviation is the standard deviation of the predicted COP output when the COP normal prediction model 12 is given the same explanatory variables as the training data. In other words, it is the standard deviation of the COP normal prediction model 12. The COP reduction judgment unit 44 gives the COP normal prediction model 12 the same explanatory variables as the training data, calculates "predicted COP - measured COP", and calculates the standard deviation σ of the calculation result. When operating data is acquired from the air conditioner 40 in operation, it is determined that (2) is satisfied if the measured COP 19 is smaller than the predicted COP 18 output by the COP normal prediction model 12 by a standard deviation of σ × N² or more.

[0128] <Determining the Prioritization of Maintenance Work> Next, we will explain how to determine the priority of maintenance work based on the assessment of COP reduction when a failure is predicted.

[0129] Figure 17 shows statistical data on the time elapsed from component failure prediction to actual failure. The horizontal axis represents the time elapsed from failure prediction to actual failure: within 1 week, within 2 weeks, within 1 month, within 3 months, and longer. The left vertical axis represents the number of failures, and the right vertical axis represents the cumulative percentage of failures.

[0130] The horizontal axis of the graph shows, using bar graphs, the number of actual failures (number of air conditioners) that occurred after failure prediction during the period. The bar graph on the left, 21, shows the number of actual failures when the COP was low, while the bar graph on the right, 22, shows the number of actual failures when the COP was normal. Comparing the bar graphs on the left (21) and right (22), it can be seen that air conditioners 40 with a low COP have a higher number of failures after failure prediction.

[0131] Figure 17 shows the cumulative percentage 23 when COP is low and the cumulative percentage 24 when it is normal. Cumulative percentages 23 and 24 are the cumulative values ​​of the percentage of failures that occur by the relevant period. The percentage is calculated as "number of air conditioners that actually failed / number of air conditioners that were predicted to fail". The cumulative percentage 23 when COP is low is consistently higher than the cumulative percentage 24 when it is normal. From this, it can be seen that the time from failure prediction to actual failure is shorter for air conditioners 40 with a low COP.

[0132] Therefore, if the COP normal prediction model 12 determines that the COP is decreasing, it is effective to prioritize maintenance work. For example, if a failure is predicted and the COP is determined to be decreasing, the output unit 47 outputs a message to the user terminal 5 suggesting maintenance work for the air conditioner. For example, to a system engineer, the message suggests: "Signs of a failure have been detected in the air conditioner. The COP is also decreasing, so the priority is 'medium'. Please come and perform maintenance work within two weeks." For example, to a customer, the message suggests: "Signs of a failure have been detected in the air conditioner. The COP is also decreasing, so the priority is 'medium'. Please request maintenance work from a service engineer."

[0133] More preferably, when a fault is predicted and it is determined that the COP is in a low state, the output unit 47 outputs the following information to the SE. "Statistical data shows that X percent of air conditioners will break down within X months." Looking at the cumulative percentage 23 in Figure 17, approximately 40% of the air conditioners 40 have failed within one month, so information such as "Statistical data shows that 40% of air conditioners 40 will fail within one month" is sent to the user terminal 5. In addition, information such as "80% of air conditioners 40 will fail within one month" may be output, depending on the statistical data of the parts whose failures have been predicted. In this way, the output unit 47 can output the predicted timing of failure and the probability of failure occurring.

[0134] As shown in Figure 17, statistical data is obtained for each component of air conditioners in the same system (same outdoor unit), so the output unit 47 can create instructions for each component based on the statistical data. In addition, the information output to the user terminal 5 may only include the predicted timing or the probability of failure occurring, as shown below. "Statistical data shows that it will break down within X months." "Statistical data shows that X percent of air conditioners break down." Figure 18 illustrates a notification screen 330 that notifies the user of a predicted failure and the component it identifies. The user terminal 5 can display a notification screen 330 like the one shown in Figure 18 by connecting to the predictive detection device 400. The output unit 48 generates the notification screen 330 and displays it on the user terminal 5. On the notification screen 330, the columns represent the component type, and the rows represent whether a failure has been predicted, the 80% failure period, the latest prediction date and time, and the priority. Figure 18(a) shows the notification screen 330 when a decrease in COP is detected.

[0135] • The "Fault Prediction Status" section indicates whether the predictive detection unit 42 has predicted a fault. "None" means no fault prediction was made, and "×" means fault prediction was made (indicating that a fault was predicted). "△" indicates that the predictive detection unit 42 predicted a fault, but the number of predictions was below a specified value.

[0136] The 80% failure period is the time it takes for 80% of the predicted failure air conditioners 40 to fail. As described above, the 80% failure period is determined from statistical data (cumulative percentage 23). Figure 18(a) shows that the 80% failure period for thermistor failure prediction is "1 month". Also, the "8" in "80%" may differ depending on the component. In this case, the column for the 80% failure period will display information such as "Statistical data shows that × percent of air conditioners will fail within ○ months".

[0137] • The latest predicted date and time is the date and time when the failure was last predicted. The first predicted date and time may be displayed separately from the latest predicted date and time, or together with the latest predicted date and time.

[0138] • The priority displayed is the priority determined by the priority determination unit 45. Since the COP is low, the priority is "medium". The priority may be displayed in three stages, such as high, medium, and low, or in five stages, such as 1 to 5.

[0139] Figure 18(b) shows the notification screen 330 when no COP degradation is detected (normal). The screen configuration is the same as in Figure 18(a), but the 80% failure period is "3 months" and the priority is "low". "3 months" is determined by the cumulative ratio 24 of the statistical data. A priority of "low" is sufficient if the priority is lower than the priority when COP is degraded. For example, the priority when COP is degraded may be "high" and the priority when COP is not degraded may be "medium".

[0140] The predictive maintenance device 400 of this disclosure prioritizes maintenance work on an air conditioner 40 if a failure is predicted in the air conditioner 40 and the COP is decreasing, higher than maintenance work on an air conditioner 40 whose COP is not decreasing. This improves the accuracy of prioritizing maintenance work on an air conditioner 40 where a failure has been predicted. The system engineer (SE) can determine the failure response schedule by looking at the 80% failure period, the latest predicted date and time, and the priority of each component.

[0141] <Presentation of the effects of maintenance work> When a failure is predicted, the air conditioner continues to operate. Therefore, if a system engineer (SE) performs maintenance work when a failure is predicted, the air conditioner 40 will continue to operate as usual, making it difficult for the customer to understand what changes were made as a result of the SE's work.

[0142] Therefore, if the COP (Coefficient of Performance) was low during the fault prediction stage, the output unit 47 could compare the COP before and after the maintenance work is completed and present this comparison to the customer. Since the SE (Systems Engineer) repair restores the declining COP to a normal value, the customer can confirm that the SE repair was indeed performed and that the COP has improved by comparing the COP before and after the repair.

[0143] Figure 19 shows an example of COP before and after maintenance work. In Figure 19, the horizontal axis represents the date and time, and the vertical axis represents the COP. SE performed maintenance work on the air conditioner 40 on date and time A. SE inputs that the maintenance work is complete into the user terminal 5, and the user terminal 5 transmits this information to the predictive maintenance device 400. The receiving unit 46 of the predictive maintenance device 400 records the parts that have been maintained, associating them with the date and time.

[0144] The maintenance work targets a component for which failure has been predicted, for example, the cleaning of an external heat exchanger. Up until date and time A, there is a large discrepancy between the predicted COP18 and the measured COP19, but after date and time A, the discrepancy becomes smaller. In this way, the output unit 47 can demonstrate to the customer that the repair was carried out properly, thereby improving customer satisfaction with the maintenance service.

[0145] Furthermore, instead of outputting the predicted COP18 and measured COP19 before and after the repair, the output unit 47 may output information regarding the air conditioning capacity of the air conditioner. For example, the output unit 47 may display a message such as "The COP of case XX has recovered to a normal level" or send a notification via email. Such a message can also show the customer that the repair was carried out properly, thereby improving customer satisfaction with the maintenance service.

[0146] <Presentation of COP reduction due to equipment degradation> Interest in energy conservation has been growing, and there is a need to reduce electricity costs. Currently, the measures that customers can take to reduce electricity costs are those that may affect comfort, such as changing the temperature setting.

[0147] For example, if the COP (Coefficient of Performance) is low, electricity costs can be saved by restoring it to normal through maintenance work. Therefore, it is considered effective to show customers that electricity costs will be reduced by restoring the air conditioner 40 to a normal state. When the COP is low, the output unit 47 can visualize how much wasted electricity is being generated compared to a normal state, which encourages repairs and leads to energy savings.

[0148] FIG. 20 shows an example of the measured COP19 and predicted COP18 for the most recent three months of a certain air conditioner 40. The horizontal axis in FIG. 20 represents the date and time, and the vertical axis represents the COP. The divergence between the predicted COP18 and the measured COP19 has started since around time B. Therefore, it is considered that the COP decrease determination unit 44 detected the decrease in COP around time B. Also, it is assumed that a failure is predicted during a period not far before and after the time when the COP decrease determination unit 44 detected the decrease in COP.

[0149] The COP calculation unit 43 calculates, for example, the average of the predicted COP and the measured COP for the past three months. The average of the COP for the past three months is shown in FIG. 21. As an example, the average of the predicted COP18 is R2, and the average of the measured COP19 is R1 (R1 < R2).

[0150] Since the refrigeration (heating) capacity and the power consumption are known for the calculation of the measured COP19, the power consumption is also known. The output unit 47 can estimate the electricity cost by multiplying the power consumption by the electricity unit price and the period. If the electricity cost for the measured COP19 is the electricity cost P, the electricity cost Q for the predicted COP18 is calculated as follows. Q = (R1 / R2) × electricity cost P In this way, by presenting to the customer by the output unit 47 that the electricity cost can be reduced by returning the air conditioner 40 to a normal state through maintenance work, maintenance can be promoted. As a customer, it is also possible to numerically determine whether to request maintenance.

[0151] <COP decrease rate> It is known that the content of the failure and the period until the occurrence of the failure change depending on whether the COP decreases rapidly or gradually. Therefore, the COP decrease determination unit 44 can determine the priority of the maintenance work by determining the decrease rate of the COP.

[0152] Figure 22 shows a comparative example of the rate of decline in COP. Figure 22(a) shows a rapidly declining COP, and Figure 22(b) shows a gradually declining COP. In both cases, the predicted COP 18 shows little fluctuation, but the measured COP 19 is declining, and in Figure 22(a), the slope of decline is steeper. Considering the need for early fault response, the period for determining the rate of decline can be set to approximately N1 [days], the same as the judgment window 15.

[0153] Figure 23 shows the correspondence between the COP decline rate, failure timing, and failure location. Statistically, a higher COP decline rate is associated with a shorter time to failure. Furthermore, statistically, failures associated with a high COP decline rate are often related to the refrigerant system (e.g., refrigerant leaks). Components that are highly likely to fail when the COP decline rate is high are pre-configured in the predictive detection device 400.

[0154] The priority determination unit 45 raises the priority of maintenance work for a component that has been predicted to fail, if that component is particularly likely to fail when the COP (Coefficient of Performance) decreases, compared to when only a decrease in COP is detected.

[0155] <Operating Procedure> Figure 24 is a flowchart illustrating the method for determining priority when a failure is predicted. The process in Figure 24 may be executed each time the predictive detection device 400 acquires operating data from the air conditioner 40 that is in operation. Note that the method for determining priority in Figure 24 is merely an example and may vary depending on various factors such as the component, the degree of COP degradation, and the property.

[0156] First, when the operation data acquisition unit 41 acquires operation data, the predictive detection unit 42 uses predictive detection logic to detect signs of failure. Failure prediction is sufficient if it is detected by either a rule-based or predictive detection model. The predictive detection unit 42 determines whether or not a failure has been predicted for each component (S21). If no failure is predicted (No. in S21), the process in Figure 24 ends.

[0157] If a failure is predicted (Yes in S21), the COP calculation unit 43 calculates the predicted COP 18 and the measured COP 19 (S22).

[0158] The COP reduction judgment unit 44 determines whether the measured COP 19 has decreased based on the COP reduction judgment criteria (S23).

[0159] If the measured COP19 has not decreased (No. in S23), the priority determination unit 45 determines that the priority is "low" (S27).

[0160] If the measured COP19 is decreasing (Yes in S23), the COP decrease determination unit 44 determines whether the rate of decrease of the measured COP19 is above a threshold and whether the faulty component is part of the refrigerant system (S24). The faulty component may be a component other than the refrigerant system if it is a component that is prone to failure when the COP decreases. Alternatively, in step S24, it may be determined only whether the rate of decrease of the measured COP19 is above a threshold, without determining whether the component is prone to failure when the COP decreases.

[0161] If the decision in step S24 is Yes, the priority determination unit 45 determines that the priority is "High" (S25). If the decision in step S24 is No, the priority determination unit 45 determines that the priority is "Medium" (S26).

[0162] Figure 25 is a flowchart illustrating the process performed by the predictive maintenance device 400 when a system engineer (SE) responds to a fault prediction. Note that the flow in Figure 25 is just one example and may vary depending on various factors such as the nature of the fault and customer requirements.

[0163] If a malfunction is predicted, the output unit 47 notifies the SE by sending an email to the user terminal 5 containing the URL of the predictive detection device 400. When the user terminal 5 connects to the predictive detection device 400, the output unit 47 causes the user terminal 5 to display a notification screen 330 as shown in Figure 18 (S31).

[0164] The SE looks at the notification screen 330, determines which air conditioners 40 and their parts have high priority, and performs maintenance work. If an individual contract is required for the maintenance work, the SE should present the customer with a graph comparing the measured COP19 and predicted COP18, as shown in Figure 20, or the average value of the measured COP19 and predicted COP18 (the electricity cost may also be presented), as shown in Figure 21. In this case, the SE inputs an operation to display the information in Figures 20 and 21 into the user terminal 5, and the user terminal 5 obtains this information from the predictive maintenance device 400.

[0165] When the SE completes the maintenance work, it inputs a message to the user terminal 5 indicating that the maintenance work has been completed. The user terminal 5 transmits the parts that were maintained and the fact that the maintenance work has been completed to the predictive maintenance device 400. The receiving unit 46 of the predictive maintenance device 400 associates the maintained parts with the date and time and saves the message that the maintenance work has been completed (S32).

[0166] For example, if a certain period of time (e.g., 3 months) has elapsed since maintenance work, the COP calculation unit 43 calculates the measured COP 19 and predicted COP 18 after the maintenance work based on the operating data after the maintenance work. The COP reduction determination unit 44 determines whether the COP has improved or not (S33). In other words, it determines whether the measured COP 19 after the maintenance work has reached a state where it is not judged to have decreased, based on the COP reduction determination criteria.

[0167] If the determination in step S33 is Yes, the output unit 47 of the predictive detection device 400 presents the COP before and after the maintenance work to the customer (S33). The timing of the presentation may be when the SE visits the customer again. If a certain period of time has elapsed since the maintenance work, the output unit 47 of the predictive detection device 400 may send an email to the customer, and the COP before and after the maintenance work may be presented to the customer in that email. Alternatively, the output unit 47 may include a URL in the email, and when the customer's user terminal 5 connects to the URL, the COP before and after the maintenance work may be presented to the customer.

[0168] <Main effects> The predictive maintenance device 400 of this disclosure determines whether the COP (Coefficient of Performance) has decreased when a failure is predicted in an air conditioner 40. If the COP has decreased, the priority of maintenance work on the air conditioner 40 is set higher than that of maintenance work on air conditioners 40 whose COP has not decreased, thereby improving the accuracy of the priority assigned to the air conditioner 40 in which a failure has been predicted. The system engineer (SE) can perform maintenance work on the air conditioners 40 with the highest urgency first, reducing the number of cases that lead to failure. Since the SE can perform maintenance work according to priority, the workload on the field can be reduced.

[0169] <Other application examples> Although the best mode for implementing this disclosure has been described above using examples, this disclosure is not limited in any way to these examples, and various modifications and substitutions can be made without departing from the gist of this disclosure.

[0170] For example, in this disclosure, COP was used as the air conditioning capacity, but it is also acceptable to use the air conditioning capacity itself to determine whether or not the air conditioning capacity has decreased.

[0171] Furthermore, in this disclosure, the COP was calculated retrospectively when a failure was predicted, but the COP may be calculated continuously even before a failure is predicted.

[0172] Furthermore, the configuration examples shown in Figure 8 and other figures are divided according to their main functions to facilitate understanding of the processing performed by the predictive detection device 400. This disclosure is not limited by the way the processing units are divided or their names. The processing of the predictive detection device 400 can be further divided into many more processing units depending on the processing content. It is also possible to divide the processing units so that each unit includes even more processing.

[0173] Furthermore, the apparatus described in the examples represents only one of several computing environments for carrying out the embodiments disclosed herein. In one embodiment, the predictive detection device 400 includes multiple computing devices, such as a server cluster. The multiple computing devices are configured to communicate with each other via any type of communication link, including a network or shared memory, and perform the processes disclosed herein.

[0174] Each of the functions of this disclosure described above can be implemented not only by software processing through program execution, but also by one or more processing circuits. Here, "processing circuit" as used herein includes processors programmed to execute each function by software, such as processors implemented by electronic circuits, as well as devices such as ASICs (Application Specific Integrated Circuits), DSPs (Digital Signal Processors), FPGAs (Field Programmable Gate Arrays), and conventional circuit modules designed to execute each of the functions described above.

[0175] <Reasons why the effect occurs> The first aspect of this disclosure involves "detecting signs of failure based on operating data, acquiring the air conditioning capacity of the air conditioning system, determining the priority of maintenance work on the air conditioning system where signs of failure have been detected based on the air conditioning capacity, and outputting the priority along with the signs of failure." This improves the accuracy of determining the priority of maintenance work to be performed on an air conditioner where failure is predicted, compared to determining the priority based on the component where signs of failure have been detected.

[0176] The second aspect of this disclosure "outputs the predicted time when a failure is likely to occur when signs of failure are detected," so that users can get an idea of ​​when a failure is likely to occur, making it easier for system engineers and others to determine maintenance work schedules.

[0177] • A third aspect of this disclosure is that, "when signs of failure are detected, the probability of failure occurring is output," which provides an indication of the likelihood of failure and makes it easier for SEs and others to decide which air conditioners should prioritize maintenance work on.

[0178] The fourth aspect of this disclosure is that, "when the difference between the expected air conditioning capacity and the measured air conditioning capacity is greater than a standard value, it is determined that the air conditioning capacity of the air conditioner has decreased based on predetermined judgment criteria," thus enabling accurate determination of whether the air conditioning capacity has decreased.

[0179] A fifth aspect of this disclosure is that "when signs of failure are detected and it is determined that the air conditioning capacity of the air conditioning system has decreased based on predetermined criteria, a message is output to the terminal indicating that maintenance work on the air conditioning system is to be proposed," so that the worker or user can understand that maintenance work should be performed because the air conditioning capacity of the air conditioning system has decreased.

[0180] The sixth aspect of this disclosure is that, "when the control unit receives notification that maintenance work has been completed for the air conditioning system, it calculates the air conditioning capacity of the air conditioning system after the maintenance work is completed." This allows the extent to which the air conditioning capacity of the air conditioning system has been restored by the maintenance work to be understood, and the effectiveness of the maintenance work can be demonstrated to the customer.

[0181] The seventh aspect of this disclosure is that "if the air conditioning capacity before maintenance work is determined to have decreased based on predetermined criteria, and the air conditioning capacity calculated after the completion of the maintenance work is determined not to have decreased based on predetermined criteria, then information regarding the air conditioning capacity of the air conditioning system is output," so that it is possible to appeal to the customer that the air conditioning capacity has returned to normal after the maintenance work.

[0182] The eighth aspect of this disclosure increases the priority of maintenance work on air conditioners in which signs of failure are detected and the air conditioning capacity of the air conditioner is judged to have decreased based on predetermined criteria, thereby increasing the priority of maintenance work on air conditioners that are at risk of failure.

[0183] The ninth aspect of this disclosure states that "when the rate of decline in air conditioning capacity is above a threshold, the priority of maintenance work on the air conditioning system in which signs of failure have been detected is increased compared to when the rate of decline in air conditioning capacity is below a threshold," thus allowing the priority of maintenance work on the air conditioner to be determined not only based on whether or not there is a decline in air conditioning capacity, but also on the rate of decline. [Explanation of Symbols]

[0184] 40 Air conditioner 60 Server Devices 100 Predictive Detection Systems 400 Predictive detection devices

Claims

1. A predictive detection device for detecting signs of failure in an air conditioning system that is subject to maintenance work, It has a control unit, The control unit acquires operating data output by the components of the air conditioning system from the air conditioning system, Based on the operating data obtained from the aforementioned air conditioning system, signs of malfunction are detected. The air conditioning capacity of the aforementioned air conditioning system is obtained, The priority of maintenance work on an air conditioning system showing signs of malfunction is determined based on the air conditioning capacity. Output the aforementioned priority along with the signs of failure. Predictive detection device.

2. The control unit, when it detects signs of failure, outputs the time when the failure is predicted to occur. The predictive detection device according to claim 1.

3. The control unit outputs the probability of a failure occurring when it detects signs of failure. The predictive detection device according to claim 1.

4. The control unit outputs the predicted air conditioning capacity by inputting the operating data acquired from the air conditioning unit into a model that has learned the correspondence between the operating data acquired from the air conditioning unit and the air conditioning capacity. The actual air conditioning capacity is calculated using the operating data obtained from the aforementioned air conditioning system. If the difference between the predicted air conditioning capacity and the measured air conditioning capacity is greater than a standard value, it is determined that the air conditioning capacity of the air conditioning system has decreased based on predetermined criteria. The predictive detection device according to claim 2 or 3.

5. The aforementioned predictive detection device can communicate via a network with a terminal operated by an operator or a user of the air conditioning system. The control unit, when it detects signs of a malfunction and determines that the air conditioning capacity of the air conditioning system has decreased based on predetermined criteria, outputs a message to the terminal indicating that it will propose maintenance work for the air conditioning system. The predictive detection device according to claim 1.

6. When signs of malfunction are detected and the air conditioning unit's cooling capacity is determined to have decreased based on predetermined criteria, the control unit receives notification that maintenance work has been completed for the air conditioning unit. The control unit calculates the air conditioning capacity of the air conditioning system after the maintenance work is completed. The predictive detection device according to claim 1.

7. The air conditioning capacity before the aforementioned maintenance work was determined to have decreased based on predetermined criteria. If the air conditioning capacity calculated after the completion of the aforementioned maintenance work is determined not to have decreased based on predetermined criteria, The control unit outputs information regarding the air conditioning capacity of the air conditioning system. The predictive detection device according to claim 6.

8. The control unit, When signs of malfunction are detected and the air conditioning capacity of the air conditioning system is determined to have decreased based on predetermined criteria, the priority of maintenance work on the air conditioning system is determined as follows: Prioritizing maintenance work on an air conditioning system where signs of failure have been detected and the air conditioning capacity of the air conditioning system has not decreased based on predetermined criteria will give it a higher priority than maintenance work on such a system. The predictive detection device according to claim 1.

9. The control unit, If it is determined that the air conditioning capacity of the aforementioned air conditioning system has decreased based on predetermined criteria, the rate of decrease in air conditioning capacity is determined, If the rate of decline in the air conditioning capacity exceeds a threshold, the maintenance work on the air conditioning system showing signs of failure will be given a higher priority than when the rate of decline in the air conditioning capacity does not exceed a threshold. The predictive detection device according to claim 1.

10. A predictive detection device is used to detect signs of failure in an air conditioning system that is subject to maintenance work, and this predictive detection method is used to detect signs of failure in the air conditioning system. The aforementioned predictive detection device has a control unit, The control unit, A process for acquiring operating data output by components of the air conditioning system from the air conditioning system, A process for detecting signs of failure based on operating data acquired from the aforementioned air conditioning system, A process for obtaining the air conditioning capacity of the aforementioned air conditioning system, A process for determining the priority of maintenance work on an air conditioning system in which signs of failure have been detected, based on the air conditioning capacity, A process that outputs the aforementioned priority along with the signs of failure, A method for detecting warning signs.

11. A predictive detection device that detects signs of failure in air conditioning equipment, which is the target of maintenance work, A process for acquiring operating data output by components of the air conditioning system from the air conditioning system, A process for detecting signs of failure based on operating data acquired from the aforementioned air conditioning system, A process for obtaining the air conditioning capacity of the aforementioned air conditioning system, A process for determining the priority of maintenance work on an air conditioning system in which signs of failure have been detected, based on the air conditioning capacity, A process that outputs the aforementioned priority along with the signs of failure, A program to execute.

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