Equipment value diagnosis system and equipment value diagnosis method

Abstract

Provided is a technique for estimating, in the case of reusing pre-owned equipment, whether the pre-owned equipment will be profitatble for customers and businesses over the life cycle of the equipment, together with an environmental impact.　This equipment value diagnosis system executes: a deterioration transition prediction step of predicting, by using equipment specification information and operation history information, deterioration prediction model information, and reuse condition information, deterioration transition regarding performance and a probability of maintenance work; an event prediction step of predicting, by using a result of the prediction of the deterioration transition, an event, which occurs during a reuse period, together with evaluation of all maintenance work to be performed when the event occurs and performance recovery resulting from the maintenance work; and a cost / environmental impact estimation step of estimating reuser's cost and environmental impact by using cost / environmental load information and the occurring event.

Description

Equipment Value Diagnosis System and Equipment Value Diagnosis Method

[0001] The present invention relates to an equipment value diagnosis system and an equipment value diagnosis method. The present invention claims the priority of Japanese Patent Application No. 2024-193578 filed on November 5, 2024, and for designated countries where incorporation by reference is permitted, the contents described in that application are incorporated herein by reference.

[0002] With the growing momentum to realize a sustainable society, there is a demand for a shift from a linear economy of mass production, mass consumption, and mass disposal to a circular economy that efficiently and circularly uses resources throughout the life cycle of the market. As one of the efforts towards this transformation, reuse, which extends the lifespan of products and efficiently uses resources, has attracted attention. To expand the reuse of used equipment for promoting reuse, it is necessary to select used products that can balance the interests of customers and business entities in the used product business. However, used products have different qualities individually, making it difficult to diagnose their value, and it is difficult to estimate in advance the costs and environmental burdens incurred by customers and business entities throughout the life cycle when reusing used products. Especially for equipment related to business operations such as industrial equipment, the proportion of costs and environmental burdens incurred during equipment operation, such as energy consumption and maintenance work, is high in the overall life cycle costs and environmental burdens. However, it is even more difficult to estimate these in advance for used equipment that has deteriorated compared to new equipment. As a result, it is difficult to conclude used product transactions because customers fear poor-quality used equipment and excessively demand price cuts. In addition, business entities may overestimate risks and discard used equipment.

[0003] Patent Document 1 describes a mechanism for calculating the future life cycle cost of machinery and equipment by accumulating information related to the state and maintenance history of machinery and equipment in a database for cost estimation throughout the life cycle.

[0004] Japanese Unexamined Patent Application Publication No. 2001-357112

[0005] However, the technology described in Patent Document 1 estimates the costs incurred throughout the entire lifecycle of machinery and equipment based on performance degradation and performance recovery through maintenance, customer and societal needs, and maintenance timing set using accumulated maintenance history. However, the cost estimation for maintenance is rough. In actual operation, the types of failures are diverse and the maintenance work changes, so the cost estimation results can be significantly inaccurate. Furthermore, the possibility that used equipment will require maintenance more frequently than new equipment, a characteristic of used equipment, is not considered. The object of the present invention is to estimate whether customers and businesses will benefit throughout the entire lifecycle of equipment when used equipment is reused, along with the environmental impact.

[0006] The present invention includes several means for solving at least part of the above problems, but an example thereof is as follows. An equipment value diagnostic system according to one aspect of the present invention that solves the above problems comprises: a first storage unit that stores equipment specification information and operation history information of a used equipment to be diagnosed; a second storage unit that stores deterioration prediction model information that has learned the deterioration progression of the used equipment to be diagnosed, using the equipment specification information and the operation history information as input; a third storage unit that stores reuse condition information including information that identifies the installation environment of the used equipment to be diagnosed and the period of use; a fourth storage unit that stores cost and environmental load information related to at least one or a combination of refurbishment work, construction work, energy consumption, maintenance work, disposal work, or insurance payment of the used equipment to be diagnosed; and a processing unit, wherein the processing unit processes the equipment specification information and the operation history information stored in the first storage unit, and the A system for diagnosing the value of equipment, characterized by performing the following steps: a deterioration trend prediction step that uses the deterioration prediction model information stored in the second storage unit and the reuse condition information stored in the third storage unit to predict the deterioration trend regarding the performance and the probability of maintenance work occurring when the used equipment to be diagnosed is reused; an event prediction step that uses the results of the deterioration trend prediction to predict events that will occur during the period in which the used equipment to be diagnosed is reused, including an evaluation of each maintenance work to be performed when the event occurs and the performance recovery caused by the maintenance work; and a cost and environmental load estimation step that uses the cost and environmental load information stored in the fourth storage unit and the events that will occur to estimate the cost and environmental load when the used equipment to be diagnosed is reused.

[0007] According to the present invention, in cases where used equipment is reused, it becomes possible to estimate, along with the environmental impact, whether it will be beneficial to the customer and the business entity throughout the entire lifecycle of the equipment. Other issues, configurations, and effects will be clarified by the following description of embodiments for carrying out the invention.

[0008] This figure shows an example configuration of the equipment value diagnostic system according to Embodiment 1. This figure shows an example configuration of target equipment information. This figure shows an example configuration of equipment specification information. This figure shows an example configuration of deterioration prediction model information. This figure shows an example flowchart of the calculation process. This figure shows an example display screen of the predicted deterioration trend results. This figure shows an example display screen of the predicted occurrence events. This figure shows an example display screen of the total lifecycle. This figure shows an example configuration of the equipment value diagnostic system according to Embodiment 2. This figure shows an example flowchart of the calculation process according to Embodiment 2. This figure shows an example display screen of the calculation results of the business entity's profits. This figure shows an example configuration of the equipment value diagnostic system according to Embodiment 3. This figure shows an example flowchart of the calculation process according to Embodiment 3. This figure shows an example configuration of equipment specification information according to Embodiment 4. This figure shows an example hardware configuration of the information processing device constituting the equipment value diagnostic system.

[0009] Embodiments of the present invention will be described below with reference to the drawings. The embodiments are illustrative examples for explaining the present invention, and have been omitted and simplified as appropriate for clarity of explanation. The present invention can also be carried out in various other forms. Unless otherwise specified, each component may be singular or plural.

[0010] The positions, sizes, shapes, and ranges of the components shown in the drawings may not represent their actual positions, sizes, shapes, and ranges in order to facilitate understanding of the invention. Therefore, the present invention is not necessarily limited to the positions, sizes, shapes, and ranges disclosed in the drawings.

[0011] Examples of various types of information may be described using expressions such as "table," "list," and "queue," but these types of information may also be represented by data structures other than these. For example, various types of information such as "XX table," "XX list," and "XX queue" may be referred to as "XX information." When describing identification information, expressions such as "identification information," "identifier," "name," "ID," and "number" are used, but these are interchangeable. Furthermore, the identification information described using these expressions may be represented using symbols, numbers, natural language, or combinations thereof in the embodiment, but the identification information may also be in other formats.

[0012] When there are multiple components with the same or similar function, they may be described using the same symbol but with different subscripts. Furthermore, when it is not necessary to distinguish between these multiple components, the subscripts may be omitted in the description.

[0013] In embodiments, processing performed by executing a program may be described. Here, the computer executes the program using a processor (e.g., CPU, GPU) and performs processing defined by the program using memory resources (e.g., memory) and interface devices (e.g., communication ports). Therefore, the main entity performing the processing by executing the program may be the processor. Similarly, the main entity performing the processing by executing the program may be a controller, device, system, computer, or node having a processor. The main entity performing the processing by executing the program may be an arithmetic unit, and may include a dedicated circuit that performs a specific processing. Here, a dedicated circuit is, for example, an FPGA (Field Programmable Gate Array), an ASIC (Application Specific Integrated Circuit), or a CPLD (Complex Programmable Logic Device).

[0014] The program may be installed on the computer from the program source. The program source may be, for example, a program distribution server or a storage medium readable by the computer. If the program source is a program distribution server, the program distribution server includes a processor and storage resources for storing the program to be distributed, and the processor of the program distribution server may distribute the program to other computers. In addition, in some embodiments, two or more programs may be implemented as a single program, or one program may be implemented as two or more programs.

[0015] Furthermore, while the present invention is typically implemented by an information processing device, it may also be implemented as a platform having the functions of the present invention.

[0016] In this embodiment, the equipment value assessment system 10 estimates the costs or environmental impacts incurred throughout the entire lifecycle when reusing the used equipment under assessment. This makes it possible to estimate whether the customer and the business entity will benefit or whether the environmental impact can be reduced throughout the lifecycle of reusing the used equipment under assessment.

[0017] Here, "used equipment to be diagnosed" refers to equipment with a history of use (for example, industrial equipment such as refrigerators and air compressors). "Customer" refers to a person who reuses the used equipment to be diagnosed. "Business entity" refers to a person who provides the used equipment to be diagnosed to a customer (for example, an equipment manufacturer, a used equipment dealer, or a leasing company). The person using the equipment value diagnosis system 10 may be the user of the equipment, i.e., a customer, or a business entity. Therefore, in this embodiment, the person using the equipment value diagnosis system 10 is referred to as the operator.

[0018] Figure 1 shows an example of the configuration of an equipment value diagnostic system. The equipment value diagnostic system 10 is composed of, for example, an information processing device. This information processing device includes a storage unit 1000, a processing unit 2000, an external information acquisition unit 3000, an input unit 4000, and a display unit 5000.

[0019] First, the storage unit 1000 stores at least the target equipment information 1100, the deterioration prediction model information 1200, the reuse condition information 1300, and the cost and environmental load information 1400. The target equipment information 1100 includes the equipment specification information 1110 of the used equipment to be diagnosed and the operation history information 1120 of the used equipment to be diagnosed. The contents of the equipment specification information 1110 and the operation history information 1120 will be described later.

[0020] The deterioration prediction model information 1200 is model information that has been trained to track the deterioration progression of a used equipment to be diagnosed, using the equipment specification information 1110 and the operation history information 1120 as inputs. The deterioration prediction model information 1200 is, for example, a model, function, various tabular data, etc., that uses the equipment specification information 1110 and the operation history information 1120 as inputs for explanatory variables, and the target variable is information including transition information of the ratio (deterioration) from the initial performance of the equipment's performance (power consumption, refrigeration capacity, etc.), and probability information of maintenance work (parts replacement, etc.) based on the aging load index. In other words, the deterioration prediction model information 1200 can be said to be trained model information that has been trained to output the deterioration progression of a used equipment to be diagnosed, given the equipment specification information 1110 and the operation history information 1120 as inputs.

[0021] The reuse conditions information 1300 is information that identifies the reuse conditions (including at least reuse period information and reuse environment information) when reusing the used equipment subject to diagnosis. The reuse environment information identifies the installation environment of the equipment to be reused, and includes information such as adjacent equipment, piping, and electrical systems. The reuse period information identifies the period during which the used equipment subject to diagnosis will be reused.

[0022] Cost and environmental impact information 1400 is information necessary to identify the costs or environmental impacts (unit price of work, CO2) related to at least one of the following when reusing the used equipment subject to diagnosis: refurbishment work, construction work, energy consumption, maintenance work, disposal work, or insurance payments. ２ This includes unit costs, etc. Alternatively, cost and environmental impact information 1400 includes information necessary to identify the costs or environmental impact related to a combination of recycling work, construction work, energy consumption, maintenance work, disposal work, or insurance payments (unit cost of work, CO2).２ Includes unit consumption, etc.

[0023] Furthermore, the processing unit 2000 includes a deterioration progression prediction unit 2100, an event prediction unit 2200, and a cost / environmental load estimation unit 2300. Here, an event refers to an event that generates costs or environmental loads throughout the entire lifecycle when the used equipment subject to diagnosis is reused.

[0024] The deterioration progression prediction unit 2100 predicts the deterioration progression of the used equipment to be reused. Specifically, the deterioration progression prediction unit 2100 predicts at least the ratio of each performance indicator of the used equipment to its initial performance, and the probability of each maintenance task occurring.

[0025] The event prediction unit 2200 predicts events that will occur during the period in which the used equipment under diagnosis is reused, including an evaluation of each maintenance operation performed when the event occurs and the performance recovery resulting from the maintenance operation. For example, a part that has failed after being used for a predetermined period (a failure event has occurred) is replaced during maintenance work, which extends the period in which the used equipment under diagnosis can be used. However, a failure event will eventually occur again. Based on these characteristics, the event prediction unit 2200 inductively predicts the probability of event occurrence using statistical methods. The event prediction unit 2200 predicts the occurrence of at least one event related to refurbishment work, construction work, energy consumption, or maintenance work.

[0026] The cost and environmental impact estimation unit 2300 estimates the expected costs and environmental impacts of reusing the used equipment subject to diagnosis. For example, the cost and environmental impact estimation unit 2300 uses the cost and environmental impact information 1400 and the events predicted to occur by the event prediction unit 2200 to estimate the costs and environmental impacts of the reuser who reuses the used equipment subject to diagnosis.

[0027] Furthermore, the external information acquisition unit 3000 acquires information via a network or portable storage medium to estimate the cost and environmental impact of reusing the used equipment to be diagnosed. The information acquired by the external information acquisition unit 3000 includes the target equipment information 1100, the deterioration prediction model information 1200, the reuse condition information 1300, and the cost and environmental impact information 1400 itself and parts thereof, as well as information for creating the target equipment information 1100, the deterioration prediction model information 1200, the reuse condition information 1300, and the cost and environmental impact information 1400 itself and parts thereof.

[0028] Furthermore, the input unit 4000 accepts various instructions from the operator. The input includes at least the target equipment information 1100 of the used equipment to be diagnosed. For example, the input unit 4000 accepts input via input devices such as a mouse or keyboard.

[0029] Furthermore, the display unit 5000 displays the output via a display device such as various displays. For this reason, the input unit 4000 and the display unit 5000 may be integrated, such as a touch panel. In addition, the input unit 4000 and the display unit 5000 may be configured in a separate enclosure from the equipment value diagnosis system 10. In this case, the input unit 4000 and the display unit 5000 may be implemented as separate terminal devices, and the input information and output information may be transferred via a network. The network may be, for example, a LAN (Local Area Network), WAN (Wide Area Network), VPN (Virtual Private Network), a communication network that uses public lines such as the Internet in part or in whole, a mobile phone communication network, or a network that is a combination of these. The network may also be a wireless communication network such as Wi-Fi (registered trademark) or 5G (Generation). Furthermore, the information processing device constituting the equipment value diagnostic system 10 is not limited to one unit; there may be even more units, including for purposes such as redundancy and load balancing. However, in this embodiment, for the sake of simplicity, we will describe it as if there were only one information processing device.

[0030] [Embodiment 1] Embodiment 1 describes an example of estimating the costs and environmental impact incurred throughout the entire lifecycle of a used piece of equipment (used refrigerator) when it is reused by a user. An example of the configuration of the equipment value diagnostic system 10 in Embodiment 1 is shown in Figure 1.

[0031] Figure 2 shows an example of the configuration of target equipment information. Target equipment information 1100 includes equipment specification information 1110 and operation history information 1120. Equipment specification information 1110 includes equipment manufacturer information 1111, model information 1112, serial number information 1113, parts information 1114, and performance information 1115.

[0032] Equipment manufacturer information 1111 identifies the manufacturer (vendor) that produced the used equipment to be diagnosed. Model information 1112 identifies the model to which the used equipment to be diagnosed belongs among the models sold by the manufacturer that produced the used equipment to be diagnosed. Serial number information 1113 identifies the used equipment to be diagnosed from among the same model. Parts information 1114 identifies the parts (components such as the casing) that make up the used equipment to be diagnosed. Performance information 1115 identifies the performance (capacity information, input power information, input current information, noise information, or lifespan information, etc.) that the used equipment to be diagnosed exhibits. Equipment specification information 1110 can be obtained from equipment specifications or media provided by the equipment manufacturer.

[0033] The operational history information 1120 includes contract information 1130, maintenance history information 1140, and operational history information 1150. The contract information 1130 includes contractor information 1131, installation date information 1132, and installation environment information 1133. Contractor information 1131 is information that identifies the person (individual name, company name, or factory name, store name, etc.) who has used the used equipment subject to diagnosis in the past. Installation date information 1132 is information that identifies the date on which the used equipment subject to diagnosis was installed in the user's environment. Installation environment information 1133 is information that identifies the environment (address, indoors or outdoors, etc.) in which the used equipment subject to diagnosis was installed.

[0034] Maintenance history information 1140 includes work date information 1141 and work content information 1142. Work date information 1141 is information that identifies the date on which the maintenance work was performed. Work content information 1142 is information that identifies the content of the maintenance work. For example, work content information 1142 includes maintenance content information (scheduled maintenance or emergency maintenance), maintenance work content information (replacement of part A, filling of part B, etc.), and failure content information (abnormal measurement value, pipe corrosion, etc.).

[0035] The operation history information 1150 includes operation time information 1151, ON / OFF information 1152, temperature information 1153, pressure information 1154, current information 1155, voltage information 1156, and frequency information 1157. The operation time information 1151 is information that identifies the cumulative operation time of the used equipment being diagnosed. The ON / OFF information 1152 is information that identifies the ON / OFF time information of the power supply of the used equipment being diagnosed, and the ON / OFF (operation, stop, operation switching, etc.) time information of the components.

[0036] Temperature information 1153 identifies the temperature information (suction temperature, discharge temperature, etc.) and ambient temperature information of the used equipment being diagnosed. Pressure information 1154 identifies the pressure information (suction pressure, discharge pressure, etc.) of the used equipment being diagnosed. Current information 1155 identifies the current information (input current, output current, etc.) of the used equipment being diagnosed. Voltage information 1156 identifies the voltage information (input voltage, output voltage, etc.) of the used equipment being diagnosed. Frequency information 1157 identifies the frequency information (power supply frequency information, inverter frequency information, etc.) of the used equipment being diagnosed.

[0037] Figure 3 shows an example of the configuration of equipment specification information 1110. Equipment specification information 1110 includes, for example, the equipment manufacturer (corresponding to equipment manufacturer information 1111), the equipment model (corresponding to model information 1112), the equipment serial number that identifies the equipment (corresponding to serial number information 1113), the refrigeration capacity that identifies the equipment's performance (corresponding to performance information 1115), power consumption (corresponding to performance information 1115), and information on the equipment's components (corresponding to component information 1114). Furthermore, while Figure 3 shows an example of a data structure when a refrigerator is treated as equipment, it is not limited to this. For example, when dealing with air compressors or other industrial equipment, equipment specification information 1110 includes output or input specification information corresponding to that equipment.

[0038] Figure 4 shows an example of the configuration of degradation prediction model information. The degradation prediction model information 1200 includes, for example, transition information of the ratio of the equipment's performance (power consumption, refrigeration capacity, etc.) to its initial performance based on an aging load index, and probability information of maintenance work (replacement of part A, replenishment of part B, etc.) based on an aging load index. For the aging load index, one or a combination thereof of cumulative installation years, cumulative operating years, cumulative operating hours, cumulative power consumption, etc. may be used. The aging load index may be calculated using a predetermined calculation formula or weighting calculation based on one or a combination thereof of cumulative installation years, cumulative operating years, cumulative operating hours, cumulative power consumption, etc. The transition information of the ratio of the equipment's performance to its initial performance based on an aging load index may be a model calculated based on the probability information of maintenance work, which is based on an aging load index. In Figure 4, the degradation prediction model information 1200 is stored as table data, but a function corresponding to the aging load index or a pre-trained model may also be used. Furthermore, when outputting degradation prediction model information, it is not necessary to output table data as shown in Figure 4; instead, graphs such as line graphs, or state transition diagrams for each maintenance task, as shown in the lower part of Figure 4, may also be output.

[0039] Figure 5 shows an example of a flowchart for the calculation process. The calculation process starts when the operator gives a start command via the input unit 4000. Alternatively, the calculation process can be started periodically (for example, daily, weekly, monthly, etc.).

[0040] First, the deterioration trend prediction unit 2100 acquires target equipment information 1100 (step S01). Specifically, the deterioration trend prediction unit 2100 identifies the used equipment to be diagnosed based on input from an operator using the input unit 4000, or based on target equipment information 1100 pre-set in the storage unit 1000, and acquires equipment specification information 1110 and operation history information 1120 of the used equipment to be diagnosed via the external information acquisition unit 3000 or the input unit 4000. The equipment specification information 1110 and operation history information 1120 acquired in this step may be selected and identified from information pre-stored in the storage unit 1000.

[0041] Then, the deterioration progression prediction unit 2100 acquires deterioration prediction model information 1200 (step S02). Specifically, the deterioration progression prediction unit 2100 identifies the used equipment to be diagnosed based on input from the operator using the input unit 4000, or based on target equipment information 1100 set in advance in the storage unit 1000, and acquires deterioration prediction model information 1200 of the used equipment to be diagnosed via the external information acquisition unit 3000 or the input unit 4000. The deterioration prediction model information 1200 acquired in this step may be selected and identified from information stored in advance in the storage unit 1000.

[0042] Then, the deterioration progression prediction unit 2100 acquires reuse condition information 1300 (step S03). Specifically, the deterioration progression prediction unit 2100 identifies the used equipment to be diagnosed based on input from the operator using the input unit 4000, or based on target equipment information 1100 set in advance in the storage unit 1000, and acquires reuse condition information 1300 for the used equipment to be diagnosed via the external information acquisition unit 3000 or the input unit 4000. The reuse condition information 1300 acquired in this step may be selected and identified from information stored in advance in the storage unit 1000.

[0043] Then, the deterioration trend prediction unit 2100 acquires the cost and environmental load information 1400 (step S04). Specifically, the deterioration trend prediction unit 2100 identifies the used equipment to be diagnosed according to the input operation from the operator using the input unit 4000 or the target equipment information 1100 set in the storage unit 1000 in advance, and acquires the cost and environmental load information 1400 of the used equipment to be diagnosed via the external information acquisition unit 3000 or the input unit 4000. The cost and environmental load information 1400 acquired in this step may be information that selects and identifies the information stored in the storage unit 1000 in advance.

[0044] Then, the deterioration trend prediction unit 2100 predicts the deterioration trend when the used equipment to be diagnosed is reused (step S05). Specifically, the deterioration trend prediction unit 2100 predicts at least the ratio of the initial performance of each performance index of the used equipment to be diagnosed and the occurrence probability of each maintenance operation. In this prediction, the deterioration trend prediction unit 2100 identifies the aging load index using, for example, the equipment specification information 1110, the operation history information 1120, and the number of years of reuse in the reuse condition information 1300, and extracts the ratio of each performance index of the corresponding aging load index in the deterioration prediction model information 1200 to the initial performance and the occurrence probability of each maintenance operation. Note that the deterioration trend prediction unit 2100 may calculate the aging load index by any one or a combination of the cumulative installation years, the cumulative operation years, the cumulative operation hours, the cumulative power consumption, the number of years of reuse, etc. using a predetermined calculation formula or weighted calculation. In addition, the deterioration trend prediction unit 2100 can also adjust the aging load index using the reuse environment included in the reuse condition information 1300. For example, when the reuse environment of the used equipment to be diagnosed is "outdoor storage", the deterioration trend prediction unit 2100 may increase the aging load index by a predetermined ratio to identify the aging load index, or when the reuse environment is "installed together with air conditioning equipment", the aging load index may be reduced by a predetermined ratio to identify the aging load index.

[0045] Then, the event prediction unit 2200 predicts the life cycle events when reusing the used equipment to be diagnosed (step S06). Specifically, the event prediction unit 2200 uses the deterioration transition when reusing the used equipment to be diagnosed predicted in step S05 to predict the events occurring throughout the life cycle when reusing the used equipment to be diagnosed and their occurrence probabilities based on the evaluation of each maintenance work occurring during the life cycle period and the performance recovery caused by each maintenance work. At that time, the event prediction unit 2200 includes at least one of recycling work, construction work, energy consumption, maintenance work, disposal work, or insurance payment that occurs when reusing the used equipment to be diagnosed in the target of the event for prediction.

[0046] Then, the cost and environmental load calculation unit 2300 calculates the cost and environmental load when reusing the used equipment to be diagnosed (step S07). Specifically, the cost and environmental load calculation unit 2300 uses the cost and environmental load information 1400 and the events occurring throughout the life cycle when reusing the used equipment to be diagnosed predicted in step S06 to calculate the expected values of the cost and environmental load of the recycler who reuses the used equipment to be diagnosed. For example, the cost and environmental load calculation unit 2300 calculates the maintenance work cost of component replacement occurring three years later, the maintenance worker for component replacement again occurring six years later, the cleaning maintenance work cost occurring four years later, the insurance premium occurring every year, the disposal cost occurring eight years later, and the greenhouse gas (for example, carbon dioxide CO ２ etc.) emissions, using the cost and environmental load information 1400. Also, at that time, the cost and environmental load calculation unit 2300 can calculate the highly probable total estimated amount and total estimated quantity by estimating the cost by multiplying the occurrence probability of a predetermined period for the maintenance work cost. The insurance premium is the fee paid to the insurance underwriting company for the repair cost for additional failures of the used equipment to be diagnosed.

[0047] Then, the display unit 5000 displays the prediction and calculation results (step S08). Specifically, the display unit 5000 displays the deterioration progression when the used equipment to be diagnosed is reused, as predicted in step S05, the probability of events occurring throughout the entire lifecycle when the used equipment to be diagnosed is reused, as predicted in step S06, and the expected values ​​of costs and environmental burdens when the used equipment to be diagnosed is reused, as predicted in step S07.

[0048] The above is an example of a flowchart for the estimation process. According to this example, in the case of reusing used equipment, it is possible to estimate whether the customer and the business entity will benefit throughout the entire lifecycle of the equipment, along with the environmental impact.

[0049] Figure 6 shows an example of a display screen for predicting deterioration trends. In example 6110 of the display screen for predicting deterioration trends, the cumulative trend of the aging load index value of the used equipment to be diagnosed, including the time of reuse and past use, is displayed based on the number of years of reuse (number of years elapsed since the most recent collection). The results of predicting the ratio of each performance index of the used equipment to be diagnosed to its initial performance, and the probability of each maintenance work occurring, according to the value of the aging load index, are also shown.

[0050] Of these, the aging load index for the 0th year of reuse, the degradation ratio from the initial performance for each performance index of the used equipment to be diagnosed, and the probability of each maintenance work occurring are displayed based on the predicted results from the equipment specification information 1110 and the operation history information 1120. The aging load index for the 1st year and beyond of reuse, the degradation ratio from the initial performance for each performance index of the used equipment to be diagnosed, and the probability of each maintenance work occurring are displayed based on the predicted results from the equipment specification information 1110, the degradation prediction model information 1200 and the reuse condition information 1300.

[0051] Furthermore, the total number of years of reuse displayed in the example 6110 of the display screen for predicting the deterioration trend is set based on the number of years of use in the reuse condition information 1300. Also, although the example 6110 of the display screen for predicting the deterioration trend in Figure 6 is displayed in a table format, it is not limited to a table format, and may also be output using a graph such as a line graph.

[0052] Figure 7 shows an example of a display screen for predicted event occurrences. In example 6120 of the display screen for predicted event occurrences, the probability of events (such as maintenance work) occurring throughout the entire lifecycle when the used equipment to be diagnosed is reused is shown according to the prediction results of the deterioration progression. The prediction results are for at least one of the following that occur when the used equipment to be diagnosed is reused: regeneration work, construction work, energy consumption, maintenance work, disposal work, or insurance payment.

[0053] Events occurring throughout the entire lifecycle may affect the predicted degradation progression or other predicted events throughout the lifecycle. Therefore, the event prediction unit 2200 predicts the occurrence of events by also considering the probability of the degradation progression or other events throughout the lifecycle that will be affected by the events predicted to occur during the lifecycle.

[0054] Specifically, the event prediction unit 2200 predicts the occurrence of further events in the period following an event, taking into consideration the evaluation of performance recovery caused by each maintenance operation that occurs during the lifecycle period, the evaluation of performance recovery caused by regeneration operations that occur during the lifecycle period, etc. (for example, partially resetting the aging load indicator related to the maintenance operations that were performed to zero).

[0055] Furthermore, regarding the probability of maintenance work occurring, assuming that used parts are used in maintenance work, the trend in the probability of subsequent maintenance work occurring may be predicted based on the deterioration of the used parts. For example, if part A is replaced in its 5th year of reuse, and the replacement part incorporated is a reused part that has deteriorated over time, the event prediction unit 2200 may predict the probability of subsequent events occurring by referring to the age-related load index of the reused part.

[0056] Furthermore, events occurring throughout the entire lifecycle may be modified by input operations from an operator using the input unit 4000. In this case, the event prediction unit 2200 evaluates the degradation progression or other changes in events occurring throughout the entire lifecycle given by the lifecycle events modified by the input operations, and predicts events that will occur during the subsequent lifecycle period. In addition, the total number of reuse years displayed in the example 6120 of the display screen for predicted event results is set based on the number of years of use in the reuse condition information 1300. Also, although the example 6120 of the display screen for predicted event results in Figure 7 is displayed in a table format, it is not limited to a table format, and may be output using graphs such as line graphs.

[0057] Figure 8 shows an example of a display screen for the total lifecycle. In example 6130 of the total lifecycle display screen, the estimated costs and expected environmental impacts for each event throughout the entire lifecycle are shown, based on the estimated results of events that occur throughout the entire lifecycle when the used equipment subject to diagnosis is reused. By summing the estimated costs and expected environmental impacts shown in example 6130 of the total lifecycle display screen, the total estimated costs and expected environmental impacts for the entire lifecycle when the used equipment subject to diagnosis is reused can be estimated.

[0058] Furthermore, the example 6130 of the cumulative lifecycle display screen may also display estimated costs and expected environmental impacts in terms of the number of years of reuse. In addition, the cumulative period displayed in the example 6130 of the cumulative lifecycle display screen is set based on the number of years of use in the reuse condition information 1300. Also, although the example 6130 of the cumulative lifecycle display screen in Figure 8 is displayed in a table format, it is not limited to a table format, and may also be output using graphs such as line graphs.

[0059] The above describes the equipment value diagnostic system 10 according to Embodiment 1. The equipment value diagnostic system 10 makes it possible to estimate the expected costs and environmental impacts incurred throughout the entire lifecycle when reusing used equipment (refrigeration units). Therefore, it can be said that decisions on reusing used equipment can be made based on the risks incurred throughout the entire lifecycle when reusing them.

[0060] [Embodiment 2] The equipment value diagnostic system 10 according to Embodiment 2 is basically the same as the equipment value diagnostic system 10 according to Embodiment 1, but there are some differences. The differences will be explained below. The equipment value diagnostic system 10 according to Embodiment 2 differs from the equipment value diagnostic system 10 according to Embodiment 1 in the following respects: - It estimates the business entity's profit when selling the used equipment to be diagnosed. - It uses sales price information to estimate the business entity's profit when selling the used equipment to be diagnosed. - It estimates the sales price in order to estimate the business entity's profit when selling the used equipment to be diagnosed.

[0061] Figure 9 shows an example of the configuration of the equipment value diagnostic system according to Embodiment 2. The example of the configuration of the equipment value diagnostic system 10 according to Embodiment 2 is basically the same as that shown in Figure 1, but differs in the following points.

[0062] The memory unit 1000 contains sales price information 1500. The sales price information 1500 is information that identifies the sales price when providing the used equipment to be diagnosed to the customer. The processing unit 2000 also contains a sales price estimation unit 2400 and a profit estimation unit 2500.

[0063] The sales price estimation unit 2400 estimates the sales price to be offered to the reuser (customer) based on the service type used when providing the used equipment to be diagnosed, by applying the reuse condition information. Specifically, the service types include a buy-out service where the used equipment to be diagnosed is sold as a standalone item; a service with installation that includes the installation of the used equipment to be diagnosed; a service with electricity costs that includes the electricity costs for operating the used equipment to be diagnosed; a service with equipment monitoring that includes a service fee for monitoring the operation of the used equipment to be diagnosed; a service with maintenance that includes a service fee for maintaining the used equipment to be diagnosed; a service with insurance that includes the insurance premium for the used equipment to be diagnosed; a service with disposal that includes the disposal costs for the used equipment to be diagnosed; or a combination of these.

[0064] The profit estimation unit 2500 estimates the expected profit of the business entity when reusing the used equipment subject to diagnosis.

[0065] Figure 10 shows an example of a flowchart for the calculation process according to Embodiment 2. The calculation process according to Embodiment 2 is basically the same as the prediction and calculation process according to Embodiment 1, but there are some differences. The differences will be explained below.

[0066] First, after step S04, the sales price estimation unit 2400 acquires sales price information 1500 (step S09). Specifically, the sales price estimation unit 2400 identifies the used equipment to be diagnosed based on input from the operator using the input unit 4000, or based on target equipment information 1100 set in advance in the storage unit 1000, and acquires sales price information 1500 for the used equipment to be diagnosed via the external information acquisition unit 3000 or the input unit 4000. The sales price information 1500 acquired in this step may be selected and identified from information stored in advance in the storage unit 1000.

[0067] Then, after step S07, the sales price estimation unit 2400 estimates the sales price of the used equipment to be diagnosed (step S10). Specifically, the sales price estimation unit 2400 estimates the sales price of the used equipment to be diagnosed using the expected cost and environmental impact of reusing the used equipment to be diagnosed, which was estimated in step S07, and the predetermined fees to be added in the service format when providing the used equipment to the customer. For example, if a service including installation is applied, the sales price estimation unit 2400 estimates the sales price by adding part or all of the installation service to the sales price of the used equipment to be diagnosed. Other services are estimated in the same manner. The service format when selling the used equipment to be diagnosed is determined according to the selection input operation by the operator using the input unit 4000, or by applying the service format predetermined in the reuse condition information 1300.

[0068] Then, after step S10, the profit estimation unit 2500 estimates the business entity's profit when reusing the used equipment subject to diagnosis (step S11). Specifically, the profit estimation unit 2500 uses the sales price information obtained in step S09, or the sales price information estimated in step S10, and the expected values ​​of costs and environmental burden when reusing the used equipment subject to diagnosis, estimated in step S07, to estimate the expected value of the business entity's profit when reusing the used equipment subject to diagnosis. For example, the profit estimation unit 2500 estimates the difference between the estimated sales price and the estimated costs of reusing (cleaning, maintenance costs, etc.) as the expected value of the business entity's profit when allowing the used equipment subject to diagnosis to be reused by a reuser.

[0069] Figure 11 shows an example of a display screen for the estimated profit of a business entity. The display screen 6210 for the estimated profit of a business entity shows the service type when selling the used equipment to be diagnosed, the sales price corresponding to the service type when selling the used equipment to be diagnosed, the expected cost of the business entity according to the service type when selling the used equipment to be diagnosed, and the expected profit according to the service type when selling the used equipment to be diagnosed. The display screen 6210 for the estimated profit of a business entity in Figure 11 is displayed in graph format, but it is not limited to graph format and may also be output in table format.

[0070] The above describes the equipment value diagnostic system 10 according to Embodiment 2. According to the equipment value diagnostic system 10 according to Embodiment 2, it is possible to estimate whether a business entity will make a profit over the entire lifecycle when reusing used equipment (refrigeration unit). Therefore, it can be said that the decision of whether or not to sell used equipment to a customer can be made based on the business risks over the entire lifecycle when reusing the equipment.

[0071] [Embodiment 3] The equipment value diagnostic system 10 according to Embodiment 3 is basically the same as the equipment value diagnostic system 10 according to Embodiment 2, but there are some differences. Furthermore, the equipment value diagnostic system 10 according to Embodiment 3 can be combined not only with Embodiment 2 but also with the equipment value diagnostic system 10 according to Embodiment 1. The differences from the equipment value diagnostic system 10 according to Embodiment 2 will be explained below. The equipment value diagnostic system 10 according to Embodiment 3 differs from the equipment value diagnostic system 10 according to Embodiment 2 in the following respects: - It generates or updates a deterioration prediction model. - It uses market equipment information to generate or update the deterioration prediction model.

[0072] Figure 12 shows an example of the configuration of the equipment value diagnostic system according to Embodiment 3. The example of the configuration of the equipment value diagnostic system 10 according to Embodiment 3 is basically the same as that shown in Figure 9, but differs in the following points.

[0073] The memory unit 1000 contains market equipment information 1600. Market equipment refers to equipment with an operational history. The market equipment information 1600 includes equipment specification information 1610 and operational history information 1620. The equipment specification information 1610 and operational history information 1620 basically have the same data structure as the equipment specification information 1110 and operational history information 1120 related to Embodiment 1, but differ in that they are data that covers all equipment available on the market.

[0074] Furthermore, the processing unit 2000 includes a deterioration prediction model generation unit 2600. The deterioration prediction model generation unit 2600 generates or updates deterioration prediction model information 1200 for predicting the deterioration progression of the used equipment to be diagnosed.

[0075] Figure 13 shows an example of a flowchart for the calculation process according to Embodiment 3. The calculation process according to Embodiment 3 is basically the same as the calculation process according to Embodiment 2, but there are some differences. The differences will be explained below.

[0076] First, after step S09, the deterioration prediction model generation unit 2600 acquires market equipment information 1600 (step S12). Specifically, the deterioration prediction model generation unit 2600 acquires market equipment information 1600 via the external information acquisition unit 3000 or the input unit 4000. The market equipment information 1600 acquired in this step may be selected and identified from information previously stored in the storage unit 1000.

[0077] Then, after step S12, the deterioration prediction model generation unit 2600 generates or updates a deterioration prediction model (step S13). Specifically, the deterioration prediction model generation unit 2600 uses the market equipment information 1600 as learning information (as explanatory variables) to generate or update deterioration prediction model information 1200 for predicting the deterioration trend of the used equipment to be diagnosed. Alternatively, the deterioration prediction model generation unit 2600 may use regression analysis or AI (Artificial Intelligence) to identify an approximate curve from the market equipment information 1600 and obtain a function of the approximate curve.

[0078] The above describes the equipment value diagnostic system 10 according to Embodiment 3. According to the equipment value diagnostic system 10 according to Embodiment 3, it is possible to generate or update deterioration prediction model information 1200 using market equipment information 1600 which has a larger accumulation of events. Therefore, it can be said that it is possible to predict the deterioration trend of the equipment to be diagnosed with higher accuracy using deterioration prediction model information 1200.

[0079] [Embodiment 4] The equipment value diagnostic system 10 according to Embodiment 4 is basically the same as the equipment value diagnostic system 10 according to Embodiments 1 to 3, but it is an example in which a used equipment other than a refrigerator is reused, for example, an air compressor.

[0080] Figure 14 shows an example of the configuration of equipment specification information according to Embodiment 4. In the equipment specification information 1110' according to Embodiment 4, since the used equipment to be diagnosed is an air compressor, the performance is handled as the amount of discharged air, whereas in the case of a refrigerator, which is the used equipment to be diagnosed according to Embodiment 1, the performance was handled as the cooling capacity. Furthermore, by changing the performance indicators and the type of energy used, it can be applied to various types of industrial equipment (including elevators, machine tools, etc.).

[0081] [Explanation of Hardware Configuration] Figure 15 shows an example of the hardware configuration of the information processing device that constitutes the equipment value diagnostic system. The information processing device 900 that constitutes the equipment value diagnostic system 10 includes a processor (for example, CPU: Central Processing Unit, or GPU: Graphics Processing Unit) 901, hardware memory 902 such as RAM (Random Access Memory), storage 903 such as a hard disk drive (HDD) or SSD (Solid State Drive), and CD (Compact Disk) or DVD (Digital Versatile). This can be implemented as a general-purpose computer or a network system comprising multiple such computers, which includes a reader 905 that reads information from a portable storage medium 904 such as a disk, an input device 906 such as a keyboard, mouse, barcode reader, or touch panel, an output device 907 such as a display, and a communication device 908 that communicates with other computers via a communication network such as a LAN or the Internet. The reader 905 may be capable of not only reading but also writing to the portable storage medium.

[0082] The processor 901 performs various processes by executing predetermined programs loaded from the storage 903 into the memory 902. These programs are, for example, application programs that can be executed on an OS (Operating System) program. These programs may be installed in the storage 903 from a portable storage medium 904 via a reader 905, or they may be downloaded from a network via a communication device 908 and executed by the processor 901.

[0083] For example, the processing unit 2000, the degradation progression prediction unit 2100, the event prediction unit 2200, the cost and environmental load estimation unit 2300, the sales price estimation unit 2400, the profit estimation unit 2500, and the degradation prediction model generation unit 2600 can be implemented by loading a program stored in the storage 903 into the memory 902 and executing it with the processor 901. The input unit 4000 and the external information acquisition unit 3000 can be implemented by the processor 901 using the input device 906, the output device 907, and the communication device 908. The storage unit 1000 can be implemented by the processor 901 using the memory 902 or the storage 903. The display unit 5000 can be implemented by using the output device 907.

[0084] The above is an example of an equipment value diagnostic system according to an embodiment of the present invention. It should be noted that the present invention is not limited to the embodiments described above, and various modifications are included. For example, the embodiments described above are explained in detail to make the present invention easier to understand, and are not necessarily limited to those comprising all the described configurations. It is possible to replace some of the configurations of an embodiment with other configurations, and it is also possible to add configurations from other embodiments to the configuration of an embodiment. Furthermore, it is possible to delete some of the configurations of an embodiment.

[0085] Each of the above-mentioned parts, configurations, functions, and processing units may be implemented in hardware, in whole or in part, for example, by designing them as integrated circuits. Alternatively, each of the above-mentioned parts, configurations, and functions may be implemented in software by having the processor interpret and execute programs that realize each function. Information such as programs, tables, and files that realize each function can be stored in memory, a recording device such as a hard disk, or a recording medium such as an IC card, SD card, or DVD.

[0086] It should be noted that the control lines and information lines in the embodiments described above are those deemed necessary for explanation and do not necessarily represent all control lines and information lines in the actual product. In practice, it can be assumed that almost all components are interconnected. The present invention has now been described, focusing on its embodiments.

[0087] 10: Equipment value diagnostic system, 1000: Memory unit, 1100: Target equipment information, 1110: Equipment specification information, 1120: Operation history information, 1200: Deterioration prediction model information, 1300: Reuse condition information, 1400: Cost and environmental load information, 2000: Processing unit, 2100: Deterioration progression prediction unit, 2200: Event prediction unit, 2300: Cost and environmental load estimation unit, 3000: External information acquisition unit, 4000: Input unit, 5000: Display unit.

Claims

1. A processing unit comprising: a first storage unit that stores equipment specification information and operation history information of the used equipment to be diagnosed; a second storage unit that stores deterioration prediction model information that has been learned to track the deterioration progression of the used equipment to be diagnosed, using the equipment specification information and operation history information as input; a third storage unit that stores reuse condition information including information that identifies the installation environment of the used equipment to be diagnosed and the period of use; a fourth storage unit that stores cost and environmental load information related to at least one or a combination of refurbishment work, construction work, energy consumption, maintenance work, disposal work, or insurance payments for the used equipment to be diagnosed; and the processing unit, the processing unit performing a deterioration progression prediction step that uses the equipment specification information and operation history information stored in the first storage unit, the deterioration prediction model information stored in the second storage unit, and the reuse condition information stored in the third storage unit to predict the deterioration progression regarding the performance and the probability of maintenance work occurring when the used equipment to be diagnosed is reused. Equipment value diagnostic system characterized by performing: an event prediction step, which uses the results of the deterioration progression prediction to predict events that will occur during the period in which the used equipment subject to diagnosis is reused, including an evaluation of each maintenance work to be performed when the event occurs and the performance recovery caused by the maintenance work; and a cost and environmental load estimation step, which uses the cost and environmental load information stored in the fourth storage unit and the events that will occur to estimate the cost and environmental load of the reuser who reuses the used equipment subject to diagnosis.

2. The equipment value diagnostic system according to claim 1, characterized in that, in the event prediction step, the occurrence of at least one of the events relating to the regeneration work, the construction work, the energy consumption, or the maintenance work is predicted.

3. An equipment value diagnostic system according to claim 1, characterized in that, in the cost and environmental load estimation step, the sales price for providing the used equipment subject to diagnosis to the customer as a reuser, in a service form that includes installation services for the used equipment subject to diagnosis, is estimated by applying the reuse condition information.

4. An equipment value diagnostic system according to claim 1, characterized in that, in the cost and environmental load estimation step, the sales price for providing the used equipment subject to diagnosis to the customer as a reuser, including the electricity cost for operating the used equipment subject to diagnosis, is estimated by applying the reuse condition information to the service form for providing the used equipment subject to diagnosis to the customer as a reuser.

5. An equipment value diagnostic system according to claim 1, characterized in that, in the cost and environmental load estimation step, the sales price for providing the used equipment subject to diagnosis to the customer as a reuser, which includes a service fee for monitoring the operation of the used equipment subject to diagnosis, is estimated by applying the reuse condition information to the service form for providing the used equipment subject to diagnosis to the customer as a reuser.

6. An equipment value diagnostic system according to claim 1, characterized in that, in the cost and environmental load estimation step, the sales price for providing the used equipment subject to diagnosis to the customer as a reuser, which includes a service fee for maintenance of the used equipment subject to diagnosis, is estimated by applying the reuse condition information.

7. An equipment value diagnostic system according to claim 1, characterized in that, in the cost and environmental load estimation step, the sales price at which the used equipment subject to diagnosis will be provided to the customer as a reuser, in an insured service form that includes the insurance premium for the used equipment subject to diagnosis, is estimated by applying the reuse condition information.

8. An equipment value diagnostic system according to claim 1, characterized in that, in the cost and environmental load estimation step, the sales price for providing the used equipment subject to diagnosis to the customer as a reuser, in a service form that includes disposal costs for the used equipment subject to diagnosis, is estimated by applying the reuse condition information.

9. An equipment value diagnostic system according to any one of claims 3 to 8, characterized in that, in the cost and environmental load estimation step, the difference between the estimated sales price and the estimated cost of reuse is estimated as the expected profit of the business entity that allows the reused equipment to be reused by the reuser.

10. An equipment value diagnostic system according to claim 1, wherein the processing unit receives input of equipment specification information and operation history information for market equipment, and performs a deterioration prediction model generation step of generating or updating deterioration prediction model information using the received equipment specification information and operation history information for at least the market equipment as explanatory variables.

11. An equipment value diagnostic system according to claim 1, wherein the equipment specification information includes at least one of equipment manufacturer information, model information, serial number information, parts information, and performance information; the operation history information includes at least one of contract information, maintenance history information, and operational history information; the contract information includes at least one of contractor information, installation date information, and installation environment information; the maintenance history information includes at least one of work date information and work content information; the operational history information includes at least one of operating time information, ON / OFF information, temperature information, pressure information, current information, voltage information, and frequency information; and the reuse condition information includes at least one of reuse years information, reuse environment information, and service form at the time of reuse.

12. A method for diagnosing the value of equipment using an information processing device, the information processing device comprising: a first storage unit for storing equipment specification information and operation history information of the used equipment to be diagnosed; a second storage unit for storing deterioration prediction model information that has learned the deterioration progression of the used equipment to be diagnosed, with the equipment specification information and the operation history information as input; a third storage unit for storing reuse condition information including information that identifies the installation environment of the used equipment to be diagnosed and the period of use; a fourth storage unit for storing cost and environmental load information related to at least one or a combination of refurbishment work, construction work, energy consumption, maintenance work, disposal work, or insurance payments for the used equipment to be diagnosed; and a processing unit, the processing unit comprising: a deterioration progression prediction step for predicting the deterioration progression regarding the performance and the probability of maintenance work occurring when the used equipment to be diagnosed is reused, using the equipment specification information and operation history information stored in the first storage unit, the deterioration prediction model information stored in the second storage unit, and the reuse condition information stored in the third storage unit; A method for diagnosing the value of equipment, characterized by: an event prediction step in which, using the results of the deterioration progression prediction, events occurring during the period in which the used equipment to be diagnosed is reused are predicted, including an evaluation of each maintenance work to be performed when the event occurs and the performance recovery resulting from the maintenance work; and a cost and environmental load estimation step in which, using the cost and environmental load information stored in the fourth storage unit and the events that occur, the costs and environmental load of the reuser who reuses the used equipment to be diagnosed are estimated.

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