Automatic maintenance support response system and automatic maintenance support response method

Abstract

We provide highly accurate maintenance support by utilizing information specific to each customer's machine. [Solution] The present invention provides an automated maintenance support response system comprising: a maintenance domain knowledge database that stores knowledge related to machine maintenance; a general-purpose knowledge database that stores knowledge not belonging to a specific domain; a customer machine-specific knowledge database that stores the operating history of the customer's machine; and a response generation unit that generates answers in natural language to customer questions regarding machine maintenance based on the information stored in the maintenance domain knowledge database, the general-purpose knowledge database, and the customer machine-specific knowledge database.

Description

Technical Field

[0001] The present invention relates to a maintenance support automatic answering system and a maintenance support automatic answering method.

Background Art

[0002] When a problem occurs in equipment, a customer who maintains and manages the equipment refers to a maintenance manual or the like. However, when the problem is very serious and there is no time to refer to the maintenance manual or the like, or when no appropriate countermeasure can be found even after referring to the maintenance manual or the like, there is a known technique in which a computer installed in a maintenance center or an equipment manufacturer automatically answers questions from customers.

[0003] The question-and-answer system of Patent Document 1 utilizes natural language information and non-language information (e.g., images, CAD data) and generates an answer based on a question by deep learning. That is, the question-and-answer system of Patent Document 1 receives a user's question as an input of voice data or text data, extracts information related to the question using natural language processing from information sources such as design specifications, maintenance reports, parts information, and CAD data, generates an answer based on the extracted information, and presents the generated answer to the user through text display, voice output, and further display of the inference process.

Prior Art Documents

Patent Documents

[0004] [[ID=*26]]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] In actual maintenance settings, the operating environment and methods of equipment strongly reflect the specific circumstances of each customer (user). Therefore, when equipment malfunctions occur, these malfunctions also strongly reflect the customer's specific circumstances. Consequently, even if the equipment manufacturer and model are the same, the answers to questions regarding malfunctions will differ from customer to customer, and even chronologically.

[0006] However, Patent Document 1 lacks the idea that malfunctions occurring in equipment strongly reflect the individual circumstances of the customer. Therefore, the question-answering system in Patent Document 1 generates answers to customer questions using a single knowledge source. As a result, it takes a considerable amount of time to obtain the specific answers that the customer truly needs. Therefore, the present invention aims to provide highly accurate maintenance support by utilizing information specific to each customer's machine. [Means for solving the problem]

[0007] The present invention provides an automated maintenance support response system comprising: a maintenance domain knowledge database that stores knowledge related to machine maintenance; a general-purpose knowledge database that stores knowledge not belonging to a specific domain; a customer machine-specific knowledge database that stores the operating history of the customer's machine; and a response generation unit that generates answers in natural language to customer questions regarding machine maintenance based on the information stored in the maintenance domain knowledge database, the general-purpose knowledge database, and the customer machine-specific knowledge database. Other means will be described within the descriptions of embodiments for carrying out the invention. [Effects of the Invention]

[0008] According to the present invention, highly accurate maintenance support can be provided by utilizing information specific to each customer's machine. More specifically, this is as follows:

[0009] This invention makes it possible to provide personalized answers to each customer by utilizing data sheets that show the operating history, maintenance history, and machine specifications unique to each customer's machine, as well as troubleshooting information. This allows for the rapid provision of specific and precise maintenance information and diagnostic results based on the current state of the customer's machine, which was not possible with conventional systems, thereby improving customer satisfaction.

[0010] Furthermore, the present invention improves the accuracy of failure prevention and anomaly diagnosis by combining machine specifications, troubleshooting information, and operating history, thereby streamlining maintenance work. This minimizes machine downtime, enables early detection of anomalies and proactive maintenance planning, and reduces operating costs and optimizes maintenance schedules.

[0011] Furthermore, the present invention can provide real-time answers to customer inquiries, and through a combination of natural language processing (NLP), large language models (LLM), and retrieve augmented generation (RAG), it can deliver expert, personalized, and rapid responses. This enables quick and accurate responses, especially to urgent troubleshooting and specific questions regarding parts, thereby reducing the time to problem resolution.

[0012] In addition, by switching and using customer-specific search extension generation modules, the present invention provides accurate answers based on individual information, including machine specifications, without confusing customer-specific data. This improves data security and reliability, and enables customized services tailored to the needs of each customer.

[0013] Finally, the present invention improves customer satisfaction and strengthens recurring business by streamlining maintenance and troubleshooting for customers' machines. In particular, by providing high value-added support based on customer-specific data, it facilitates the building of long-term relationships with customers and promotes the renewal of maintenance and service contracts. [Brief explanation of the drawing]

[0014] [Figure 1] This diagram shows the overall configuration of the automated maintenance support response system according to Embodiment 1. [Figure 2] This diagram illustrates the functions of the main components of the automated maintenance support response system of Embodiment 1 in a step-by-step manner. [Figure 3] This diagram shows the processing flow of Embodiment 1. [Figure 4] This diagram illustrates the functions of the main components of the automated maintenance support response system of Embodiment 2 in a step-by-step manner. [Figure 5] This diagram shows the processing flow of Embodiment 2. [Figure 6] This diagram illustrates the functions of the main components of the automated maintenance support response system of Embodiment 3 in a stepwise manner. [Figure 7] This diagram shows the processing flow of Embodiment 4. [Figure 8] This diagram illustrates the functions of the main components of the automated maintenance support response system of Embodiment 5 in a stepwise manner. [Figure 9] This is a diagram illustrating the chat screen. [Figure 10] This is a hardware configuration diagram of a computer applied to an automated maintenance support response system. [Modes for carrying out the invention]

[0015] (Introduction) Embodiments of the present invention utilize customer machine-specific information including maintenance history, operation history, data sheets showing machine specifications such as those of industrial equipment like centrifugal compressors, and information regarding troubleshooting to provide specialized and individualized responses.

[0016] Centrifugal compressors are used in a wide range of industries such as petroleum refining plants, petrochemical and synthetic chemical plants, and natural gas plants, and support stable production processes. In particular, in order to maintain the performance and reliability of compressors, appropriate maintenance and failure prevention are essential, and after-sales services for supporting this are widely provided.

[0017] However, in conventional after-sales services, often only general technical information and standard maintenance procedures are provided. Customized responses considering data sheets showing operation history, maintenance history, machine specifications, etc. specific to customer machines and information regarding troubleshooting have not been fully carried out. As a result, there are cases where sufficient responses cannot be made to specific problems faced by customers, customer satisfaction decreases, and efficient maintenance work is not performed. Embodiments of the present invention improve this point.

[0018] Hereinafter, as embodiments of the present invention, Embodiments [1-5] will be described in detail along with the figures. For ease of understanding, first, an outline of these will be described as follows. That is, Embodiment 1 is so-called a basic type, and uses information from three different knowledge databases to generate answers to questions (Figs. 1-3). Embodiments 2-5 are developed types of Embodiment 1, and hereinafter, mainly additional configurations compared to Embodiment 1 will be described. Among these, Embodiment 2 additionally acquires machine-specific knowledge other than natural language according to the content of the question (Figs. 4 and 5). Embodiment 3 separates and manages machine-specific knowledge for each customer (Fig. 6). Embodiment 4 updates machine-specific knowledge (Fig. 7). Embodiment 5 uses external knowledge (Fig. 8).

[0019] <Embodiment 1> Figure 1 shows the overall configuration of the automated maintenance support response system of Embodiment 1. The automated maintenance support response system 1 provides specialized and personalized maintenance information in response to maintenance-related questions from customers by referring to multiple knowledge databases (DBs).

[0020] The maintenance support automated response system 1 includes an application server 3, a web server 4, a customer information DB 21, a general-purpose knowledge DB 22, a maintenance domain knowledge DB 23, a customer machine-specific knowledge DB 24, an asset knowledge DB 25, and a machine specification DB 26. The maintenance support automated response system 1 is connected to the customer device 2 and an external portal site 5 via a wired or wireless network (not shown), such as the Internet.

[0021] The application server 3 has a chatbot application 11. The chatbot application 11 has an answer generation unit 12, a customer question acquisition unit 13, a question distribution agent 14, an additional information acquisition agent 15, and an action processing unit 18 other than AI (Artificial Intelligence). The additional information acquisition agent 15 has agent α 16 and agent β 17.

[0022] The maintenance support automated response system 1 is a general-purpose computer and its hardware includes a central control unit, input devices such as a keyboard, output devices such as a display, main memory, auxiliary storage, and communication devices (see Figure 10 below for the hardware configuration). The auxiliary storage stores each DB shown in Figure 1.

[0023] The chatbot application 11 in Figure 1, as well as each part and agent contained therein, are programs as software. In the following explanation, when a subject is indicated as "part ○" or "agent ○", it means that the central control unit reads each program from the auxiliary storage device into the main memory and executes the processing pre-written in that program.

[0024] In Figure 1, the automated maintenance support response system 1 is represented as a single enclosure. However, the automated maintenance support response system 1 may be configured as a distributed system (cloud) across multiple enclosures. In that case, each component of the automated maintenance support response system 1 as shown in Figure 1 will be contained within any enclosure on the cloud. For example, each database may be stored in an enclosure independent of the automated maintenance support response system 1. Note that Figure 1 is also common to embodiments 2 to 5 described later.

[0025] Figure 2 is a diagram illustrating the functions of the main components of the automated maintenance support response system of Embodiment 1 in a stepwise manner. In Figure 2, one step (indicated by symbols such as "104") may correspond to multiple configurations (rectangular and cylindrical). The same applies to other embodiments.

[0026] <Stage 101> Customer device 2 is a means by which the customer accesses the Web server 4 via a browser or dedicated application and sends questions to the maintenance support automated response system 1. Specifically, it is a personal computer, tablet, smartphone, etc. When the customer enters a maintenance-related question into customer device 2, customer device 2 sends the question to the Web server 4. Note that customer devices 2 used exclusively by a particular customer are indicated as "Customer A's device 2a," for example.

[0027] <Stage 102> The web server 4 receives an HTTP request from the customer, analyzes the customer's question, and forwards (routes) it to the application server 3. This allows appropriate processing according to the content of the question to be carried out in subsequent stages.

[0028] <Stage 103> The application server 3 functions as the central hub of the entire maintenance support automated response system 1, managing the data and information necessary to process customer inquiries. First, application server 3 accesses customer information DB21 to retrieve basic information about the customer (customer ID (Identifier), customer name, owned machine name, contract details, etc.). Note that within customer information DB21, the section that stores information for a specific customer is denoted as "Customer A Information DB21a," for example. This prepares the basic information necessary to provide personalized responses to each customer. Next, the application server 3 forwards the customer's question to the chatbot application 11 via the customer question acquisition unit 13.

[0029] <Stage 104> The chatbot application 11 receives questions from customers via the customer question acquisition unit 13 and analyzes their content. During the analysis process, the question distribution agent 14 analyzes the question content in detail in order to determine which database information should be retrieved from.

[0030] In this process, the chatbot application 11 uses a question distribution agent 14 to retrieve appropriate information from multiple databases. The question distribution agent 14 obtains the necessary information from one or more of the general-purpose knowledge database 22, the maintenance domain knowledge database 23, and the customer machine-specific knowledge database 24. By collecting knowledge from multiple sources rather than relying on a single database in this way, a wider range of accurate answers becomes possible.

[0031] The General Knowledge DB22 is a database that stores knowledge that does not belong to a specific domain, and it stores general knowledge about technical information and basic operating principles. The General Knowledge DB22 can also handle questions that are not directly related to machine maintenance. The Maintenance Domain Knowledge Database (DB23) stores knowledge related to machine maintenance. For example, if the machine is a centrifugal compressor, the Maintenance Domain Knowledge Database (DB23) stores maintenance information and technical knowledge for centrifugal compressors, as well as failure modes and corresponding countermeasures. The Maintenance Domain Knowledge Database (DB23) provides appropriate answers to technical questions about the machine. The Customer Machine-Specific Knowledge Database (DB24) stores specific data about the customer's machine (maintenance history, operating history, datasheets, instruction manuals, troubleshooting information). The Customer Machine-Specific Knowledge Database (DB24) may store this specific data for each customer. In this case, a separate area within the Customer Machine-Specific Knowledge Database (DB24) dedicated to a specific customer is denoted as "Customer A Machine-Specific Knowledge Database (DB24a)". The Customer Machine-Specific Knowledge Database (DB24) generates customized answers for each customer.

[0032] <Stage 105> The information acquired by the question distribution agent 14 is passed to the answer generation unit 12. The answer generation unit 12 integrates the information from multiple databases received from the question distribution agent 14 and uses natural language processing to generate an answer in a format that is easy for the customer to understand. This allows the customer to quickly obtain specialized information regarding the maintenance of their machine.

[0033] For example, if a customer asks, "When should the next maintenance check be performed?", the answer generation unit 12 takes into account the maintenance history, operating history, and manufacturer-recommended maintenance intervals obtained from the customer machine-specific knowledge DB 24 to suggest the optimal inspection timing. Furthermore, the answer generation unit 12 also takes into account general maintenance cycle information obtained from the maintenance domain knowledge DB 23 to create the final answer.

[0034] As shown above, Figure 2 visually illustrates the overall configuration of the automated maintenance support response system 1, clearly showing how each component works in cooperation with others. The automated maintenance support response system 1 enables customers to quickly receive expert and personalized support regarding maintenance. The entire chatbot application 11, or a part of its configuration, can be implemented as artificial intelligence (AI) that takes questions as input and outputs answers. Various databases may be used to deep learn this AI. Components shown in Figure 1 that are not explained in Figure 2 will be described later.

[0035] Figure 3 shows the processing flow of Embodiment 1. This processing flow visually illustrates the series of processes from when a customer inputs a question into the maintenance support automated response system 1 until the final answer is provided. Due to space limitations, in many cases, the entity responsible for the processing is indicated in the rectangles showing each step in Figure 3 instead of the processing content itself.

[0036] In step S201, customer device 2 accepts customer input of maintenance-related questions via a browser or dedicated application. Customer device 2 then sends these questions to web server 4. In step S202, the web server 4 forwards the results of the HTTP request analysis and the question to the application server 3.

[0037] In step S203, the application server 3 accesses the customer information DB 21 and obtains necessary information about the customer (customer ID, customer name, owned machine name, contract details, etc.). This information is essential for providing appropriate answers to questions and forms the basis for generating personalized answers in subsequent processing. Then, based on the obtained customer information, the application server 3 passes the question to the chatbot application 11.

[0038] In step S204, the chatbot application 11 receives a question from the customer via the customer question acquisition unit 13 and processes its content. Specifically, the chatbot application 11 sends the content of the question to the question distribution agent 14.

[0039] In step S205, the question distribution agent 14 determines which database to retrieve information from based on the content of the question. For example, if the information related to the question is spread across multiple databases, such as the general knowledge database 22, the maintenance domain knowledge database 23, and the customer machine-specific knowledge database 24, the question distribution agent 14 accesses each database simultaneously to collect the most appropriate information. This allows the maintenance support automated response system 1 to provide more accurate and comprehensive answers without relying on a single database.

[0040] In step S206, the question distribution agent 14 initiates a database search and retrieves information from the selected database. This search process is performed to quickly compare accumulated knowledge and customer machine-specific information to derive the most appropriate answer. In step S207, the answer generation unit 12 receives search results from the question sorting agent 14. Based on the acquired information, the answer generation unit 12 uses natural language processing to generate an answer in a format that is easy for the customer to understand.

[0041] In step S208, the answer generation unit 12 uses LLM to complement the answer generation process. Specifically, the answer generation unit 12 uses LLM to further refine the search results and provide contextually appropriate, natural, and accurate answers. This means that highly accurate answers to customer questions are possible. In step S209, the response generation unit 12 sends the generated response back to the customer device 2. Here, the response generation unit 12, acting as a chatbot, provides the response in a conversational format and sends maintenance-related information to the customer device 2 in real time. This allows the customer to quickly obtain specific guidance to proceed with maintenance work.

[0042] Figure 3 illustrates this processing flow, clearly showing the steps involved in generating appropriate answers to customer inquiries. It describes a process in which each component works collaboratively, resulting in an efficient and accurate overall operation.

[0043] <Summary of Embodiment 1> As is clear from the above, the maintenance support automated response system 1 of Embodiment 1 includes a maintenance domain knowledge DB 23 that stores knowledge, technical information, and troubleshooting information related to the general maintenance of mechanical equipment such as centrifugal compressors. The maintenance domain knowledge DB 23 includes parts replacement cycles, maintenance procedures, common failure modes, and countermeasures for machines such as centrifugal compressors, and functions as a foundation for providing general maintenance information that is not dependent on specific customer machines. This enables it to respond to questions about general machine maintenance.

[0044] Next, the maintenance support automated response system 1 of Embodiment 1 is equipped with a general-purpose knowledge DB 22 that stores general knowledge not belonging to a specific domain, enabling the system to handle a wide range of questions other than machine maintenance. The general-purpose knowledge DB 22 stores knowledge of technical information, operating principles, and general work procedures that are not directly related to machine maintenance, enabling flexible responses to various questions.

[0045] Furthermore, the maintenance support automated response system 1 of Embodiment 1 includes a customer machine-specific knowledge DB 24 that stores customer machine-specific operating history, maintenance history, data sheets showing machine specifications, instruction manuals, and troubleshooting information. The customer machine-specific knowledge DB 24 stores detailed operating history and maintenance history related to a specific machine owned by the customer and functions as a source of information for generating customized responses specific to the customer machine. This enables detailed support for individual machines.

[0046] As is clear from the above, the maintenance support automated response system 1 of Embodiment 1 includes a chatbot application 11 which contains a natural language processing module (not shown) that analyzes customer questions and determines which database to refer to based on the content of the question. This natural language processing module forms the basis for extracting relevant information from the appropriate database in response to customer questions and providing answers.

[0047] <Embodiment 2> Figure 4 is a diagram illustrating the functions of the main components of the automated maintenance support response system of Embodiment 2 in a stepwise manner. The automated maintenance support response system 1 of Embodiment 2 extracts information not only from natural language but also from additional documents such as tabular and graphical formats based on customer questions, and provides the optimal answer.

[0048] <Stage 301> Customer A's device 2a sends a question to the maintenance support automated answering system 1 via the web server 4. The web server 4 receives the HTTP request and routes it to the application server 3. <Stage 302> Application Server 3 receives a question from Customer A and manages which DB and agent to use to generate the answer. Customer Information DB 21 stores information such as customer ID, customer name, owned machine name, and contract details, and at this time provides the information necessary for customer identification and processing of the question.

[0049] <Stage 303> The chatbot application 11 receives a question from the customer and forwards it to the question distribution agent 14. The question distribution agent 14 analyzes the category of the customer's question and determines which agent to use and which database to refer to. Depending on the content of the question, information from multiple databases may be integrated.

[0050] The chatbot application 11 has an additional information acquisition agent 15 in addition to the question distribution agent 14. The additional information acquisition agent 15 plays an additional role in acquiring specific information. Agent α16 of the additional information acquisition agent 15 acquires troubleshooting information from the asset knowledge DB 25. The asset knowledge DB 25 stores technical information regarding countermeasures and repair procedures for specific defects in tabular or graphical format, providing accurate answers to customer questions.

[0051] Furthermore, agent β17, one of the additional information acquisition agents 15, acquires detailed machine specifications from the machine specifications DB26. The machine specifications DB26 stores information on machine design specifications, machine parameters, parts compatibility, and parts performance in tabular or graphical format. In this way, the agents work in conjunction with each DB to extract accurate information in response to specific questions.

[0052] <Stage 304> The response generation unit 12 generates a response in natural language format using LLM based on the information collected from the agent and the DB. This response goes beyond simply presenting data and provides comprehensive information that addresses the customer's question. <Stage 305> The customer machine-specific knowledge database 24 stores maintenance history, operating data, and detailed specifications for each customer's machine, and includes customer A machine-specific knowledge database 24a. This allows for the provision of personalized information for specific customer machines.

[0053] <Stage 306> The maintenance support automated response system 1 is equipped with a maintenance domain knowledge DB 23 that stores general knowledge about maintenance. By integrating this general knowledge with machine-specific information, more advanced responses are generated. <Stage 307> The non-AI action processing unit 18 performs a different action (for example, providing contact information) based on the question category in response to a specific question, rather than providing an AI answer, thereby achieving a more flexible response. With the above configuration, the maintenance support automated response system 1 shown in Figure 4 utilizes multiple databases and agents to provide the optimal answer to customer inquiries.

[0054] Figure 5 shows the processing flow of Embodiment 2. In step S401, customer A's device 2a receives HTTP requests from customer A regarding questions and maintenance. In step S402, the web server 4 receives an HTTP request and routes it to the application server 3.

[0055] In step S403, the application server 3 processes the question, accesses the customer information DB 21, and retrieves customer A's ID, contract details, and information on owned machinery. In step S404, the chatbot application 11 analyzes the content of the question. Based on customer A's question, the chatbot application 11 sends the analysis results to the question distribution agent 14.

[0056] In step S405, the question distribution agent 14 determines the category of the question and decides which database to access or which other agent to use. The question distribution agent 14 accesses the customer machine-specific knowledge DB 24 to retrieve historical data about customer A's machine and, if necessary, activates the additional information retrieval agent 15 to retrieve additional information.

[0057] In step S406, the additional information acquisition agent 15 decides to acquire additional data using other agents. In step S407, agent α16 retrieves troubleshooting information from the asset knowledge DB25. The asset knowledge DB25 includes past maintenance and repair history and is used as reference information to identify the cause and solution to the problem.

[0058] In step S408, agent β17 collects detailed technical data of the machine from the machine specification DB26. This data is used to provide specification information necessary for maintenance work and repairs. In step S409, the response generation unit 12 collects this information and generates an appropriate response.

[0059] In step S410, the response generation unit 12 uses LLM to create a response in natural language. In this process, the response generation unit 12 integrates specialized knowledge and technical information related to maintenance obtained by referring to the maintenance domain knowledge DB 23, as well as general knowledge obtained by referring to the general knowledge DB 22, into the response generated in step S409.

[0060] If an action other than an AI response is required, in step S411, the non-AI action processing unit 18 performs action processing based on the question category. At this time, the response generation unit 12 sends the final response after the action processing to the customer device 2 via the chatbot application 11. The customer receives the response in real time. As is clear from the above, the maintenance support automated response system 1 seamlessly integrates a series of processes such as question analysis, information acquisition, and response generation to provide accurate and prompt answers to customer maintenance-related questions.

[0061] <Summary of Embodiment 2> As is clear from the above, the maintenance support automatic response system 1 of Embodiment 2 includes an asset knowledge DB 25 that stores technical information regarding countermeasures and repair procedures for specific defects, and a machine specification DB 26 that stores information regarding machine design specifications, machine parameters, parts compatibility, and parts performance. The response generation unit 12 generates a response by integrating information obtained from at least one of the asset knowledge DB 25 and machine specification DB 26 according to the content of the question with information obtained from the maintenance domain knowledge DB 23, general-purpose knowledge DB 22, and customer machine-specific knowledge DB 24 according to the content of the question.

[0062] Furthermore, the asset knowledge DB25 and machine specification DB26 each store information in tabular and graph formats, and the response generation unit 12 generates responses in natural language format as well as in at least one of the tabular and graph formats.

[0063] Furthermore, the maintenance support automated response system 1 of Embodiment 2 realizes the maintenance domain knowledge DB 23, general-purpose knowledge DB 22, customer machine-specific knowledge DB 24, and response generation unit 12 through a combination of a large-scale language model and search extension generation.

[0064] <Embodiment 3> Figure 6 is a diagram illustrating the functions of the main components of the automated maintenance support response system of Embodiment 3 in a stepwise manner. The automated maintenance support response system 1 of Embodiment 3 physically separates and manages the data of customer A and customer B, preventing the accidental disclosure of one customer's information to another customer. In particular, in generative AI-based data processing, data leakage can be prevented by clearly switching the information source for each customer in order to minimize the risk of unintended data being accessed.

[0065] <Stage 501> First, Customer A and Customer B access the maintenance support automated response system 1 using Customer A's device 2a and Customer B's device 2b, respectively. Customer A's device 2a and Customer B's device 2b connect to the web server 4 and send HTTP requests. The web server 4 receives these HTTP requests and routes them to the application server 3.

[0066] <Stage 502> The application server 3 retrieves information about customer A from customer A information DB 21a and performs appropriate data processing based on the customer's questions (the same applies to customer B). Each customer's maintenance history and contract details are managed independently and are not mixed with data from other customers. Furthermore, generative AI such as LLM is used for this data processing, which can generate appropriate answers in natural language to customer questions.

[0067] <Stage 503> The chatbot application 11 receives and analyzes the customer's question. <Stage 504> The question distribution agent 14 analyzes the content of the customer's question and, based on that, accesses the appropriate database to generate an answer. For questions from customer A, the question distribution agent 14 accesses customer A machine-specific knowledge DB 24a, and for questions from customer B, it accesses customer B machine-specific knowledge DB 24b. This access method ensures that each customer's data is completely isolated and prevents it from being mistakenly provided to other customers.

[0068] <Stage 505> The additional information acquisition agent 15 is called by the question distribution agent 14 and collects the necessary information from each DB. In this process, agent α16 acquires troubleshooting information from the asset knowledge DB25, and agent β17 acquires detailed machine specification information from the machine specification DB26.

[0069] <Stage 506> The answer generation unit 12 integrates this information to generate the optimal answer. During the answer generation process, the answer generation unit 12 also acquires information from the maintenance domain knowledge DB 23 as needed. This ensures that the customer is provided with expert and accurate answers regarding machine maintenance.

[0070] <Stage 507> If a customer's question cannot be resolved by AI-generated answers alone, the answer generation unit 12 performs processing based on the question category and takes appropriate actions such as providing contact information. In this way, a system is realized that provides efficient and secure maintenance support while ensuring security and privacy, with customer information kept separate.

[0071] The automated maintenance support response system 1 manages data individually for each customer, preventing the mixing of information from different customers and minimizing the risk of data leakage during maintenance operations. Furthermore, the automated maintenance support response system 1 provides each customer with prompt and accurate maintenance information under proper data management.

[0072] <Summary of Embodiment 3> As is clear from the above, in the maintenance support automated response system 1 of Embodiment 3, the customer machine-specific knowledge DB 24 is independent for each customer. The response generation unit 12 uses the information stored in the customer X machine-specific knowledge DB 24x corresponding to a specific customer X to generate a response for that specific customer. This provides a response based on data specific to the customer machine and prevents confusion with information from other customers. This mechanism enables accurate and secure individualized support for each customer, thereby increasing customer satisfaction.

[0073] <Embodiment 4> Figure 7 shows the processing flow of Embodiment 4. The maintenance support automated response system 1 of Embodiment 4 updates the operation history, maintenance history, and failure history from the customer and generates more accurate responses based on them.

[0074] In step S601, the customer device 2 accepts the customer accessing the maintenance support automated response system 1 and inputting the latest operational history, maintenance history, and failure history information for its machine. In step S602, the customer device 2 stores this data in the customer machine-specific knowledge DB24. The customer machine-specific knowledge DB24 accumulates the operating history, maintenance history, failure history, etc., of the customer's machine.

[0075] In step S603, the customer machine-specific knowledge DB24 updates its stored operational history, maintenance history, and failure history. This update may be performed sequentially in response to the occurrence of a predetermined event, or it may be performed periodically. Specifically, the customer machine-specific knowledge DB24 may be updated whenever a malfunction occurs in the customer machine, or it may be updated during nighttime batch processing, end-of-month batch processing, etc. As a result, the customer machine-specific knowledge DB24 is always kept up-to-date. In step S604, the answer generation unit 12 generates an answer to the customer's question based on the updated data. Through this process, the maintenance support automated answer system 1 accurately reflects the latest operating status and maintenance history of the customer's machine and provides the customer with an appropriate answer. Since the updated data reflects the latest data from the customer, the answer also reflects the latest data from the customer and is highly accurate.

[0076] In step S605, customer device 2 ultimately outputs a highly accurate response. The customer can receive the optimal maintenance method and appropriate countermeasures for their machine in real time, based on the latest data. Since the features of Embodiment 4 are concise, the "Summary of Embodiment 4" will be omitted.

[0077] <Embodiment 5> Figure 8 is a diagram illustrating the functions of the main components of the automated maintenance support response system of Embodiment 5 step by step. The automated maintenance support response system 1 of Embodiment 5 combines internal information from the customer machine-specific knowledge DB 24 and external information from the external portal site 5 to generate comprehensive and expert answers.

[0078] <Stage 701> Customer device 2 accepts the customer accessing the customer machine-specific knowledge DB24 and referencing internal information. <Stage 702> The customer machine-specific knowledge DB24 stores information specific to the machine owned by the customer (operation history, maintenance history, failure history, etc.) and plays an important role in generating responses by the maintenance support automated response system 1.

[0079] <Stage 703> Furthermore, the maintenance support automated response system 1 also references external information from an external portal site 5. The external portal site 5 includes publicly available maintenance information, technical documents, industry standard guidelines, and other publicly available data related to machine maintenance. By using the external portal site 5, the maintenance support automated response system 1 can generate responses based on a wider range of information, rather than relying solely on internal information.

[0080] <Stage 704> The answer generation unit 12 integrates internal and external information to generate the optimal answer to the customer's question. The answer generation unit 12 refers not only to customer machine-specific data but also to external information, providing a comprehensive answer based on the latest industry information and standards, rather than simply an answer based on past data.

[0081] <Stage 705> Finally, the automated maintenance support response system 1 provides the customer with a comprehensive and expert response. This response utilizes both internal and external information, enabling the customer to provide the information necessary for their maintenance work with greater accuracy and expertise. In this way, it becomes possible to improve the efficiency of the customer's maintenance work and speed up problem resolution. This configuration allows the automated maintenance support response system 1 to provide more valuable answers to customers and improve reliability in machine maintenance by flexibly utilizing external information without relying solely on internal information. Since the features of Embodiment 5 are concise, the "Summary of Embodiment 4" will be omitted.

[0082] (Chat screen) Figure 9 is a diagram illustrating the chat screen. In the left column of the chat screen 31 displayed by the customer device 2, the response generation unit 12 displays questions 1, 2, and 3 (codes 41a, 41b, and 41c) posed by the customer in chronological order. In the right column of the chat screen 31, the response generation unit 12 displays its own responses 1, 2, and 3 (codes 42a, 42b, and 42c) in chronological order. Response 1 corresponds to question 1, response 2 corresponds to question 2, and response 3 corresponds to question 3. Note that questions do not necessarily take the form of interrogative sentences. Here, in a broad sense, the words posed by the customer are referred to as "questions." Similarly, responses do not necessarily take the form of answers to questions and may include follow-up questions to the customer. Here, in a broad sense, the words posed by the response generation unit 12 are referred to as "responses."

[0083] The general-purpose knowledge DB22 stores knowledge 32, "Identify the areas the customer is focusing on." Knowledge 32 is not knowledge belonging to a specific domain, but rather very general knowledge for eliciting customer requests. The maintenance domain knowledge DB23 stores knowledge 33, "If abnormal noises occur, check their frequency." Knowledge 33 is knowledge belonging to the maintenance domain and is used to narrow down the necessary maintenance. The customer machine-specific knowledge DB24 stores knowledge 34, "In one instance, an object was placed around customer A's outdoor unit, resulting in poor ventilation and causing the compressor to intermittently emit abnormal noises." Knowledge 34 is knowledge specific to that customer's machine and is used to specifically resolve customer-specific problems.

[0084] The answer generation unit 12 generates answer 1 for question 1 by referring to knowledge 32. The answer generation unit 12 generates answer 2 for question 2 by referring to knowledge 33. The answer generation unit 12 generates answer 3 for question 3 by referring to knowledge 34.

[0085] (Sharing knowledge with other customers) As is clear from Figure 9, whether a customer can obtain truly accurate answers depends on the quality of the knowledge 34 stored in the customer machine-specific knowledge DB 24. A particular customer A would be satisfied if they could obtain answers that reflect the knowledge 34 about their own machine (operational history, maintenance history, failure history, etc.), but it would be even more convenient if those answers also reflected the knowledge 34 about similar machines used by other customers.

[0086] Therefore, the customer question acquisition unit 13 may obtain prior consent from the customer to "share knowledge about similar machines among customers." Such customers are called "knowledge-opening customers." Now, suppose customer A changes from a regular customer to a knowledge-opening customer. Then, the question distribution agent 14 modifies the knowledge 34 as follows.

[0087] (Knowledge before correction 34) In one instance, objects were placed around customer A's outdoor unit, resulting in poor ventilation and causing the compressor to intermittently emit unusual noises. (Revised knowledge 34b) Objects were placed around the outdoor unit [ID=○○], which reduced ventilation and caused the compressor to intermittently emit unusual noises.

[0088] In the revised knowledge 34b, "Customer A" has been removed. Furthermore, an identifier that uniquely identifies the model (not the individual unit) of the "outdoor unit" has been assigned. As a result, the answer generation unit 12 will provide answers that reflect the revised knowledge 34b to questions from customers other than Customer A. If other customers are using that model, the answer generation unit 12 can provide more accurate answers to those customers. In this way, knowledge that appears to be unique to a particular customer can be anonymized and then reflected in answers to other customers.

[0089] (Application to centrifugal compressors) The following is an example of the operation of Maintenance Support Automatic Response System 1 applied to a centrifugal compressor. Unlike mass-produced products, centrifugal compressors are individually designed and manufactured and operated in customer plants for long periods of time. Centrifugal compressors are equipped with various sensors. These sensors collect operational data including inlet pressure, outlet pressure, operating temperature, cooling characteristics, rotational speed, and vibration trends of the rotor or bearing parts under load conditions.

[0090] This operational data is periodically stored in the Customer Machine-Specific Knowledge Database (DB24). DB24 stores information regarding the aging characteristics and operational status of centrifugal compressors, including design change history, modification history, and the availability of replacement and repair parts. This information is regularly updated by the system administrator to reflect the latest operational data, maintaining the overall accuracy and effectiveness of the system.

[0091] When a customer inputs a maintenance-related question into the automated maintenance support answering system 1, the system analyzes the content of the question using natural language processing technology. Then, the automated maintenance support answering system 1 searches for relevant information from the maintenance domain knowledge DB 23 and the customer machine-specific knowledge DB 24 to generate an answer to the analyzed question. In this process, the automated maintenance support answering system 1 makes recommendations for inspections and parts replacements based on pressure and temperature fluctuation data, proposes preventive maintenance based on vibration data, provides operating guides according to rotational conditions, and suggests surging countermeasures. For example, if the outlet pressure of a centrifugal compressor is fluctuating significantly over time, the automated maintenance support answering system 1 estimates that the cause is either a leak in the seals inside the centrifugal compressor or deterioration of the piping, and recommends specific inspection points and necessary parts replacements.

[0092] Furthermore, if the operating temperature trend data suggests a decrease in the efficiency of the cooling system, the maintenance support automated response system 1 will propose an inspection of the cooling system. In addition, if the vibration trend of the rotor or bearing parts changes, the maintenance support automated response system 1 will prompt early repair or parts replacement as preventive maintenance based on operational data such as the machine's operating hours. If a history of design changes or modifications is accumulated, the maintenance support automated response system 1 will make maintenance proposals that correspond to the latest information based on these changes. In this way, the maintenance support automated response system 1 improves the efficiency of customer maintenance work and the reliability of the compressor by providing the time-series information necessary to support the long-term operation of the centrifugal compressor.

[0093] Figure 10 is a hardware configuration diagram of a computer applied to an automated maintenance support response system. The application server 11 in Figure 1 is composed of one or more computers as shown in Figure 10. Each computer 900 shown in Figure 10 is configured to have a CPU 901, RAM 902, ROM 903, HDD 904, communication I / F 905, input / output I / F 906, and media I / F 907. The communication interface 905 is connected to an external communication device 915. The input / output interface 906 is connected to an input / output device 916. The media interface 907 reads and writes data to the recording medium 917. Furthermore, the CPU 901 executes the program (application) loaded into the RAM 902 to realize the various functions shown in Figure 1, such as the answer generation unit 12, the customer question acquisition unit 13, and the question distribution agent 14. This program can be distributed via a communication line or by recording it on a recording medium 917 such as a CD-ROM and distributing it.

[0094] 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 described in detail for the purpose of clearly illustrating the present invention, and the present invention is not necessarily limited to having all the configurations described. Furthermore, it is possible to add, delete, or replace some of the configurations of the embodiments described above with other configurations. [Explanation of Symbols]

[0095] 1. Maintenance Support Automated Response System 2 Customer devices 3. Application Server 4 Web Server 5. External portal sites 11. Chatbot Applications 12 Answer generation part 13. Customer Inquiry Acquisition Department 14 Question Sorting Agent 15 Additional Information Acquisition Agent 16 Agent α 17 Agent β 18 Action Processing Units Other Than AI 21. Customer Information Database (DB) 22. General-purpose knowledge database (DB) 23. Maintenance Domain Knowledge Database (DB) 24. Customer Machine-Specific Knowledge Database (DB) 25. Asset Knowledge Database (DB) 26. Machine Specification Database (DB)

Claims

1. A maintenance domain knowledge database that accumulates knowledge about machine maintenance, A general-purpose knowledge database that accumulates knowledge not belonging to a specific domain, A customer machine-specific knowledge database that accumulates the operating history of the customer's machine, An answer generation unit that generates answers in natural language to customer questions regarding machine maintenance based on information stored in the aforementioned maintenance domain knowledge database, the aforementioned general-purpose knowledge database, and the aforementioned customer machine-specific knowledge database, To be equipped, A maintenance support automated response system characterized by the following features.

2. An asset knowledge database that stores technical information regarding countermeasures and repair procedures for specific defects, A machine specifications database that stores information on machine design specifications, machine parameters, parts compatibility, and parts performance, Equipped with, The aforementioned response generation unit, The information obtained from at least one of the asset knowledge database and the machine specification database according to the content of the question is integrated with the information obtained from the maintenance domain knowledge database, the general-purpose knowledge database and the customer machine-specific knowledge database according to the content of the question to generate the answer. The maintenance support automatic response system according to claim 1, characterized by the following:

3. The aforementioned asset knowledge database and the aforementioned machine specification database are: Information is stored in tabular and graph formats. The aforementioned response generation unit, In addition to natural language format, the response will be generated in at least one of the following formats: tabular format and graph format. The maintenance support automatic response system according to claim 2, characterized by the above.

4. The aforementioned maintenance domain knowledge database, the aforementioned general-purpose knowledge database, and the aforementioned customer machine-specific knowledge database and the aforementioned answer generation unit, This will be achieved through a combination of large-scale language models and search extension generation. The maintenance support automatic response system according to claim 1, characterized by the following:

5. The customer machine-specific knowledge database is Each of the aforementioned customers is independent, The aforementioned response generation unit, Using information stored in a customer machine-specific knowledge database corresponding to a specific customer, generate a response for the said specific customer. The maintenance support automatic response system according to claim 1, characterized by the following:

6. The aforementioned customer machine-specific knowledge database is To periodically update the operating history, maintenance history, and failure history of the machines used by the customer, which are stored in the system, or to update them in the event of a predetermined event. The maintenance support automatic response system according to claim 1, characterized by the following:

7. The aforementioned response generation unit, To generate answers by referring to information provided on external portal sites related to machine maintenance. The maintenance support automatic response system according to claim 1, characterized by the following:

8. The aforementioned machine, It is a centrifugal compressor, The aforementioned customer machine-specific knowledge database is To accumulate the operating history of the centrifugal compressor regarding pressure, temperature, or vibration, The maintenance support automatic response system according to claim 1, characterized by the following:

9. The maintenance support automated response system is A maintenance domain knowledge database that accumulates knowledge about machine maintenance, A general-purpose knowledge database that accumulates knowledge not belonging to a specific domain, A customer machine-specific knowledge database that accumulates the operating history of the customer's machine, Based on the information accumulated, generate natural language answers to customer questions regarding machine maintenance. A maintenance support automated response method characterized by the following.

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