CN117216629A - Fault diagnosis method, device, equipment and storage medium - Google Patents
Fault diagnosis method, device, equipment and storage medium Download PDFInfo
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Abstract
The invention discloses a fault diagnosis method, device, equipment and storage medium. The method comprises the following steps: acquiring event data of the fault power generation equipment; determining target expert information according to the event data, and determining target operation and maintenance data from a plurality of operation and maintenance data by utilizing the target expert information; and determining a fault diagnosis result by utilizing the target operation and maintenance data and a preset multi-mode model. According to the technical scheme provided by the embodiment of the invention, the target expert information can be determined according to the data of the fault event, the target expert matched with the fault power generation equipment is notified remotely, the fault diagnosis result can be determined remotely by utilizing the target operation and maintenance data and the preset multi-mode model, the fault diagnosis expert does not need to be in close proximity to the fault site, the two modes of expert remote assistance and multi-mode model intelligent diagnosis are well combined, and the working efficiency and the accuracy of fault diagnosis and decision-making of the power generation equipment are improved.
Description
Technical Field
The present invention relates to the field of fault diagnosis technologies, and in particular, to a fault diagnosis method, device, apparatus, and storage medium.
Background
With the continuous development of information technology, the structure of power generation equipment is more and more complex, the automation degree is higher and higher, and a plurality of equipment relates to a plurality of professional fields such as machinery, electricity, fluid, power and automatic control, and along with the rapid development of the power market scale in recent years in China, power generation enterprises are rapidly expanding.
However, various key components in the equipment are mutually connected and mutually dependent, and the mechanism and the operation mode are complex, so that the fault diagnosis difficulty of the equipment is increased. Meanwhile, compared with various novel power generation equipment which is updated continuously and rapidly, the number and the culture of professional talents in the field of operation and maintenance of the power generation equipment are relatively lagged, the overall quality of on-site operation and maintenance personnel is not high, and generally, only some simple problems can be solved. Whenever a relatively complex fault occurs in the equipment, a plurality of relevant diagnostic experts are required to visit the fault site for consultation, which is difficult to realize.
Disclosure of Invention
The invention provides a fault diagnosis method, device, equipment and storage medium, which are used for solving the problem that a diagnosis expert is required to approach a fault site whenever a complex fault is faced.
In a first aspect, the present invention provides a method for fault diagnosis, comprising:
acquiring event data of the fault power generation equipment;
determining target expert information according to the event data, and determining target operation and maintenance data from a plurality of operation and maintenance data by utilizing the target expert information, wherein the target operation and maintenance data are operation and maintenance data associated with the fault power generation equipment, and the target expert information is expert information associated with a fault type corresponding to the fault power generation equipment;
and determining a fault diagnosis result by utilizing the target operation and maintenance data and a preset multi-mode model.
In a second aspect, the present invention provides an apparatus for fault diagnosis, comprising:
the event data acquisition module is used for acquiring event data of the fault power generation equipment;
the operation and maintenance data determining module is used for determining target expert information according to the event data and determining target operation and maintenance data from a plurality of operation and maintenance data by utilizing the target expert information, wherein the target operation and maintenance data are operation and maintenance data associated with the fault power generation equipment, and the target expert information is expert information associated with a fault type corresponding to the fault power generation equipment;
and the diagnosis result determining module is used for determining a fault diagnosis result by utilizing the target operation and maintenance data and a preset multi-mode model.
In a third aspect, the present invention provides an electronic device comprising:
at least one processor;
and a memory communicatively coupled to the at least one processor;
wherein the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method of fault diagnosis of the first aspect described above.
In a fourth aspect, the present invention provides a computer readable storage medium storing computer instructions for causing a processor to execute the method of performing the fault diagnosis of the first aspect described above.
According to the fault diagnosis scheme provided by the invention, event data of the fault power generation equipment are obtained, target expert information is determined according to the event data, and target operation and maintenance data is determined from a plurality of operation and maintenance data by utilizing the target expert information, wherein the target operation and maintenance data are data of operation and maintenance related to the fault power generation equipment, the target expert information is information of an expert related to a fault type corresponding to the fault power generation equipment, and a fault diagnosis result is determined by utilizing the target operation and maintenance data and a preset multi-mode model. By adopting the technical scheme, the target expert information can be determined according to the data of the fault event, the target expert matched with the fault power generation equipment is notified remotely, the screening of operation and maintenance data can be completed through the remote assistance of the target expert, the target operation and maintenance data are obtained, finally the fault diagnosis result can be determined remotely by utilizing the target operation and maintenance data and a preset multi-mode model, the fault diagnosis expert is not required to be in close to a fault site, and the two modes of remote assistance of the expert and intelligent diagnosis of the multi-mode model are well combined, so that the working efficiency and the accuracy of fault diagnosis and decision of the power generation equipment are improved.
It should be understood that the description in this section is not intended to identify key or critical features of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for fault diagnosis according to a first embodiment of the present invention;
FIG. 2 is a schematic flow chart of fault diagnosis of a power generation device according to a first embodiment of the present invention;
FIG. 3 is a flow chart of a method for fault diagnosis according to a second embodiment of the present invention;
fig. 4 is a schematic structural view of a fault diagnosis apparatus according to a third embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electronic device according to a fourth embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. In the description of the present invention, unless otherwise indicated, "a plurality" means two or more. "and/or", describes an association relationship of an association object, and indicates that there may be three relationships, for example, a and/or B, and may indicate: a exists alone, A and B exist together, and B exists alone. The character "/" generally indicates that the context-dependent object is an "or" relationship. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
Fig. 1 is a flowchart of a method for diagnosing a fault in a power generating apparatus according to an embodiment of the present invention, where the method may be performed by a fault diagnosing apparatus, and the fault diagnosing apparatus may be implemented in hardware and/or software, and the fault diagnosing apparatus may be configured in an electronic apparatus, and the electronic apparatus may be configured by two or more physical entities or may be configured by one physical entity.
As shown in fig. 1, the method for diagnosing a fault provided in the first embodiment of the present invention specifically includes the following steps:
s101, acquiring event data of the fault power generation equipment.
Fig. 2 is a schematic flow chart of fault diagnosis of a power generation device, in this embodiment, as shown in fig. 2, an event collector may be arranged in the cloud in advance, and when a fault that cannot be resolved in the field occurs in the power generation device, the event collector may immediately collect event data of the fault power generation device, at which time remote fault diagnosis starts. The event data may include data such as fault time, fault type, and fault range.
S102, determining target expert information according to the event data, and determining target operation and maintenance data from a plurality of operation and maintenance data by utilizing the target expert information, wherein the target operation and maintenance data are operation and maintenance data associated with the fault power generation equipment, and the target expert information is information of an expert associated with a fault type corresponding to the fault power generation equipment.
In the present embodiment, the target expert information may be determined using event data. For example, as shown in fig. 2, after diagnosis starts, relevant experts may be matched first, that is, the fault type included in the event data is matched with expert information stored in advance in an expert database deployed in the cloud, and the expert information that is successfully matched is determined as target expert information, where the expert information may include information such as the fault type that the expert is good at solving. The operation and maintenance data of the power generation equipment can be uploaded to a remote database deployed at the cloud in advance in real time. As shown in fig. 2, the relevant data may be matched, that is, the expert side corresponding to the target expert information is notified, and the expert team on the expert side may screen out the data associated with the faulty power generating device from the plurality of operation and maintenance data stored in the remote database, where the data is the target operation and maintenance data. The expert side may be an electronic device with a communication function, and the expert database may manage and maintain expert information.
S103, determining a fault diagnosis result by utilizing the target operation and maintenance data and a preset multi-mode model.
In this embodiment, the target operation data may be input into a preset multi-mode model, and the fault diagnosis result may be determined according to the result output by the model, if the model output is an X fault, the fault diagnosis result may be directly determined as an X fault. The multi-modal model can be understood as an artificial intelligence model, which can accept a plurality of different input modes, such as language, image, voice, video and the like, and can output recognition results of different modalities.
According to the fault diagnosis method provided by the embodiment of the invention, event data of the fault power generation equipment are obtained, target expert information is determined according to the event data, and target operation and maintenance data is determined from a plurality of operation and maintenance data by utilizing the target expert information, wherein the target operation and maintenance data are data of operation and maintenance related to the fault power generation equipment, the target expert information is information of an expert related to a fault type corresponding to the fault power generation equipment, and a fault diagnosis result is determined by utilizing the target operation and maintenance data and a preset multi-mode model. According to the technical scheme, the target expert information can be determined according to the data of the fault event, the target expert matched with the fault power generation equipment is notified remotely, then screening of operation and maintenance data can be completed through remote assistance of the target expert, the target operation and maintenance data are obtained, finally the fault diagnosis result can be determined remotely by utilizing the target operation and maintenance data and a preset multi-mode model, the fault diagnosis expert does not need to be in close to a fault site, and the two modes of expert remote assistance and multi-mode model intelligent diagnosis are well combined, so that the working efficiency and the accuracy of fault diagnosis and decision-making of the power generation equipment are improved.
Optionally, before the determining the target expert information according to the event data, the method further includes: and acquiring expert information of a fault handling expert, and storing the expert information into a preset expert database, wherein the expert information at least comprises information of power generation equipment faults which are good at being handled by the fault handling expert. The method has the advantages that the expert information can be read and matched at any time by creating the preset expert database in advance, and the fault processing efficiency is improved.
Specifically, as shown in fig. 2, an expert database, that is, a preset expert database, may be previously established, and expert information of fault handling experts acquired in advance may be stored in the expert database. The expert information at least comprises information such as fault types and fault reasons of faults of the power generation equipment which are good at being processed by the fault processing expert.
Optionally, before the acquiring the event data of the fault generating device, the method further includes: judging whether a target fault exists, wherein the target fault is a fault of power generation equipment which cannot be solved on the current site; the obtaining the event data of the fault power generation equipment comprises the following steps: and if the target fault exists, acquiring event data of the fault power generation equipment corresponding to the target fault. The advantage of this arrangement is that by determining the target fault, the fault that can be resolved in the field can be removed, so that expert resources are not wasted.
Specifically, when a target fault exists, the fault is complex, and on-site operation and maintenance personnel cannot solve the fault, and event data of the fault power generation equipment corresponding to the target fault needs to be acquired at the moment so as to apply for related experts to assist in solving the fault.
Example two
Fig. 3 is a flowchart of a fault diagnosis method provided by the second embodiment of the present invention, and the technical solution of the embodiment of the present invention is further optimized based on the foregoing alternative technical solutions, and a specific way of diagnosing a fault of a power generating device is provided.
Optionally, the determining the fault diagnosis result by using the target operation and maintenance data and a preset multi-mode model includes: determining a target model from a plurality of preset multi-mode models according to the target operation and maintenance data; inputting the target operation and maintenance data into the target model to obtain a multi-mode fusion diagnosis result; and determining a fault diagnosis result by using the target expert information and the multi-mode fusion diagnosis result. The method has the advantages that the preliminary result of fault diagnosis, namely the multi-mode fusion diagnosis result, can be obtained by inputting the target operation data into the target model, and then the accurate fault diagnosis result can be obtained by utilizing the expert side corresponding to the target expert information and the multi-mode fusion diagnosis result, so that compared with the situation that the expert goes to the site to participate in the fault diagnosis, the method is more convenient and faster, and the fault diagnosis time and the economic cost are saved.
Optionally, the determining the target expert information according to the event data includes: and matching the fault type in the event data with the information of the power generation equipment faults which are good for processing by the fault processing expert in the preset expert database, and determining target expert information according to a matching result. The advantage of this is that by matching the event data with the data in the preset expert database, the target expert information associated with the type of failure of the failed power generation apparatus can be quickly determined.
Optionally, the determining the target dimension data from the plurality of dimension data by using the target expert information includes: and sending the plurality of operation and maintenance data to a target expert side corresponding to the target expert information through a preset remote communication mode, and determining the target operation and maintenance data according to reply information which is returned by the target expert side and is input by the target expert remotely, wherein the operation and maintenance data are multi-mode data. The advantage of this arrangement is that by sending the operation data to the target expert side and obtaining the reply information returned by the target expert side, the expert can remotely assist in diagnosing faults.
As shown in fig. 3, a fault diagnosis method provided in the second embodiment of the present invention specifically includes the following steps:
s201, judging whether a target fault exists, if so, executing step 202, and if not, repeating step 201.
S202, acquiring event data of the fault power generation equipment corresponding to the target fault.
S203, matching the fault type in the event data with information of the power generation equipment faults which are good for processing by the fault processing expert in a preset expert database, and determining target expert information according to a matching result.
For example, if the fault type in the event data is a, and the information of the fault of the power generation equipment that the fault handling expert in the preset expert database is good at handling includes the fault type information, the fault type information that is consistent with or similar to the a may be screened out from the preset expert database, and the fault handling expert corresponding to the fault type information is the target expert information.
S204, sending the plurality of operation and maintenance data to a target expert side corresponding to the target expert information through a preset remote communication mode, and determining the target operation and maintenance data according to reply information which is returned by the target expert side and is input by the target expert remotely.
Wherein the operation and maintenance data are multi-modal data.
For example, a plurality of operation and maintenance data, such as operation and maintenance data of a fault power generation apparatus of the last year, may be transmitted to the target expert side. The target expert may return, through the target expert side, target operation and maintenance data, such as operation and maintenance data of a certain date or a certain period of time, where the operation and maintenance data has the highest failure association strength with the power generation equipment.
S205, determining a target model from a plurality of preset multi-mode models according to the target operation and maintenance data.
Specifically, the target operation and maintenance data may be data of a text, a field image, a video, a sound and other modes, so that the most suitable multi-mode model can be determined according to the mode of the target operation and maintenance data, for example, if the mode of the target operation and maintenance data is the text and the image, the CLIP model can be determined as the target model.
Optionally, the target model includes at least one of a PolyVit model, a MURAL model, and a CNN-ViT model. The advantage of this arrangement is that the operation and maintenance data in various modalities can be processed efficiently and accurately.
In particular, the PolyVit model is adept at processing video and audio data. The MURAL (multi-modal, multitask Retrieval Across Languages, cross-language multi-modal, multi-tasking) model is a model for image and text matching that applies multi-tasking learning to image and text pairs, in combination with translation pairs covering over 100 languages, that allows users to express through images those words that cannot be directly translated into the target language. The CNN in the CNN-ViT model represents a convolutional neural network, the CNN-ViT model combines the local processing capacity of an efficient convolutional layer and the global coding capacity of a light-duty transducer model, and the accuracy of the output result of the model is high. As shown in fig. 2, a plurality of multi-modal models may be stored in advance in a diagnosis model library deployed in the cloud, and the number of target models may be a plurality of models, that is, a polymit model, a MURAL model, a CNN-ViT model, and the like. The diagnosis model library can realize the creation, training, calculation, storage and the like of various multi-mode models.
S206, inputting the target operation and maintenance data into a target model to obtain a multi-mode fusion diagnosis result.
For example, as shown in fig. 2, a part of the target motion data may be input into the polymit model, a part of the target motion data may be input into the MURAL model, and a part of the target motion data may be input into the CNN-ViT model, etc., so as to obtain a plurality of multi-modal fusion diagnosis results.
S207, determining a fault diagnosis result by using the target expert information and the multi-mode fusion diagnosis result.
Specifically, the multi-mode fusion diagnosis result can be sent to an expert side corresponding to the target expert information, and an expert team on the expert side can perform operations such as screening and judging on the multi-mode fusion diagnosis result, so that a fault diagnosis result is obtained.
Optionally, the determining the fault diagnosis result by using the target expert information and the multi-mode fusion diagnosis result includes: sending the multi-mode fusion diagnosis result to a target expert side corresponding to the target expert information through a preset remote communication mode, and receiving diagnosis opinion information which is returned by the target expert side and is input by a target expert remotely; and determining a fault diagnosis result according to the semantic recognition result of the diagnosis opinion information. The intelligent diagnosis system has the advantages that the two modes of expert remote diagnosis and multi-mode model intelligent diagnosis are well combined, so that the fault processing efficiency is improved, and the accuracy of fault diagnosis is ensured.
Specifically, the multimodal fusion diagnosis result can be sent to the target expert side corresponding to the target expert information through a preset remote communication mode such as mail. As shown in fig. 2, the expert at the expert side can combine the own professional experience and the multi-mode fusion diagnosis result, and return the diagnosis opinion information through the target expert side after the remote comprehensive research and diagnosis decision is performed on line. After the diagnosis opinion information is subjected to semantic recognition, a fault diagnosis result can be obtained. As shown in fig. 2, a corresponding diagnosis report may also be generated according to the fault diagnosis result, and fed back to the operation and maintenance personnel side of the fault site in time.
According to the fault diagnosis method provided by the embodiment of the invention, the event data is matched with the data in the preset expert database, the target expert information related to the fault type of the fault power generation equipment is rapidly determined, then the operation data is sent to the target expert side, the reply information returned by the target expert side is obtained, so that the expert can remotely assist in fault diagnosis, finally the target operation data is input into the target model to obtain a primary fault diagnosis result, namely a multi-mode fusion diagnosis result, and then the expert side corresponding to the target expert information and the multi-mode fusion diagnosis result are utilized to obtain an accurate fault diagnosis result.
Example III
Fig. 4 is a schematic structural diagram of a fault diagnosis device according to a third embodiment of the present invention. As shown in fig. 4, the apparatus includes: an event data acquisition module 301, a fortune dimension determination module 302, and a diagnostic result determination module 303, wherein:
the event data acquisition module is used for acquiring event data of the fault power generation equipment;
the operation and maintenance data determining module is used for determining target expert information according to the event data and determining target operation and maintenance data from a plurality of operation and maintenance data by utilizing the target expert information, wherein the target operation and maintenance data are operation and maintenance data associated with the fault power generation equipment, and the target expert information is expert information associated with a fault type corresponding to the fault power generation equipment;
and the diagnosis result determining module is used for determining a fault diagnosis result by utilizing the target operation and maintenance data and a preset multi-mode model.
According to the fault diagnosis device provided by the embodiment of the invention, the target expert information can be determined according to the data of the fault event, the target expert matched with the fault power generation equipment is notified remotely, the screening of operation and maintenance data can be completed through the remote assistance of the target expert, the target operation and maintenance data are obtained, finally, the fault diagnosis result can be determined remotely by utilizing the target operation and maintenance data and a preset multi-mode model, the fault diagnosis expert is not required to visit a fault site, and the two modes of remote assistance of the expert and intelligent diagnosis of the multi-mode model are well combined, so that the working efficiency and the accuracy of fault diagnosis and decision of the power generation equipment are improved.
Optionally, the diagnostic result determining module includes:
the model determining unit is used for determining a target model from a plurality of preset multi-mode models according to the target operation and maintenance data;
the multi-modal result determining unit is used for inputting the target operation data into the target model to obtain a multi-modal fusion diagnosis result;
and the diagnosis result determining unit is used for determining a fault diagnosis result by utilizing the target expert information and the multi-mode fusion diagnosis result.
Optionally, the determining the fault diagnosis result by using the target expert information and the multi-mode fusion diagnosis result includes: sending the multi-mode fusion diagnosis result to a target expert side corresponding to the target expert information through a preset remote communication mode, and receiving diagnosis opinion information which is returned by the target expert side and is input by a target expert remotely; and determining a fault diagnosis result according to the semantic recognition result of the diagnosis opinion information.
Optionally, the apparatus further comprises:
and the database determining module is used for acquiring expert information of a fault handling expert before determining target expert information according to the event data, and storing the expert information into a preset expert database, wherein the expert information at least comprises information of faults of the power generation equipment which are good for being handled by the fault handling expert.
Optionally, the operation and data determining module includes:
and the matching unit is used for matching the fault type in the event data with the information of the power generation equipment faults which are good for processing by the fault processing expert in the preset expert database, and determining target expert information according to a matching result.
Optionally, the operation and data determining module includes:
and the target data determining unit is used for sending the plurality of operation and maintenance data to a target expert side corresponding to the target expert information in a preset remote communication mode, and determining the target operation and maintenance data according to the reply information which is returned by the target expert side and is input by the target expert remotely, wherein the operation and maintenance data are multi-mode data.
Optionally, the apparatus further comprises:
the judging module is used for judging whether a target fault exists before the event data of the fault power generation equipment are acquired, wherein the target fault is the fault of the power generation equipment which cannot be solved on the current site.
Optionally, the event data obtaining module is specifically configured to obtain event data of the fault power generation device corresponding to the target fault if the information returned by the judging module is that the target fault exists.
Optionally, the target model at least comprises at least one of a PolyVit model, a MURAL model and a CNN-ViT model.
The fault diagnosis device provided by the embodiment of the invention can execute the fault diagnosis method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
Example IV
Fig. 5 shows a schematic diagram of an electronic device 40 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 5, the electronic device 40 includes at least one processor 41, and a memory communicatively connected to the at least one processor 41, such as a Read Only Memory (ROM) 42, a Random Access Memory (RAM) 43, etc., in which the memory stores a computer program executable by the at least one processor, and the processor 41 may perform various suitable actions and processes according to the computer program stored in the Read Only Memory (ROM) 42 or the computer program loaded from the storage unit 48 into the Random Access Memory (RAM) 43. In the RAM 43, various programs and data required for the operation of the electronic device 40 may also be stored. The processor 41, the ROM 42 and the RAM 43 are connected to each other via a bus 44. An input/output (I/O) interface 45 is also connected to bus 44.
Various components in electronic device 40 are connected to I/O interface 45, including: an input unit 46 such as a keyboard, a mouse, etc.; an output unit 47 such as various types of displays, speakers, and the like; a storage unit 48 such as a magnetic disk, an optical disk, or the like; and a communication unit 49 such as a network card, modem, wireless communication transceiver, etc. The communication unit 49 allows the electronic device 40 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processor 41 may be various general and/or special purpose processing components with processing and computing capabilities. Some examples of processor 41 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 41 performs the respective methods and processes described above, for example, a fault diagnosis method.
In some embodiments, the method of fault diagnosis may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as the storage unit 48. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 40 via the ROM 42 and/or the communication unit 49. When the computer program is loaded into RAM 43 and executed by processor 41, one or more steps of the above-described method of fault diagnosis may be performed. Alternatively, in other embodiments, processor 41 may be configured to perform the method of fault diagnosis in any other suitable manner (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for carrying out methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
The computer equipment provided by the above can be used for executing the fault diagnosis method provided by any embodiment, and has corresponding functions and beneficial effects.
Example five
In the context of the present invention, a computer-readable storage medium may be a tangible medium, which when executed by a computer processor, is adapted to perform a method of fault diagnosis, the method comprising:
acquiring event data of the fault power generation equipment;
determining target expert information according to the event data, and determining target operation and maintenance data from a plurality of operation and maintenance data by utilizing the target expert information, wherein the target operation and maintenance data are operation and maintenance data associated with the fault power generation equipment, and the target expert information is expert information associated with a fault type corresponding to the fault power generation equipment;
and determining a fault diagnosis result by utilizing the target operation and maintenance data and a preset multi-mode model.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The computer equipment provided by the above can be used for executing the fault diagnosis method provided by any embodiment, and has corresponding functions and beneficial effects.
It should be noted that, in the embodiment of the fault diagnosis apparatus, each unit and module included are only divided according to the functional logic, but not limited to the above-mentioned division, so long as the corresponding functions can be implemented; in addition, the specific names of the functional units are also only for distinguishing from each other, and are not used to limit the protection scope of the present invention.
Note that the above is only a preferred embodiment of the present invention and the technical principle applied. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, while the invention has been described in connection with the above embodiments, the invention is not limited to the embodiments, but may be embodied in many other equivalent forms without departing from the spirit or scope of the invention, which is set forth in the following claims.
Claims (10)
1. A method of fault diagnosis, comprising:
acquiring event data of the fault power generation equipment;
determining target expert information according to the event data, and determining target operation and maintenance data from a plurality of operation and maintenance data by utilizing the target expert information, wherein the target operation and maintenance data are operation and maintenance data associated with the fault power generation equipment, and the target expert information is expert information associated with a fault type corresponding to the fault power generation equipment;
and determining a fault diagnosis result by utilizing the target operation and maintenance data and a preset multi-mode model.
2. The method of claim 1, wherein determining a fault diagnosis result using the target operation and maintenance data and a preset multi-modal model comprises:
determining a target model from a plurality of preset multi-mode models according to the target operation and maintenance data;
inputting the target operation and maintenance data into the target model to obtain a multi-mode fusion diagnosis result;
and determining a fault diagnosis result by using the target expert information and the multi-mode fusion diagnosis result.
3. The method of claim 2, wherein said determining a fault diagnosis using said target expert information and said multimodal fusion diagnosis comprises:
sending the multi-mode fusion diagnosis result to a target expert side corresponding to the target expert information through a preset remote communication mode, and receiving diagnosis opinion information which is returned by the target expert side and is input by a target expert remotely;
and determining a fault diagnosis result according to the semantic recognition result of the diagnosis opinion information.
4. The method of claim 1, further comprising, prior to said determining target expert information from said event data:
acquiring expert information of a fault handling expert, and storing the expert information into a preset expert database, wherein the expert information at least comprises information of power generation equipment faults which are good at being handled by the fault handling expert;
the determining the target expert information according to the event data comprises the following steps:
and matching the fault type in the event data with the information of the power generation equipment faults which are good for processing by the fault processing expert in the preset expert database, and determining target expert information according to a matching result.
5. The method of any of claims 1-4, wherein determining target dimension data from a plurality of dimension data using the target expert information comprises:
and sending the plurality of operation and maintenance data to a target expert side corresponding to the target expert information through a preset remote communication mode, and determining the target operation and maintenance data according to reply information which is returned by the target expert side and is input by the target expert remotely, wherein the operation and maintenance data are multi-mode data.
6. The method of claim 1, further comprising, prior to the acquiring event data for the failed power plant:
judging whether a target fault exists, wherein the target fault is a fault of power generation equipment which cannot be solved on the current site;
the obtaining the event data of the fault power generation equipment comprises the following steps:
and if the target fault exists, acquiring event data of the fault power generation equipment corresponding to the target fault.
7. The method of claim 2, wherein the target model comprises at least one of a polymit model, a MURAL model, and a CNN-ViT model.
8. A fault diagnosis apparatus, comprising:
the event data acquisition module is used for acquiring event data of the fault power generation equipment;
the operation and maintenance data determining module is used for determining target expert information according to the event data and determining target operation and maintenance data from a plurality of operation and maintenance data by utilizing the target expert information, wherein the target operation and maintenance data are operation and maintenance data associated with the fault power generation equipment, and the target expert information is expert information associated with a fault type corresponding to the fault power generation equipment;
and the diagnosis result determining module is used for determining a fault diagnosis result by utilizing the target operation and maintenance data and a preset multi-mode model.
9. An electronic device, the electronic device comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method of fault diagnosis of any one of claims 1-7.
10. A computer readable storage medium storing computer instructions for causing a processor to perform the method of fault diagnosis of any one of claims 1-7.
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