A fault positioning method, device and equipment of a diesel engine and a storage medium

Abstract

The application discloses a diesel engine fault positioning method, device, equipment and storage medium. The diesel engine fault positioning method comprises the following steps: during the operation of a target diesel engine, at least two diesel engine state parameters of the target diesel engine are collected in real time according to at least two diesel engine state sensors; based on the parameter values of the diesel engine state parameters, a target state parameter and a non-target state parameter are determined from the at least two diesel engine state parameters; based on a particle swarm optimization algorithm, the target state parameter and the non-target state parameter are subjected to correlation analysis, and the parameter correlation degree of the target state parameter and at least one non-target state parameter is determined; and based on the parameter correlation degree, the diesel engine positions corresponding to the at least one non-target state parameter are sequentially subjected to fault troubleshooting. Through the technical scheme, the positioning efficiency of fault positioning is improved.

Description

Technical Field

[0001] This application relates to the field of mechanical fault diagnosis technology, specifically to a fault location method, device, equipment, and storage medium for a diesel engine. Background Technology

[0002] Nuclear power plant diesel generator sets consist of multiple complex systems. The most effective and intuitive method for evaluating the operating status of the set or diagnosing faults is to analyze its thermodynamic parameters. Common thermodynamic parameters include pressure, temperature, speed, and humidity, which comprehensively reflect the performance status of the diesel generator set. Once the generator set malfunctions, it will be directly reflected in changes in these thermodynamic parameters.

[0003] Diesel engines are crucial for ensuring the proper functioning of the fuel system, therefore timely fault diagnosis of diesel engines is of paramount importance. Summary of the Invention

[0004] This application provides a method, apparatus, device, and storage medium for locating faults in diesel engines, thereby improving the efficiency of locating diesel engine faults.

[0005] According to one aspect of this application, a fault location method for a diesel engine is provided, the method comprising:

[0006] During the operation of the target diesel engine, at least two diesel engine status parameters of the target diesel engine are collected in real time based on at least two diesel engine status sensors;

[0007] Based on the parameter values ​​of the diesel engine state parameters, a target state parameter and a non-target state parameter are determined from at least two diesel engine state parameters; wherein, the target state parameter refers to the diesel engine state parameter that is abnormal.

[0008] Based on the particle swarm optimization algorithm, a correlation analysis is performed on the target state parameters and non-target state parameters to determine the degree of parameter correlation between the target state parameters and at least one non-target state parameter.

[0009] Based on the correlation of the parameters, troubleshooting is performed on the diesel engine positions corresponding to at least one non-target state parameter in sequence.

[0010] According to another aspect of this application, a fault location device for a diesel engine is provided, the device comprising:

[0011] The parameter acquisition module is used to collect at least two diesel engine status parameters of the target diesel engine in real time based on at least two diesel engine status sensors during the operation of the target diesel engine.

[0012] The target parameter determination module is used to determine a target state parameter and a non-target state parameter from at least two diesel engine state parameters based on the parameter values ​​of the diesel engine state parameters; wherein, the target state parameter refers to the diesel engine state parameter that is abnormal;

[0013] The correlation determination module is used to perform correlation analysis on the target state parameters and non-target state parameters based on the particle swarm optimization algorithm, and determine the degree of correlation between the target state parameters and at least one non-target state parameter.

[0014] The fault diagnosis module is used to perform fault diagnosis on the diesel engine location corresponding to at least one non-target state parameter in sequence based on the correlation degree of the parameters.

[0015] According to another aspect of this application, an electronic device is provided, the electronic device comprising:

[0016] One or more processors;

[0017] Memory, used to store one or more programs;

[0018] When the one or more programs are executed by the one or more processors, the one or more processors implement any of the diesel engine fault location methods provided in the embodiments of this application.

[0019] According to another aspect of this application, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements any of the diesel engine fault location methods provided in the embodiments of this application.

[0020] According to another aspect of this application, a computer program product is provided, including a computer program that, when executed by a processor, implements any of the diesel engine fault location methods provided in the embodiments of this application.

[0021] This application, when locating faults in a diesel engine, analyzes the correlation between target state parameters and non-target state parameters to determine the degree of parameter association with the target state parameters, expands the fault location judgment conditions, identifies non-target state parameters that are related to the target state parameters, more accurately locates the fault location of the diesel engine, narrows the fault investigation scope, and improves the fault location efficiency. Attached Figure Description

[0022] Figure 1 This is a flowchart of a diesel engine fault location method according to Embodiment 1 of this application;

[0023] Figure 2 This is a flowchart of a diesel engine fault location method according to Embodiment 2 of this application;

[0024] Figure 3 This is a structural schematic diagram of a fault location device for a diesel engine according to Embodiment 3 of this application;

[0025] Figure 4 This is a schematic diagram of the structure of an electronic device that implements the fault location method for a diesel engine according to Embodiment 4 of this application. Detailed Implementation

[0026] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0027] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0028] Example 1

[0029] Figure 1 This is a flowchart of a diesel engine fault location method according to Embodiment 1 of this application. This embodiment is applicable to fault location of a diesel engine that is malfunctioning while in operation. The fault location can be performed by a diesel engine fault location device, which can be implemented in hardware and / or software and can be configured in a computer device, such as a server. Figure 1 As shown, the method includes:

[0030] S110. During the operation of the target diesel engine, at least two diesel engine status parameters of the target diesel engine are collected in real time based on at least two diesel engine status sensors.

[0031] The target diesel engine can refer to the diesel engine for which fault monitoring is to be performed.

[0032] It should be noted that the diesel engine condition sensors may include vibration sensors, cylinder pressure sensors, temperature sensors, and speed sensors, etc., and those skilled in the art can make adaptive settings according to the type of target diesel engine and the diesel engine operating environment.

[0033] Optionally, based on at least two diesel engine status sensors, at least two diesel engine status parameters of the target diesel engine are collected in real time, including: within the target acquisition period, based on at least two diesel engine status sensors, at a preset acquisition frequency, at least two diesel engine status parameters of the target diesel engine are collected in real time, and a diesel engine status parameter sequence corresponding to each diesel engine status sensor is generated.

[0034] Specifically, within the same target acquisition period, at least two types of diesel engine status sensors can be deployed at a preset acquisition frequency to collect at least two types of diesel engine status parameters of the target diesel engine in real time, generating a diesel engine status parameter sequence for each diesel engine status sensor within the target acquisition period. It should be noted that the diesel engine status parameters in the sequence can be arranged according to the order of their acquisition timestamps.

[0035] S120. Based on the parameter values ​​of the diesel engine state parameters, determine the target state parameter and the non-target state parameter from at least two diesel engine state parameters.

[0036] Among them, the target state parameter can refer to the state parameter of the diesel engine that is abnormal, and the non-target state parameter can refer to the state parameter of the diesel engine that is not abnormal.

[0037] Specifically, in this embodiment of the invention, the diesel engine status parameters collected by the diesel engine status sensor can be monitored in real time. If the parameter value of the diesel engine status parameter collected by the diesel engine status sensor is not within the normal parameter threshold range corresponding to the diesel engine status sensor, then the diesel engine status parameter is determined as the target status parameter, and the other types of diesel engine status parameters are determined as non-target status parameters. Optionally, different diesel engine status sensors correspond to different normal parameter threshold ranges. The normal parameter threshold range can be used to characterize the range of parameter values ​​collected by the diesel engine status sensor under normal operating conditions. The normal parameter threshold range can be adaptively set according to those skilled in the art.

[0038] S130. Based on the particle swarm optimization algorithm, perform correlation analysis on the target state parameters and non-target state parameters to determine the degree of parameter correlation between the target state parameters and at least one non-target state parameter.

[0039] Among them, the degree of parameter correlation can be used to characterize the parameter dependency relationship between non-target state parameters and target state parameters.

[0040] It should be noted that when a diesel engine experiences some complex faults, it is difficult to locate the fault using a single diesel engine status parameter. Introducing other diesel engine status parameters that are closely related to the abnormal diesel engine status parameter can narrow down the range of possible causes or locations of the fault.

[0041] S140. Based on the degree of parameter correlation, troubleshoot the diesel engine position corresponding to at least one non-target state parameter in sequence.

[0042] Specifically, after determining the degree of correlation between the target state parameters and the other non-target state parameters, the non-target state parameters can be prioritized according to the degree of correlation. The non-target state parameters with a higher degree of correlation are ranked first, and the fault location of the target diesel engine is prioritized based on the non-target state parameters with a higher degree of correlation and the target state parameters.

[0043] This application, when locating faults in a diesel engine, analyzes the correlation between target state parameters and non-target state parameters to determine the degree of parameter association with the target state parameters, expands the fault location judgment conditions, identifies non-target state parameters that are related to the target state parameters, more accurately locates the fault location of the diesel engine, narrows the fault investigation scope, and improves the fault location efficiency.

[0044] Example 2

[0045] Figure 2 This is a flowchart of a diesel engine fault location method according to Embodiment 2 of this application. Based on the technical solutions of the above embodiments, this embodiment refines the step of "conducting correlation analysis on target state parameters and non-target state parameters based on particle swarm optimization algorithm to determine the degree of parameter correlation between the target state parameter and at least one non-target state parameter" into "setting a corresponding particle swarm for each diesel engine state parameter and randomly initializing the particle swarm; generating a position information matrix corresponding to each diesel engine state parameter based on the particle positions in the particle swarm corresponding to the diesel engine state parameter; determining the degree of parameter correlation between the target state parameter and at least one non-target state parameter based on the state parameter matrix and position information matrix corresponding to the target state parameter, and the state parameter matrix and position information matrix corresponding to the non-target state parameter." It should be noted that for parts not detailed in this embodiment, please refer to the relevant descriptions in other embodiments. Figure 2 As shown, the method includes:

[0046] S210. During the operation of the target diesel engine, at least two diesel engine status parameters of the target diesel engine are collected in real time based on at least two diesel engine status sensors.

[0047] S220. Based on the parameter values ​​of the diesel engine state parameters, determine the target state parameter and the non-target state parameter from at least two diesel engine state parameters.

[0048] S230. Set a corresponding particle swarm for each diesel engine state parameter and randomly initialize the particle swarm.

[0049] Optionally, a corresponding particle swarm can be set for each diesel engine state parameter, including: determining the number of particles in the particle swarm corresponding to each diesel engine state parameter based on the data scale of the diesel engine state parameters collected by the diesel engine state sensor.

[0050] S240. Generate a position information matrix corresponding to each diesel engine state parameter based on the particle positions in the particle swarm corresponding to the diesel engine state parameters.

[0051] Specifically, based on the data scale corresponding to each diesel engine state parameter, i.e. the amount of data in the diesel engine state parameter sequence, the number of particles in the particle swarm corresponding to each diesel engine state parameter can be determined, and the particles in the particle swarm can be randomly initialized to determine the initial position of each particle; and based on the initial position information of each particle, a position information matrix corresponding to each diesel engine state parameter can be generated.

[0052] S250. Based on the state parameter matrix and position information matrix corresponding to the target state parameter, and the state parameter matrix and position information matrix corresponding to the non-target state parameter, determine the degree of parameter correlation between the target state parameter and at least one non-target state parameter.

[0053] The state parameter matrix corresponding to the target state parameter can be a parameter matrix generated from the diesel engine state parameter sequence corresponding to the target state parameter, and the state parameter matrix corresponding to the non-target state parameter can be a parameter matrix generated from the diesel engine state parameter sequence corresponding to the non-target state parameter. It should be noted that the matrix parameters in the state parameter matrix and the position information matrix corresponding to each type of diesel engine state parameter are in one-to-one correspondence; that is, both the state parameter matrix and the position information matrix are generated based on the parameter sequence corresponding to the diesel engine state parameter sequence.

[0054] Optionally, based on the state parameter matrix and position information matrix corresponding to the target state parameter, and the state parameter matrix and position information matrix corresponding to the non-target state parameter, the parameter correlation degree between the target state parameter and at least one non-target state parameter is determined, including: solving the correlation objective function according to the state parameter matrix and position information matrix corresponding to the target state parameter, and the state parameter matrix and position information matrix corresponding to the non-target state parameter, to determine the first projection vector of the position information matrix corresponding to the target state parameter, and the second projection vector of the position information matrix corresponding to the non-target state parameter; determining the candidate parameter correlation degree between the target state parameter and the non-target state parameter according to the first projection vector and the second projection vector, and the position information matrix corresponding to the target state parameter and the non-target state parameter; after the number of iterations of the particle swarm reaches a preset number of iterations, the candidate parameter correlation degree generated in the last iteration is taken as the target parameter correlation degree between the target state parameter and the non-target state parameter.

[0055] The correlation objective function can be used to determine the location information matrix corresponding to the target state parameters and the location information matrix corresponding to the non-target state parameters, as well as the optimal projection vector for each. It should be noted that the optimal projection vector maximizes the function value of the correlation objective function, and the preset number of iterations can be adaptively set according to those skilled in the art.

[0056] In this embodiment of the invention, the position information matrix corresponding to the target state parameter and the position information matrix corresponding to the non-target state parameter can be normalized and mean-reduced respectively to determine the projection vectors corresponding to each, and to determine the maximum spatial correlation between the two projection vectors.

[0057] Optionally, the correlation objective function can be expressed by the following formula:

[0058] ;

[0059] ;

[0060] ;

[0061] ;

[0062] Where w can be the projection vector of the position information matrix corresponding to the target state parameters, v can be the projection vector of the position information matrix corresponding to the non-target state parameters, N can be the data size of the target state parameters, A can be the position information matrix corresponding to the target state parameters, and B can be the position information matrix corresponding to the non-target state parameters.

[0063] Optionally, in this embodiment of the invention, the relevance objective function can be rewritten using the support vector machine optimization method. The rewritten relevance objective function can be expressed by the following formula:

[0064] ;

[0065] ;

[0066] Specifically, singular value decomposition can be used to solve the rewritten correlation objective function to determine the optimal projection vector.

[0067] By transforming the measurements from diesel engine condition sensors in the physical world into parameters or constraints for an optimization problem, and through the iterative optimization process of the particle swarm optimization algorithm, the parameter correlations between each parameter are determined, thereby improving the effectiveness and robustness of the optimization results.

[0068] Optionally, after determining the optimal projection vectors of the position information matrices corresponding to the target state parameters and the position information matrices corresponding to the non-target state parameters, the linear correlation between the two optimal projection vectors can be determined according to the correlation coefficient calculation function, and this linear correlation can be used as the degree of correlation of candidate parameters between the target state parameters and the non-target state parameters.

[0069] Optionally, in this embodiment of the invention, after determining the correlation degree between the candidate parameters of the target state parameter and the non-target state parameter in each iteration, the method further includes: updating the velocity and position of the particles in the particle swarm corresponding to the target state parameter and the non-target state parameter respectively.

[0070] Specifically, the position update formula and velocity update formula in the particle swarm optimization algorithm can be used to update the velocity and position of particles in the particle swarm in each iteration.

[0071] By updating the velocity and position of particles during the iteration process, the convergence speed, global search capability, and robustness of the particle swarm optimization algorithm are improved.

[0072] S260. Based on the degree of parameter correlation, troubleshoot the diesel engine position corresponding to at least one non-target state parameter in sequence.

[0073] This application embodiment uses a particle swarm optimization algorithm to quantify the correlation between target state parameters and multiple non-target state parameters, quickly locating the suspected fault location of the diesel engine, thereby improving the fault diagnosis efficiency of subsequent expert system intelligent algorithms.

[0074] Example 3

[0075] Figure 3This is a structural schematic diagram of a diesel engine fault location device according to Embodiment 3 of this application. It is applicable to fault location of a diesel engine experiencing an abnormality during operation. This diesel engine fault location device can be implemented in hardware and / or software and can be configured in computer equipment, such as a server. Figure 3 As shown, the device includes:

[0076] The parameter acquisition module 310 is used to collect at least two diesel engine status parameters of the target diesel engine in real time based on at least two diesel engine status sensors during the operation of the target diesel engine.

[0077] The target parameter determination module 320 is used to determine a target state parameter and a non-target state parameter from at least two diesel engine state parameters based on the parameter values ​​of the diesel engine state parameters; wherein, the target state parameter refers to the diesel engine state parameter that is abnormal.

[0078] The correlation determination module 330 is used to perform correlation analysis on the target state parameters and non-target state parameters based on the particle swarm optimization algorithm, and determine the degree of correlation between the target state parameters and at least one non-target state parameter.

[0079] The fault diagnosis module 340 is used to perform fault diagnosis on the diesel engine position corresponding to at least one non-target state parameter in sequence based on the correlation degree of the parameters.

[0080] This application, when locating faults in a diesel engine, analyzes the correlation between target state parameters and non-target state parameters to determine the degree of parameter association with the target state parameters, expands the fault location judgment conditions, identifies non-target state parameters that are related to the target state parameters, more accurately locates the fault location of the diesel engine, narrows the fault investigation scope, and improves the fault location efficiency.

[0081] Optionally, the association determination module 330 includes:

[0082] The particle initialization unit is used to set the corresponding particle swarm for each diesel engine state parameter and to randomly initialize the particle swarm.

[0083] The matrix generation unit is used to generate a position information matrix corresponding to each diesel engine state parameter based on the particle positions in the particle swarm corresponding to the diesel engine state parameters.

[0084] The parameter correlation degree determination unit is used to determine the parameter correlation degree between the target state parameter and at least one non-target state parameter based on the state parameter matrix and position information matrix corresponding to the target state parameter, and the state parameter matrix and position information matrix corresponding to the non-target state parameter.

[0085] Optionally, the parameter correlation degree determination unit can be specifically used for:

[0086] Based on the state parameter matrix and position information matrix corresponding to the target state parameter, and the state parameter matrix and position information matrix corresponding to the non-target state parameter, the correlation objective function is solved to determine the first projection vector of the position information matrix corresponding to the target state parameter and the second projection vector of the position information matrix corresponding to the non-target state parameter.

[0087] Based on the first projection vector and the second projection vector, as well as the position information matrix corresponding to the target state parameter and the position information matrix corresponding to the non-target state parameter, the degree of correlation between the candidate parameters between the target state parameter and the non-target state parameter is determined;

[0088] After the particle swarm reaches the preset number of iterations, the correlation degree of the candidate parameters generated in the last iteration is used as the correlation degree of the target parameters between the target state parameters and the non-target state parameters.

[0089] Optionally, the parameter correlation degree determination unit may also include:

[0090] The update sub-unit is used to update the velocity and position of the particles in the particle swarm corresponding to the target state parameter and the non-target state parameter respectively after determining the degree of correlation between the candidate parameters between the target state parameter and the non-target state parameter in each iteration.

[0091] Optionally, the particle initialization unit may be specifically used to: determine the number of particles in the particle swarm corresponding to each diesel engine state parameter based on the data scale of the diesel engine state parameters collected by the diesel engine state sensor.

[0092] Optionally, the parameter acquisition module 310 can be specifically used to: within the target acquisition period, based on at least two diesel engine status sensors, acquire at least two diesel engine status parameters of the target diesel engine in real time at a preset acquisition frequency, and generate a diesel engine status parameter sequence corresponding to each diesel engine status sensor.

[0093] The fault location device for diesel engines provided in this application can execute the fault location method for diesel engines provided in any embodiment of this application, and has the corresponding functional modules and beneficial effects for executing the fault location method for each diesel engine.

[0094] According to embodiments of this application, this application also provides an electronic device, a readable storage medium, and a computer program product.

[0095] Example 4

[0096] Figure 4 This is a schematic diagram of the structure of an electronic device 410 implementing the diesel engine fault location method according to embodiments of this application. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present application described and / or claimed herein.

[0097] like Figure 4 As shown, the electronic device 410 includes at least one processor 411 and a memory, such as a read-only memory 412 or a random access memory 413, communicatively connected to the at least one processor 411. The memory stores computer programs executable by the at least one processor. The processor 411 can perform various appropriate actions and processes based on the computer program stored in the read-only memory 412 or loaded from storage unit 418 into the random access memory 413. The random access memory 413 can also store various programs and data required for the operation of the electronic device 410. The processor 411, read-only memory 412, and random access memory 413 are interconnected via a bus 414. An input / output interface 415 is also connected to the bus 414.

[0098] Multiple components in electronic device 410 are connected to input / output interface 415, including: input unit 416, such as keyboard, mouse, etc.; output unit 417, such as various types of monitors, speakers, etc.; storage unit 418, such as disk, optical disk, etc.; and communication unit 419, such as network card, modem, wireless transceiver, etc. Communication unit 419 allows electronic device 410 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0099] Processor 411 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 411 include, but are not limited to, central processing units, graphics processing units, various special-purpose artificial intelligence computing chips, various processors running machine learning model algorithms, digital signal processors, and any suitable processor, controller, microcontroller, etc. Processor 411 performs the various methods and processes described above, such as fault location methods for diesel engines.

[0100] In some embodiments, the diesel engine fault location method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 418. In some embodiments, part or all of the computer program may be loaded into and / or installed on electronic device 410 via read-only memory 412 and / or communication unit 419. When the computer program is loaded into random access memory 413 and executed by processor 411, one or more steps of the diesel engine fault location method described above may be performed. Alternatively, in other embodiments, processor 411 may be configured as the diesel engine fault location method by any other suitable means (e.g., by means of firmware).

[0101] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays, application-specific integrated circuits (ASICs), application-specific standard products (ASICs), systems-on-a-chip (SoCs), payload programmable logic devices, computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0102] Computer programs used to implement the methods of this application may be written in any combination of one or more programming languages. These computer programs may be provided to the processor of a general-purpose computer, a special-purpose computer, or other programmable diesel engine fault location device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The computer programs may be executed entirely on the machine, partially on the machine, or as a standalone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0103] In the context of this application, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory, read-only memory, erasable programmable read-only memory, optical fibers, portable compact disk read-only memory, optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0104] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a cathode ray tube or liquid crystal display monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0105] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0106] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a host product within the cloud computing service system to address the shortcomings of traditional physical hosts and virtual private servers, such as high management difficulty and weak business scalability.

[0107] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this application can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this application can be achieved, and this is not limited herein.

[0108] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for fault location in a diesel engine, characterized in that, include: During the operation of the target diesel engine, at least two diesel engine status parameters of the target diesel engine are collected in real time based on at least two diesel engine status sensors; Based on the parameter values ​​of the diesel engine state parameters, a target state parameter and a non-target state parameter are determined from at least two diesel engine state parameters; wherein, the target state parameter refers to the diesel engine state parameter that is abnormal. Based on the particle swarm optimization algorithm, a correlation analysis is performed on the target state parameters and non-target state parameters to determine the degree of parameter correlation between the target state parameters and at least one non-target state parameter. Based on the correlation degree of the parameters, the faults of the diesel engine positions corresponding to at least one non-target state parameter are checked in turn. The step of performing correlation analysis on the target state parameters and non-target state parameters based on the particle swarm optimization algorithm to determine the degree of parameter correlation between the target state parameters and at least one non-target state parameter includes: Set a corresponding particle swarm for each diesel engine state parameter and randomly initialize the particle swarm; Based on the particle positions in the particle swarm corresponding to the diesel engine state parameters, a position information matrix corresponding to each diesel engine state parameter is generated. Based on the state parameter matrix and position information matrix corresponding to the target state parameter, and the state parameter matrix and position information matrix corresponding to the non-target state parameter, the correlation objective function is solved to determine the first projection vector of the position information matrix corresponding to the target state parameter and the second projection vector of the position information matrix corresponding to the non-target state parameter. Based on the first projection vector and the second projection vector, as well as the position information matrix corresponding to the target state parameter and the position information matrix corresponding to the non-target state parameter, the degree of correlation between the candidate parameters between the target state parameter and the non-target state parameter is determined. After the particle swarm reaches the preset number of iterations, the correlation degree of the candidate parameters generated in the last iteration is used as the correlation degree of the target parameters between the target state parameters and the non-target state parameters.

2. The method according to claim 1, characterized in that, In each iteration, after determining the correlation between candidate parameters and non-target state parameters, the following steps are also included: Update the velocity and position of the particles in the particle swarm corresponding to the target state parameter and the non-target state parameter, respectively.

3. The method according to claim 1, characterized in that, The step of setting a corresponding particle swarm for each diesel engine state parameter includes: Based on the data scale of the diesel engine state parameters collected by the diesel engine state sensor, the number of particles in the particle swarm corresponding to each diesel engine state parameter is determined.

4. The method according to claim 1, characterized in that, The step of collecting at least two diesel engine state parameters of the target diesel engine in real time based on at least two diesel engine state sensors includes: Within the target acquisition period, at least two diesel engine status parameters of the target diesel engine are acquired in real time at a preset acquisition frequency based on at least two diesel engine status sensors, generating a diesel engine status parameter sequence corresponding to each diesel engine status sensor.

5. A fault location device for a diesel engine, characterized in that, include: The parameter acquisition module is used to collect at least two diesel engine status parameters of the target diesel engine in real time based on at least two diesel engine status sensors during the operation of the target diesel engine. The target parameter determination module is used to determine a target state parameter and a non-target state parameter from at least two diesel engine state parameters based on the parameter values ​​of the diesel engine state parameters; wherein, the target state parameter refers to the diesel engine state parameter that is abnormal; The correlation determination module is used to perform correlation analysis on the target state parameters and non-target state parameters based on the particle swarm optimization algorithm, and determine the degree of correlation between the target state parameters and at least one non-target state parameter. The fault diagnosis module is used to perform fault diagnosis on the diesel engine location corresponding to at least one non-target state parameter in sequence based on the correlation degree of the parameters; The association determination module includes: The particle initialization unit is used to set the corresponding particle swarm for each diesel engine state parameter and to randomly initialize the particle swarm. The matrix generation unit is used to generate a position information matrix corresponding to each diesel engine state parameter based on the particle positions in the particle swarm corresponding to the diesel engine state parameters. The parameter correlation degree determination unit is specifically used for: Based on the state parameter matrix and position information matrix corresponding to the target state parameter, and the state parameter matrix and position information matrix corresponding to the non-target state parameter, the correlation objective function is solved to determine the first projection vector of the position information matrix corresponding to the target state parameter and the second projection vector of the position information matrix corresponding to the non-target state parameter. Based on the first projection vector and the second projection vector, as well as the position information matrix corresponding to the target state parameter and the position information matrix corresponding to the non-target state parameter, the degree of correlation between the candidate parameters between the target state parameter and the non-target state parameter is determined; After the particle swarm reaches the preset number of iterations, the correlation degree of the candidate parameters generated in the last iteration is used as the correlation degree of the target parameters between the target state parameters and the non-target state parameters.

6. An electronic device, characterized in that, include: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the fault location method for a diesel engine as described in any one of claims 1-4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the fault location method for a diesel engine as described in any one of claims 1-4.

8. A computer program product comprising a computer program that, when executed by a processor, implements the fault location method for a diesel engine according to any one of claims 1-4.

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