Marine air compressor fault diagnosis method, device, equipment and medium

Abstract

The embodiment of the invention discloses a marine air compressor fault diagnosis method, device and equipment and a medium. The method comprises the steps of obtaining current twinborn data corresponding to the air compressor, and determining a current running state and a current fault diagnosis result corresponding to the air compressor based on the current twinborn data and an air compressor twinborn model; reverse modeling is carried out based on the current fault diagnosis result, and a current air compressor fault model is determined; based on the current twin data and the current air compressor fault model, a reference operation state corresponding to the air compressor is determined; and if the current operation state and the reference operation state meet the preset consistency condition, the current fault diagnosis result is determined as a target fault diagnosis result corresponding to the air compressor. Through the technical scheme of the embodiment of the invention, the target fault diagnosis result corresponding to the air compressor can be accurately and conveniently determined, and the efficiency and accuracy of fault diagnosis of the marine air compressor are improved.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of computer technology, and in particular to a method, device, equipment and medium for diagnosing faults of a marine air compressor. Background Art

[0002] With the development of technology, equipment on ships tends to be automated, and equipment fault detection also tends to be automated.

[0003] At present, offline data is usually used for fault diagnosis of marine air compressors, which cannot achieve real-time diagnosis. In addition, in the process of using deep learning models for fault diagnosis of marine air compressors, as the equipment runs for a long time and the surrounding environment changes, the types of faults will gradually increase, resulting in inaccurate diagnosis of deep learning models. Summary of the invention

[0004] The embodiments of the present invention provide a method, device, equipment and medium for diagnosing faults of marine air compressors, so as to accurately and conveniently determine the target fault diagnosis result corresponding to the air compressor, thereby improving the efficiency and accuracy of diagnosing faults of marine air compressors.

[0005] In a first aspect, an embodiment of the present invention provides a method for diagnosing a fault of a marine air compressor, comprising:

[0006] Acquire current twin data corresponding to the air compressor, and determine the current operating state and current fault diagnosis result corresponding to the air compressor based on the current twin data and the air compressor twin model;

[0007] Perform reverse modeling based on the current fault diagnosis result to determine the current air compressor fault model;

[0008] Determine a reference operating state corresponding to the air compressor based on the current twin data and the current air compressor fault model;

[0009] If the current operating state and the reference operating state satisfy a preset consistency condition, the current fault diagnosis result is determined as a target fault diagnosis result corresponding to the air compressor.

[0010] Optionally, the method also includes: the current twin data includes: air compressor equipment data, sensor acquisition data and environmental data at the current moment; the air compressor equipment data includes: geometric parameters, material data and assembly relationships; the sensor acquisition data includes: current acquisition data, measurement point positioning and data acquisition interval; the environmental data includes: temperature, humidity and flow rate.

[0011] Optionally, the method further comprises: the air compressor twin model comprises: a geometric model, a fault diagnosis model and an evolution model;

[0012] The geometric model in the twin model of the air compressor is updated based on the air compressor equipment data to obtain an updated geometric model; fault diagnosis is performed based on the sensor acquisition data and the fault diagnosis model to determine the current fault diagnosis result corresponding to the air compressor; on the basis of the updated geometric model, the current operating status corresponding to the air compressor is determined based on the environmental data, the sensor acquisition data and the evolution model.

[0013] Optionally, the method also includes: establishing an environmental model for the current air compressor fault model based on the environmental data in the current twin data; establishing an operating mechanism model for the current air compressor fault model based on the air compressor equipment data in the current twin data; and determining the reference operating state corresponding to the air compressor based on the sensor acquisition data in the current twin data, the environmental model, the operating mechanism model and the current air compressor fault model.

[0014] Optionally, the method also includes: if the state difference between the current operating state and the reference operating state is less than a preset difference, determining that the current operating state and the reference operating state meet a preset consistency condition; and determining the current fault diagnosis result as the target fault diagnosis result corresponding to the air compressor.

[0015] Optionally, the method also includes: if the current operating state and the reference operating state do not meet the preset consistency condition, iteratively training the fault diagnosis model in the air compressor twin model based on the fault data of the current fault diagnosis result in the database to obtain a reconstructed fault diagnosis model.

[0016] In a second aspect, an embodiment of the present invention further provides a marine air compressor fault diagnosis device, the device comprising:

[0017] A current information determination module, used to obtain current twin data corresponding to the air compressor, and determine the current operating state and current fault diagnosis result corresponding to the air compressor based on the current twin data and the air compressor twin model;

[0018] A current air compressor fault model determination module, used to perform reverse modeling based on the current fault diagnosis result to determine the current air compressor fault model;

[0019] A reference operating state determination module, used to determine a reference operating state corresponding to the air compressor based on the current twin data and the current air compressor fault model;

[0020] The target fault diagnosis result determination module is used to determine the current fault diagnosis result as the target fault diagnosis result corresponding to the air compressor if the current operating state and the reference operating state meet a preset consistency condition.

[0021] In a third aspect, an embodiment of the present invention further provides an electronic device, the electronic device comprising:

[0022] one or more processors;

[0023] A memory for storing one or more programs;

[0024] When the one or more programs are executed by the one or more processors, the one or more processors implement the marine air compressor fault diagnosis method provided by any embodiment of the present invention.

[0025] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a marine air compressor fault diagnosis method as provided in any embodiment of the present invention.

[0026] In a fifth aspect, an embodiment of the present invention provides a computer program product, including a computer program, which, when executed by a processor, implements a marine air compressor fault diagnosis method as provided in any embodiment of the present invention.

[0027] The technical solution of the embodiment of the present invention obtains the current twin data corresponding to the air compressor, and determines the current operating state and current fault diagnosis result corresponding to the air compressor based on the current twin data and the twin model of the air compressor; performs reverse modeling based on the current fault diagnosis result to determine the current air compressor fault model; determines the reference operating state corresponding to the air compressor based on the current twin data and the current air compressor fault model; if the current operating state and the reference operating state meet the preset consistency conditions, the current fault diagnosis result is determined as the target fault diagnosis result corresponding to the air compressor, thereby accurately and conveniently determining the target fault diagnosis result corresponding to the air compressor, thereby improving the efficiency and accuracy of marine air compressor fault diagnosis.

[0028] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0030] Figure 1is a flow chart of a method for diagnosing faults of a marine air compressor provided in Embodiment 1 of the present invention;

[0031] Figure 2 is a flow chart of a method for diagnosing faults of a marine air compressor provided in Embodiment 2 of the present invention;

[0032] Figure 3 It is a structural schematic diagram of a marine air compressor fault diagnosis device provided by Embodiment 3 of the present invention;

[0033] Figure 4 It is a structural schematic diagram of an electronic device for implementing the marine air compressor fault diagnosis method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0034] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0035] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0036] Embodiment 1

[0037] Figure 1 A flow chart of a method for diagnosing a fault of a marine air compressor is provided for the first embodiment of the present invention. This embodiment is applicable to the case where the fault diagnosis result is detected online to obtain an accurate target fault diagnosis result. The method can be executed by a marine air compressor fault diagnosis device. The marine air compressor fault diagnosis device can be implemented in the form of hardware and / or software. The marine air compressor fault diagnosis device can be configured in an electronic device. Figure 1 As shown, the method includes:

[0038] S110. Obtain the current twin data corresponding to the air compressor, and determine the current operating status and current fault diagnosis result corresponding to the air compressor based on the current twin data and the air compressor twin model.

[0039] Among them, an air compressor may refer to a machine that compresses air to increase gas pressure or transport gas. In an embodiment of the present invention, an air compressor may refer to an air compressor entity, that is, a real air compressor. Twin data can be used to construct or update an air compressor twin model. The air compressor twin model can be used to synchronously map the air compressor entity and the operation status of the air compressor entity in a virtual space. The current operating state may refer to the operating state of the air compressor at the current moment. In an embodiment of the present invention, the compressed air output by the air compressor can be used as the current operating state of the air compressor. For example, the current operating state may include but is not limited to the gas volume, pressure value and water vapor content of the compressed air output by the air compressor. The current fault diagnosis result may refer to the fault diagnosis result of the air compressor at the current moment. For example, the current fault diagnosis result may be but is not limited to no fault and faulty. Faults can be divided into high exhaust temperature, overheating of the compressor and insufficient output pressure.

[0040] Specifically, the current twin data corresponding to the air compressor is obtained, and based on the current twin data and the twin model of the air compressor, the model is updated, the operating state evolves, and fault diagnosis is performed to determine the current operating state and current fault diagnosis results corresponding to the air compressor.

[0041] Based on the above technical solution, the current twin data includes: air compressor equipment data, sensor collection data and environmental data at the current moment; air compressor equipment data includes: geometric parameters, material data and assembly relationships; sensor collection data includes: current collection data, measurement point positioning and data collection interval; environmental data includes: temperature, humidity and flow rate.

[0042] Among them, twin data can refer to the root of health management driven by data twins. Twin data integrates multi-angle and multi-dimensional data to more comprehensively describe the health status of physical entities. Air compressor equipment data can refer to the hardware and hardware component data of the air compressor entity. Air compressor equipment data includes: geometric parameters, material data and assembly relationships. Geometric parameters may include but are not limited to the shape, position and size of the air compressor and its components. Material data may refer to the material data of each component of the air compressor. Assembly relationships may refer to the assembly relationships of parts in each component of the air compressor. For example, assembly relationships may include the assembly relationships between cylinders, compressors and coolers, and the assembly relationships of parts in each component. Air compressor equipment data can be used to map air compressor entities to obtain geometric models based on fault diagnosis and state evolution. Sensor acquisition data may refer to the data collected by each sensor configured on the air compressor entity. Current acquisition data may refer to the air compressor entity data collected by the sensor at the current moment. Measurement point positioning may refer to the acquisition position of each sensor on the air compressor entity. The data collection interval may refer to a preset data collection interval corresponding to sensors with different collection functions. The environmental data may refer to information about the environment in which the air compressor is physically located.

[0043] Specifically, in the embodiments of the present invention, real-time mapping of physical entities and virtual models and dynamic visualization of health status can be achieved. The virtual model is a data twin mapping model constructed based on the physical entity, and a closed-loop mapping mechanism is formed between the virtual model and the physical entity. The air compressor twin model is a data twin mapping model constructed based on the air compressor entity, and a closed-loop mapping mechanism is formed between the air compressor entity. In the health management stage of the entire life cycle, the air compressor entity is a prerequisite for the implementation of the twin mapping model, thereby generating a mapping mechanism from real to virtual. The air compressor entity is composed of multiple functional modules and sensors, each of which is composed of different units. The air compressor is constructed at the system level. The functional modules of the air compressor entity are mainly composed of cylinders, compressors, coolers, lubrication systems, etc.

[0044] On the basis of the above technical scheme, "determining the current operating status and current fault diagnosis result corresponding to the air compressor based on the current twin data and the twin model of the air compressor" may include: updating the geometric model in the twin model of the air compressor based on the air compressor equipment data to obtain an updated geometric model; performing fault diagnosis based on the sensor acquisition data and the fault diagnosis model to determine the current fault diagnosis result corresponding to the air compressor; and determining the current operating status corresponding to the air compressor based on the environmental data, sensor acquisition data and the evolution model on the basis of the updated geometric model.

[0045] Among them, the air compressor twin model includes: a geometric model, a fault diagnosis model and an evolutionary model. The geometric model can be used to describe the geometric parameters and relationships of the air compressor entity. The fault diagnosis model can be used to realize the analysis and fault diagnosis of the physical entity based on the various types of data collected. For example, in order to realize real-time analysis and prediction of faults, the embodiment of the present invention constructs a fault diagnosis model through a data-driven fault diagnosis method and a fault result verification method of model simulation. The fault diagnosis model can be a fault diagnosis model of a deep bidirectional long short-term memory network. The evolutionary model can be used to synchronize the real operating status of the marine air compressor in the virtual space.

[0046] Specifically, the air compressor equipment data at the current moment is compared with the air compressor equipment data at the last acquisition moment. If they are consistent, the geometric model corresponding to the last acquisition moment is used as the geometric model corresponding to the current moment. If they are inconsistent, the geometric model corresponding to the last acquisition moment is updated for the inconsistent equipment data to obtain an updated geometric model. The sensor acquisition data is input into the fault diagnosis model for fault diagnosis to determine the current fault diagnosis result corresponding to the air compressor. On the basis of the updated geometric model, the air compressor operation simulation is performed based on the environmental data, the sensor acquisition data and the evolution model to deduce the air compressor state, thereby obtaining the current operation state corresponding to the air compressor.

[0047] S120: Perform reverse modeling based on the current fault diagnosis result to determine the current air compressor fault model.

[0048] Among them, the current air compressor fault model may refer to the fault model under the fault mode corresponding to the current fault diagnosis result. For example, the current air compressor fault model may be, but is not limited to, a fault model corresponding to high exhaust temperature, that is, the model input data corresponding to no fault, and the fault diagnosis result obtained by inputting into the fault model is high exhaust temperature. The current twin data may also include knowledge data. Knowledge data may include: fault model library, fault knowledge rule library, and equipment maintenance rule library. The fault model library may store fault models corresponding to each fault mode. The fault knowledge rule library may store summarized fault rules. The equipment maintenance rule library may store rules for handling faults.

[0049] Specifically, based on the current fault diagnosis result, the current air compressor fault model corresponding to the current fault diagnosis result is determined from the fault model library. If the current fault diagnosis result is no fault, a pre-built standard air compressor model is selected from the fault model library as the current air compressor fault model.

[0050] S130. Determine a reference operating state corresponding to the air compressor based on the current twin data and the current air compressor fault model.

[0051] Among them, the reference operating state may refer to the accurate operating state of the air compressor corresponding to the air compressor in the current fault diagnosis result under the condition simulated by the current twin data.

[0052] Specifically, the environmental data in the current twin data is used to simulate the operating environment of the air compressor. On this basis, the sensor acquisition data in the current twin data and the current air compressor fault model are used to evolve the operating state of the air compressor and determine the corresponding reference operating state of the air compressor.

[0053] S140: If the current operating state and the reference operating state meet a preset consistency condition, the current fault diagnosis result is determined as a target fault diagnosis result corresponding to the air compressor.

[0054] The preset consistency condition may refer to a condition that the data error can be regarded as consistent within a preset error range. The target fault diagnosis result may refer to an accurate fault diagnosis result obtained through accuracy judgment.

[0055] Specifically, if the parameter difference between each current operating parameter value of the current operating state and each reference operating parameter value of the reference operating state is within a preset error range, that is, the preset consistency condition is met, the current fault diagnosis result is determined as the target fault diagnosis result corresponding to the air compressor.

[0056] On the basis of the above technical solution, "if the current operating state and the reference operating state meet the preset consistency conditions, then the current fault diagnosis result is determined as the target fault diagnosis result corresponding to the air compressor" may include: if the state difference between the current operating state and the reference operating state is less than the preset difference, then it is determined that the current operating state and the reference operating state meet the preset consistency conditions; the current fault diagnosis result is determined as the target fault diagnosis result corresponding to the air compressor.

[0057] The state difference may refer to a parameter difference between state parameter values. For example, the state difference may refer to a parameter difference between each current operating parameter value of the current operating state and each reference operating parameter value of the reference operating state. The preset difference may refer to an absolute value of a boundary of a preset error range.

[0058] Specifically, if the state difference between the current operating state and the reference operating state is less than the preset difference, it is determined that the current operating state and the reference operating state meet the preset consistency condition; and the current fault diagnosis result is determined as the target fault diagnosis result corresponding to the air compressor.

[0059] The technical solution of the embodiment of the present invention obtains the current twin data corresponding to the air compressor, and determines the current operating state and current fault diagnosis result corresponding to the air compressor based on the current twin data and the twin model of the air compressor; performs reverse modeling based on the current fault diagnosis result to determine the current air compressor fault model; determines the reference operating state corresponding to the air compressor based on the current twin data and the current air compressor fault model; if the current operating state and the reference operating state meet the preset consistency conditions, the current fault diagnosis result is determined as the target fault diagnosis result corresponding to the air compressor, thereby accurately and conveniently determining the target fault diagnosis result corresponding to the air compressor, thereby improving the efficiency and accuracy of marine air compressor fault diagnosis.

[0060] Based on the above technical solution, the method also includes: if the current operating state and the reference operating state do not meet the preset consistency conditions, the fault diagnosis model in the air compressor twin model is iteratively trained based on the fault data of the current fault diagnosis result in the database to obtain a reconstructed fault diagnosis model.

[0061] The database may be composed of historical twin data and corresponding labels of the air compressor entity (equivalent to the target fault diagnosis results corresponding to the historical twin data). The historical twin data and corresponding labels may be understood as fault data.

[0062] Specifically, if the current operating state and the reference operating state do not meet the preset consistency conditions, it indicates that the fault diagnosis model in the air compressor twin model is inaccurate and the fault diagnosis model needs to be reconstructed. The fault data corresponding to the current fault diagnosis result is obtained from the database, and the deep learning model is reconstructed based on the fault data and existing fault data to obtain the reconstructed fault diagnosis model, and the fault diagnosis model in the air compressor twin model is updated after reconstruction.

[0063] For example, if the current fault diagnosis result is detected as a fault that has not occurred before, the new fault is analyzed and studied based on the current twin data corresponding to the current fault diagnosis result, and the summarized fault rules are saved to the fault knowledge rule base. New fault data is collected and combined with existing fault data to reconstruct the deep learning model to obtain a reconstructed fault diagnosis model, and the fault diagnosis model in the air compressor twin model is updated after reconstruction.

[0064] It should be noted that when the actual state of the air compressor entity during operation is inconsistent with the state obtained by the air compressor twin model, the state of the digital twin model in the synchronization information space is synchronized to make its state consistent with the actual operation state of the air compressor. The real-time correction of the digital twin model can ensure that when a fault occurs, the state monitoring function and fault diagnosis function can timely visualize the fault, ensuring the effectiveness of the fault information.

[0065] Embodiment 2

[0066] Figure 2 This is a flow chart of a method for diagnosing a fault of a marine air compressor provided in the second embodiment of the present invention. Based on the above embodiment, this embodiment describes in detail the process of determining the reference operating state corresponding to the air compressor. The explanations of the terms that are the same or corresponding to the above embodiments are not repeated here. Figure 2 As shown, the method includes:

[0067] S210. Obtain the current twin data corresponding to the air compressor, and determine the current operating status and current fault diagnosis result corresponding to the air compressor based on the current twin data and the twin model of the air compressor.

[0068] S220: Perform reverse modeling based on the current fault diagnosis result to determine the current air compressor fault model.

[0069] S230. Establish an environmental model for the current air compressor fault model based on the environmental data in the current twin data.

[0070] Among them, the environmental model can be used to describe the impact of the external environment of the air compressor on the operation of the air compressor.

[0071] S240. Establish an operation mechanism model for the current air compressor fault model based on the air compressor equipment data in the current twin data.

[0072] Among them, the operation mechanism model can be used to describe the movement laws and interactions of various components of the air compressor.

[0073] S250. Determine the reference operating state corresponding to the air compressor based on the sensor collection data, environmental model, operating mechanism model and current air compressor fault model in the current twin data.

[0074] Specifically, the operation simulation of the air compressor can be realized based on the environmental model, the operation mechanism model and the current air compressor fault model, and the corresponding reference operating state of the air compressor can be obtained by simulating the sensor collection data in the current twin data.

[0075] S260: If the current operating state and the reference operating state meet a preset consistency condition, the current fault diagnosis result is determined as a target fault diagnosis result corresponding to the air compressor.

[0076] The technical solution of the embodiment of the present invention is to establish an environmental model for the current air compressor fault model based on the environmental data in the current twin data; to establish an operating mechanism model for the current air compressor fault model based on the air compressor equipment data in the current twin data; thereby using the environmental model to describe the impact of the external environment of the air compressor on the operation of the air compressor, and using the operating mechanism model to describe the movement laws and interactions of the various components of the air compressor, and then based on the environmental model, the operating mechanism model and the current air compressor fault model to achieve accurate simulation of the air compressor, and in conjunction with the sensor acquisition data in the current twin data, to accurately and conveniently determine the corresponding reference operating state of the air compressor. In the subsequent consistency detection, the accuracy of consistency detection and the accuracy of marine air compressor fault diagnosis are further improved.

[0077] Exemplarily, the specific steps of another optional embodiment of the embodiment of the present invention include: obtaining the current twin data corresponding to the air compressor, and determining the current operating state and current fault diagnosis result corresponding to the air compressor based on the current twin data and the twin model of the air compressor. Obtain the current air compressor fault diagnosis model corresponding to the current fault diagnosis result from the fault diagnosis model library. Based on the current twin data and the current air compressor fault diagnosis model, determine the reference fault diagnosis result corresponding to the air compressor. If the current fault diagnosis result and the reference fault diagnosis result meet the preset consistency condition, the current fault diagnosis result is determined as the target fault diagnosis result corresponding to the air compressor. If the current fault diagnosis result and the reference fault diagnosis result do not meet the preset consistency condition, the fault diagnosis model in the twin model of the air compressor is reconstructed based on the above method.

[0078] The following is an embodiment of a marine air compressor fault diagnosis device provided by an embodiment of the present invention. The device and the marine air compressor fault diagnosis method of the above-mentioned embodiments belong to the same inventive concept. For details not described in detail in the embodiment of the marine air compressor fault diagnosis device, reference can be made to the embodiment of the above-mentioned marine air compressor fault diagnosis method.

[0079] Embodiment 3

[0080] Figure 3 This is a schematic diagram of the structure of a marine air compressor fault diagnosis device provided by the third embodiment of the present invention. Figure 3 As shown, the device includes: a current information determination module 310, a current air compressor fault model determination module 320, a reference operating state determination module 330 and a target fault diagnosis result determination module 340.

[0081] Among them, the current information determination module 310 is used to obtain the current twin data corresponding to the air compressor, and determine the current operating state and current fault diagnosis result corresponding to the air compressor based on the current twin data and the air compressor twin model; the current air compressor fault model determination module 320 is used to perform reverse modeling based on the current fault diagnosis result to determine the current air compressor fault model; the reference operating state determination module 330 is used to determine the reference operating state corresponding to the air compressor based on the current twin data and the current air compressor fault model; the target fault diagnosis result determination module 340 is used to determine the current fault diagnosis result as the target fault diagnosis result corresponding to the air compressor if the current operating state and the reference operating state meet the preset consistency conditions.

[0082] The technical solution of the embodiment of the present invention obtains the current twin data corresponding to the air compressor, and determines the current operating state and current fault diagnosis result corresponding to the air compressor based on the current twin data and the twin model of the air compressor; performs reverse modeling based on the current fault diagnosis result to determine the current air compressor fault model; determines the reference operating state corresponding to the air compressor based on the current twin data and the current air compressor fault model; if the current operating state and the reference operating state meet the preset consistency conditions, the current fault diagnosis result is determined as the target fault diagnosis result corresponding to the air compressor, thereby accurately and conveniently determining the target fault diagnosis result corresponding to the air compressor, thereby improving the efficiency and accuracy of marine air compressor fault diagnosis.

[0083] Based on the above technical solution, the current twin data includes: air compressor equipment data, sensor collection data and environmental data at the current moment; air compressor equipment data includes: geometric parameters, material data and assembly relationships; sensor collection data includes: current collection data, measurement point positioning and data collection interval; environmental data includes: temperature, humidity and flow rate.

[0084] Based on the above technical solution, the twin model of air compressor includes: geometric model, fault diagnosis model and evolution model;

[0085] The current information determination module 310 is specifically used to: update the geometric model in the air compressor twin model based on the air compressor equipment data to obtain an updated geometric model; perform fault diagnosis based on the sensor acquisition data and the fault diagnosis model to determine the current fault diagnosis result corresponding to the air compressor; and determine the current operating status corresponding to the air compressor based on the environmental data, sensor acquisition data and the evolution model on the basis of the updated geometric model.

[0086] Based on the above technical solution, the reference operating state determination module 330 is specifically used to: establish an environmental model for the current air compressor fault model based on the environmental data in the current twin data; establish an operating mechanism model for the current air compressor fault model based on the air compressor equipment data in the current twin data; determine the reference operating state corresponding to the air compressor based on the sensor acquisition data, environmental model, operating mechanism model and current air compressor fault model in the current twin data.

[0087] Based on the above technical solution, the target fault diagnosis result determination module 340 is specifically used for: if the state difference between the current operating state and the reference operating state is less than the preset difference, then determining that the current operating state and the reference operating state meet the preset consistency condition; determining the current fault diagnosis result as the target fault diagnosis result corresponding to the air compressor.

[0088] On the basis of the above technical solution, the device also includes:

[0089] The model reconstruction module is used to iteratively train the fault diagnosis model in the air compressor twin model based on the fault data of the current fault diagnosis result in the database if the current operating state and the reference operating state do not meet the preset consistency conditions, so as to obtain the reconstructed fault diagnosis model.

[0090] The marine air compressor fault diagnosis device provided in the embodiment of the present invention can execute the marine air compressor fault diagnosis method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the marine air compressor fault diagnosis method.

[0091] It is worth noting that in the above-mentioned embodiment of marine air compressor fault diagnosis, the various units and modules included are only divided according to functional logic, but are not limited to the above-mentioned division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0092] Embodiment 4

[0093] Figure 4 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. 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 can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, 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 examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0094] As shown Figure 4 in FIG. 1, the electronic device 10 includes at least one processor 11 and a memory communicatively connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc. The memory stores a computer program executable by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0095] Multiple components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disc, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0096] The processor 11 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the marine air compressor fault diagnosis method.

[0097] In some embodiments, the marine air compressor fault diagnosis method can be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the marine air compressor fault diagnosis method described above can be executed. Alternatively, in other embodiments, the processor 11 can be configured to execute the marine air compressor fault diagnosis method by any other appropriate means (e.g., by means of firmware).

[0098] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0099] Computer programs for implementing the 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, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0100] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0101] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0102] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0103] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0104] An embodiment of the present invention further provides a computer program product, including a computer program, which, when executed by a processor, implements the marine air compressor fault diagnosis method provided in any embodiment of the present application.

[0105] In the process of implementation, the computer program product can be written in one or more programming languages ​​or a combination thereof to perform the computer program code of the present invention, and the programming language includes an object-oriented programming language, such as Java, Smalltalk, C++, and also includes a conventional procedural programming language, such as "C" language or similar programming language. The program code can be executed completely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on the remote computer, or completely on the remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, using an Internet service provider to connect through the Internet). The program product and the marine air compressor fault diagnosis method disclosed in each embodiment of the present application belong to the same inventive concept, so it is not repeated here.

[0106] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0107] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for diagnosing faults of a marine air compressor, characterized in that: include: Acquire current twin data corresponding to the air compressor, and determine the current operating state and current fault diagnosis result corresponding to the air compressor based on the current twin data and the air compressor twin model; Perform reverse modeling based on the current fault diagnosis result to determine the current air compressor fault model; Determine a reference operating state corresponding to the air compressor based on the current twin data and the current air compressor fault model; If the current operating state and the reference operating state satisfy a preset consistency condition, the current fault diagnosis result is determined as a target fault diagnosis result corresponding to the air compressor.

2. The method according to claim 1, characterized in that The current twin data includes: air compressor equipment data, sensor collection data and environmental data at the current moment; The air compressor equipment data includes: geometric parameters, material data and assembly relationships; The sensor acquisition data includes: current acquisition data, measurement point location and data acquisition interval; The environmental data includes: temperature, humidity and flow rate.

3. The method according to claim 2, characterized in that The air compressor twin model includes: a geometric model, a fault diagnosis model and an evolution model; Determining the current operating state and the current fault diagnosis result corresponding to the air compressor based on the current twin data and the air compressor twin model includes: The geometric model in the twin model of the air compressor is updated based on the air compressor equipment data to obtain an updated geometric model; Perform fault diagnosis based on sensor collected data and a fault diagnosis model to determine a current fault diagnosis result corresponding to the air compressor; On the basis of the updated geometric model, the current operating state corresponding to the air compressor is determined based on the environmental data, the sensor collected data and the evolution model.

4. The method according to claim 1, characterized in that: The determining, based on the current twin data and the current air compressor fault model, a reference operating state corresponding to the air compressor includes: Establishing an environmental model for the current air compressor fault model based on the environmental data in the current twin data; Establishing an operation mechanism model for the current air compressor fault model based on the air compressor equipment data in the current twin data; Based on the sensor collected data in the current twin data, the environmental model, the operating mechanism model and the current air compressor fault model, the reference operating state corresponding to the air compressor is determined.

5. The method according to claim 1, characterized in that If the current operating state and the reference operating state meet a preset consistency condition, determining the current fault diagnosis result as a target fault diagnosis result corresponding to the air compressor includes: If the state difference between the current running state and the reference running state is less than a preset difference, determining that the current running state and the reference running state meet a preset consistency condition; The current fault diagnosis result is determined as the target fault diagnosis result corresponding to the air compressor.

6. The method according to claim 1, characterized in that The method further comprises: If the current operating state and the reference operating state do not meet the preset consistency condition, the fault diagnosis model in the air compressor twin model is iteratively trained based on the fault data of the current fault diagnosis result in the database to obtain a reconstructed fault diagnosis model.

7. A marine air compressor fault diagnosis device, characterized in that: The device comprises: A current information determination module, used to obtain current twin data corresponding to the air compressor, and determine the current operating state and current fault diagnosis result corresponding to the air compressor based on the current twin data and the air compressor twin model; A current air compressor fault model determination module, used to perform reverse modeling based on the current fault diagnosis result to determine the current air compressor fault model; A reference operating state determination module, used to determine a reference operating state corresponding to the air compressor based on the current twin data and the current air compressor fault model; The target fault diagnosis result determination module is used to determine the current fault diagnosis result as the target fault diagnosis result corresponding to the air compressor if the current operating state and the reference operating state meet a preset consistency condition.

8. An electronic device, characterized in that: The electronic device comprises: one or more processors; A memory for storing 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 marine air compressor fault diagnosis method as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the marine air compressor fault diagnosis method as described in any one of claims 1-6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the computer program implements the marine air compressor fault diagnosis method as described in any one of claims 1 to 6.

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