Edge diagnosis system, method, equipment and medium

Abstract

The invention provides an edge diagnosis system, method and device and a medium, and is applied to the technical field of fault diagnosis, specifically, an edge terminal comprises an edge terminal, different types of sensors arranged on a monitoring device and used for collecting different device state signals, and a local instrument arranged on a device site; a modular acquisition card, a fault diagnosis module and an alarm module are arranged in the edge terminal; the modular acquisition card receives equipment state signals acquired by different types of sensors or on-site instruments; the fault diagnosis module performs edge side fault diagnosis on the monitoring equipment based on the equipment state signal to obtain a fault diagnosis result; when the alarm module determines that the fault diagnosis result meets an alarm condition according to a preset alarm threshold value, equipment early warning information is sent to a far end; and the remote end prompts the early warning information of the equipment, so that real-time, intelligent and lightweight state monitoring and fault diagnosis are realized.

Description

Technical Field

[0001] This application relates to the field of fault diagnosis technology, and in particular to an edge diagnosis system, method, device and medium. Background Technology

[0002] Power plants, including thermal power plants and wind power plants, contain a large number of devices requiring condition monitoring and fault diagnosis. These include auxiliary equipment such as turbine-side circulating water pumps, condensate pumps, closed-loop pumps, open-loop pumps, and vacuum pumps; boiler-side forced draft fans, induced draft fans, and primary air fans; and core equipment such as generator sets, boiler systems, and turbine systems. These devices are crucial for ensuring the safe and stable operation of generator sets, and their operating status directly affects power generation efficiency and operational safety. However, existing monitoring and maintenance systems for these devices still have significant technical limitations. For example, there is a lack of online monitoring methods; relying solely on manual inspections and offline monitoring makes it difficult to fully grasp the health status of the equipment and achieve real-time, intelligent, and lightweight condition monitoring and fault diagnosis. Summary of the Invention

[0003] In view of this, this application provides an edge diagnostic system, method, device, and medium to solve the problem that existing technologies are unable to achieve real-time, intelligent, and lightweight equipment status monitoring and fault diagnosis. Specifically, the technical solution provided by this application is as follows: On the one hand, this application provides an edge diagnosis method, including an edge end and a distal end; The edge end includes edge terminals, different types of sensors installed on the monitoring equipment to collect status signals of different equipment, and local instruments installed at the equipment site; The edge terminal has a built-in modular data acquisition card, fault diagnosis module, and alarm module; Modular data acquisition card, used to receive equipment status signals collected by different types of sensors or local instruments; The fault diagnosis module is used to perform edge-side fault diagnosis on the monitoring equipment based on the equipment status signals to obtain fault diagnosis results; The alarm module is used to send equipment warning information of the monitoring equipment to the remote end when the fault diagnosis result meets the alarm conditions according to the preset alarm threshold. Remotely, it is used to provide early warning information for monitoring equipment.

[0004] Optionally, the modular acquisition card includes acquisition card modules that correspond one-to-one with local instruments and different types of sensors; each acquisition card module is used to receive equipment status signals acquired by the corresponding type of sensor or local instrument; the modular acquisition card is used to combine acquisition card modules that are suitable for the monitoring equipment according to the equipment type of the monitoring equipment.

[0005] Optionally, the remote end includes user terminals deployed on the power plant's intranet; The user terminal is used to receive equipment warning information from the monitoring equipment sent by the alarm module and to provide notifications regarding the equipment warning information.

[0006] Optionally, the edge terminal also has a built-in data storage module; the data storage module is used to store the device status signal received by the modular acquisition card, the fault diagnosis result diagnosed by the fault diagnosis module, and the device warning information sent by the alarm module for a short time when the device status of the monitoring device is normal; and to store the device status signal received by the modular acquisition card, the fault diagnosis result diagnosed by the fault diagnosis module, and the device warning information sent by the alarm module for a long time when the device status of the monitoring device is abnormal.

[0007] Optionally, the remote end includes a storage and publishing server deployed on the power plant's intranet, which is communicatively connected to the data storage module. The storage and publishing server is used to store data file packages of monitoring equipment transmitted by the data storage module. The data file packages include equipment status signals received by the modular acquisition card, fault diagnosis results diagnosed by the fault diagnosis module, and equipment early warning information sent by the alarm module.

[0008] Optionally, the remote end also includes a deep diagnostic analysis terminal deployed in the power plant's intranet; the deep diagnostic analysis terminal is used to receive data file packages of monitoring equipment transmitted by the storage and publishing server, and to perform multi-dimensional deep analysis on the data file packages of monitoring equipment sent by the storage and publishing server to obtain and display the multi-dimensional deep analysis results.

[0009] Optionally, the remote end also includes a remote diagnostic center, which is connected to the storage and publishing server via an intercity intranet. The remote diagnostic center is used to receive data file packages of the monitoring equipment transmitted by the storage and publishing server, and to perform in-depth analysis and artificial intelligence diagnostic model training on the data file packages of the monitoring equipment in order to iteratively optimize the fault mode library and fault diagnosis algorithm.

[0010] On the other hand, this application provides an edge diagnostic device applied to an edge terminal in the aforementioned edge diagnostic system, comprising: The modular acquisition card receives equipment status signals collected by different types of sensors on the monitoring equipment, as well as equipment status signals collected by local instruments at the equipment site. The fault diagnosis module performs edge-side fault diagnosis on the monitoring equipment based on the equipment status signal to obtain the fault diagnosis result; When the alarm module determines that the fault diagnosis result meets the alarm conditions based on the preset alarm threshold, it sends the equipment warning information of the monitoring equipment to the remote end so that the remote end can be alerted to the equipment warning information of the monitoring equipment.

[0011] On the other hand, this application provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described edge diagnosis method.

[0012] On the other hand, this application also provides a computer-readable storage medium storing computer instructions that, when executed by a processor, implement the aforementioned edge diagnosis method.

[0013] The beneficial effects of this application are as follows: This application enables online intelligent monitoring and diagnostic functions by deploying edge terminals next to the monitoring equipment, thereby achieving low latency and real-time response and avoiding the bottleneck of remote centralized processing. In addition, edge terminals occupy little space and have low cost, eliminating the need for complex cables and network bandwidth, thus realizing real-time, intelligent and lightweight status monitoring and fault diagnosis.

[0014] Other features and advantages of this application will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the application. The objectives and other advantages of this application may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings. Attached Figure Description

[0015] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 This is a schematic diagram of the composition structure of the edge diagnostic system in the embodiments of this application; Figure 2 This is a schematic diagram outlining the edge diagnosis method in the embodiments of this application; Figure 3 This is the hardware structure of the electronic device in the embodiments of this application. Detailed Implementation

[0016] To make the objectives, technical solutions, and beneficial effects of this application clearer, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0017] This application provides an edge diagnostic system, see the following embodiments. Figure 1As shown, the edge diagnostic system provided in this application embodiment includes an edge end and a remote end; The edge end includes edge terminals, different types of sensors installed on the monitoring equipment to collect status signals of different equipment, and local instruments installed at the equipment site; The edge terminal has a built-in modular data acquisition card, fault diagnosis module, and alarm module; Modular data acquisition card, used to receive equipment status signals collected by different types of sensors or local instruments; The fault diagnosis module is used to perform edge-side fault diagnosis on the monitoring equipment based on the equipment status signals to obtain fault diagnosis results; The alarm module is used to send equipment warning information of the monitoring equipment to the remote end when the fault diagnosis result meets the alarm conditions according to the preset alarm threshold. Remotely, it is used to provide early warning information for monitoring equipment.

[0018] In one possible implementation, the following three types of sensors are configured according to the equipment status monitoring requirements, all of which are connected to the edge terminal via wired connections: Vibration sensors: 2 units, respectively installed on the front and rear bearing end covers of the motor, to collect vibration acceleration signals (range: 0-50g, sampling frequency: 1024Hz). Temperature sensors: 3 units, respectively installed on the front and rear bearings of the motor and the pump housing, to collect temperature signals (range: -20℃-150℃). Liquid level sensor: 1 unit, installed in the lubricating oil tank, to collect lubricating oil level signals (range: 0-500mm).

[0019] In one possible implementation, the modular acquisition card includes acquisition card modules that correspond one-to-one with local instruments and different types of sensors; each acquisition card module is used to receive equipment status signals acquired by the corresponding type of sensor or local instrument; the modular acquisition card is used to assemble acquisition card modules that are compatible with the monitoring equipment according to the equipment type of the monitoring equipment.

[0020] In one possible implementation, four types of data acquisition card modules are combined according to sensor / instrument type, with each type containing 1-2 data acquisition cards: Vibration acquisition cards: 2, which receive acceleration signals from vibration sensors and support signal amplification, filtering and analog-to-digital conversion; Temperature acquisition card: 1 unit, which receives temperature signals from temperature sensors and supports multi-point parallel acquisition; Process quantity acquisition card: 1 unit, which receives 4-20mA signals from pressure transmitters and electromagnetic flow meters, as well as analog signals from liquid level sensors; Electrical quantity acquisition card: 1 unit, which receives the 0-10V current signal from the ammeter and converts it into a digital quantity.

[0021] In one possible implementation, the modular data acquisition card integrates a piezoelectric-electromagnetic composite energy harvester on the back, which uses the energy from tower swaying and unit vibration to provide power, achieving zero-cable deployment.

[0022] In one possible implementation, the local instruments reuse the existing local instruments at the power plant and are connected to the edge terminal via a wired connection through a signal branch module: Pressure transmitter: installed on the outlet pipeline of the water pump to collect the outlet pressure signal (range: 0-1.6MPa, output signal: 4-20mA). Electromagnetic flow meter: installed on the inlet pipe to collect cooling water flow rate signals (range: 0-50m³ / h, output signal: 4-20mA). Ammeter: Installed in the motor control cabinet, it collects the operating current signal (range: 0-50A, output signal: 0-10V).

[0023] In one possible implementation, the fault diagnosis module is used to perform parsing and feature extraction on the device status signals received by the modular acquisition card to obtain high-order status feature data, and based on the high-order status feature data, to perform edge-side fault diagnosis on the monitoring device using an artificial intelligence diagnostic model and a fault mode library and fault diagnosis algorithm customized for the monitoring device to obtain fault diagnosis results.

[0024] In one possible implementation, the fault mode library stores the characteristic parameter threshold ranges and fault feature association rules corresponding to each typical fault; among them, typical fault types include bearing wear, impeller scaling, misalignment, insufficient lubricating oil, etc.; characteristic parameter threshold ranges include, for example, the characteristic parameter threshold ranges corresponding to bearing wear are: vibration 1st harmonic amplitude ≥ 5.0 mm / s, bearing temperature ≥ 85℃, and lubricating oil film thickness ≤ 0.03 mm.

[0025] In one possible implementation, the fault diagnosis algorithm integrates a feature value comparison algorithm and a multi-parameter correlation verification algorithm. For example, the bearing wear diagnosis logic is: vibration 1st harmonic amplitude ≥ 5.0 mm / s (threshold met) + bearing temperature continues to rise (change rate ≥ 2℃ / h) + current is stable (fluctuation ≤ 5%). If the correlation rules are met, the fault is determined.

[0026] In one possible implementation, the fault diagnosis module includes a computation processing unit and a fault diagnosis unit. The processing unit is used to extract feature values, perform FFT transformation, feature frequency identification, rate of change calculation, correlation coefficient and state analysis on the device status signal received by the modular acquisition card, and obtain signal feature values, spectrum matrix, feature frequency amplitude table, rate of change vector, correlation coefficient matrix and state label and confidence as high-order state feature data. The fault diagnosis unit compares and matches high-order state feature data with the characteristic parameter threshold ranges and fault feature association rules corresponding to each typical fault in the fault mode library to obtain matching results. The matching results are then logically verified using a fault diagnosis algorithm to obtain a judgment result: if only the high-order state feature data meets the characteristic parameter threshold requirements of a typical fault, but the changing trend of this high-order state feature data and its associated other high-order state feature data does not meet the characteristic association rules of that typical fault, or the high-order state feature data does not match the characteristic parameter threshold requirements of any typical fault, then the monitoring equipment is determined to be in normal condition or has an atypical abnormality. If the high-order state feature data meets the characteristic parameter threshold requirements of a typical fault, and the changing trend of this high-order state feature data and its associated other high-order state feature data conforms to the characteristic association rules of that typical fault, then the monitoring equipment is determined to have that typical fault. A fault diagnosis result is formed based on the judgment result.

[0027] In one possible implementation, the edge terminal also has a built-in display unit for presenting the fault diagnosis results of the monitoring equipment.

[0028] In one possible implementation, the display unit shows the device status in a three-dimensional interface (yellow indicators for alarms and red indicators for danger), and simultaneously displays fault codes and text descriptions.

[0029] In one possible implementation, the edge terminal also has a built-in data storage module; the data storage module is used to store for a short time the device status signal received by the modular acquisition card, the fault diagnosis result diagnosed by the fault diagnosis module, and the device warning information sent by the alarm module when the device status of the monitoring device is normal; and to store for a long time the device status signal received by the modular acquisition card, the fault diagnosis result diagnosed by the fault diagnosis module, and the device warning information sent by the alarm module when the device status of the monitoring device is abnormal.

[0030] In one possible implementation, the data storage module is configured with a hierarchical storage strategy: when the equipment is normal: store high-order characteristic data (vibration characteristic values, average temperature, etc.) within a first time range (e.g., 7 days), and retain raw data for only a set duration (e.g., 24 hours); when the equipment is abnormal: store all raw data (vibration waveforms, continuous temperature curves), fault diagnosis results, and alarm records within a second time range (e.g., 1 year) for a long period.

[0031] In one possible implementation, the remote end includes a user terminal deployed on the power plant's intranet; The user terminal is used to receive equipment warning information from the monitoring equipment sent by the alarm module and to provide notifications regarding the equipment warning information.

[0032] In one possible implementation, the remote end also includes a storage and publishing server deployed on the power plant's intranet, which is communicatively connected to the data storage module. The storage and publishing server is used to store data file packages of monitoring equipment transmitted by the data storage module. The data file packages include equipment status signals received by the modular acquisition card, fault diagnosis results diagnosed by the fault diagnosis module, and equipment warning information sent by the alarm module.

[0033] In one possible implementation, the remote end also includes a deep diagnostic analysis terminal deployed in the power plant's intranet; the deep diagnostic analysis terminal is used to receive data file packages of monitoring equipment transmitted by the storage and publishing server, and to perform multi-dimensional deep analysis on the data file packages of monitoring equipment sent by the storage and publishing server to obtain and display the multi-dimensional deep analysis results.

[0034] In one possible implementation, the remote end also includes a remote diagnostic center, which is connected to the storage and publishing server via an intercity intranet. The remote diagnostic center is used to receive data file packages of the monitoring equipment transmitted by the storage and publishing server, and to perform in-depth analysis and artificial intelligence diagnostic model training on the data file packages of the monitoring equipment to iteratively optimize the fault mode library and fault diagnosis algorithm.

[0035] In one possible implementation, the edge terminal also has a built-in life prediction module; the life prediction module is used to output the remaining life hours of the bearing / impeller by using a lightweight model for remaining life prediction (RNN-LSTM, parameter count <1MB). The remote diagnostic center is also used to train a high-fidelity digital twin using data from all units of the same type in the plant, and periodically distributes the twin parameters to the edge to achieve self-improvement in life prediction accuracy.

[0036] In one possible implementation, the edge terminal also has a built-in lubricant management module for recording lubricant addition information of the monitoring device; wherein, the lubricant addition information includes lubrication location, number of lubrication points, type of lubricant, filling method, oil change / replenishment amount, and filling time.

[0037] In one possible implementation, the alarm module supports custom alarm thresholds and operating condition association logic, and configures on-site audible and visual alarms.

[0038] In this embodiment, edge terminal monitoring and diagnosis is a crucial approach to achieving lean, intelligent, and lightweight equipment status management. Deploying numerous, low-cost edge terminals customized based on equipment characteristics at the forefront enables more direct, real-time, and efficient monitoring of equipment status, facilitating condition-based maintenance and smart power plant construction. Specifically, this includes the following aspects: (1) Realize the edge deployment of the equipment monitoring and diagnostic system, reduce data latency and network bandwidth occupation, deploy important auxiliary machines as needed, and eliminate the need for centralized deployment and large-capacity servers (compared to the batch construction of auxiliary machine online monitoring systems). (2) The equipment status parameters are displayed in all directions on the spot, and alarm messages are sent to the mobile APP in real time. The equipment dynamics are grasped at the first time, and the equipment status ledger is automatically generated on a regular basis, reducing a lot of manual inspection workload. (3) Customize the fault mode library, status judgment logic rules and alarm logic standards according to the characteristics of the equipment, and truly tailor it for each piece of equipment to realize edge-side fault diagnosis: reduce the workload of manual analysis and provide reference for equipment maintenance strategies.

[0039] (4) Automatically generate equipment status analysis reports to assist in equipment ledger management and facilitate the tracing of abnormal conditions. The long-term plan is to incorporate large and small artificial intelligence models to enhance intelligence and autonomy.

[0040] Based on the above embodiments, this application provides an edge diagnostic method applied to the edge terminal of the aforementioned edge diagnostic system. (See attached document.) Figure 2 As shown, the general flow of the edge diagnosis method provided in this application embodiment is as follows: Step 201: Receive equipment status signals collected by different types of sensors on the monitoring equipment and equipment status signals collected by local instruments at the equipment site through the modular acquisition card.

[0041] In this embodiment, the modular acquisition card synchronously receives the following signals at a preset sampling frequency (vibration 1024Hz, temperature 1Hz, process quantity 1Hz, electrical quantity 1Hz): Vibration sensors: vibration acceleration signal of the front bearing of the motor (real-time value: 3.2g), vibration acceleration signal of the rear bearing (real-time value: 2.8g); Temperature sensors: front bearing temperature (72℃), rear bearing temperature (75℃), pump body housing temperature (58℃); Local instruments: outlet pressure (1.2MPa), cooling water flow rate (35m³ / h), operating current (32A); Liquid level sensor: Lubricating oil level (320mm).

[0042] Each acquisition card module amplifies, filters (low-pass filter, cutoff frequency 500Hz) and performs analog-to-digital conversion on the received analog signal, converting it into a 16-bit digital signal before transmitting it to the fault diagnosis module.

[0043] Step 202: Based on the equipment status signal, the fault diagnosis module performs edge-side fault diagnosis on the monitoring equipment to obtain the fault diagnosis result.

[0044] In practical implementation, the fault diagnosis module performs analysis and feature extraction on the equipment status signals to obtain high-order status feature data. Based on the high-order status feature data, the artificial intelligence diagnostic model uses a fault mode library and fault diagnosis algorithm customized for the monitoring equipment to perform edge-side fault diagnosis on the monitoring equipment to obtain fault diagnosis results.

[0045] In this embodiment of the application, the fault diagnosis module performs multi-dimensional processing on the digital signal to generate high-order state feature data, including: Feature extraction: For example, vibration signal extraction includes passband amplitude (front bearing: 4.2 mm / s, rear bearing: 3.8 mm / s) and 1st harmonic amplitude (front bearing: 3.5 mm / s, rear bearing: 3.0 mm / s); temperature signal extraction includes mean (bearing average temperature 73.5℃) and rate of change (increase of 1.5℃ / h in the past hour). FFT Transform: For example, performing an FFT transform on a vibration acceleration signal generates a spectral matrix (frequency range 0-500Hz, resolution 0.5Hz). Correlation coefficient calculation: For example, calculate the correlation coefficient between vibration 1st harmonic amplitude and bearing temperature (0.85), and the correlation coefficient between cooling water flow rate and outlet pressure (0.72). Status label: For example, based on the unit load, the operating condition is determined to be stable operation with a confidence level of 99.2%.

[0046] In this embodiment of the application, the fault diagnosis module calls a customized fault mode library to compare and match high-order state feature data with typical fault features, including: Preliminary matching: For example, the front bearing vibration amplitude at 1st octave is 3.5 mm / s (which does not reach the bearing wear threshold of 5.0 mm / s), and the vibration parameters, temperature parameters, and process parameters of the rear bearing do not meet the threshold requirements of any typical fault. Logical verification: For example, none of the high-order state feature data met the threshold requirements for any typical fault and the conditions for matching the association rule; Result generation: For example, if the equipment is determined to be in normal condition, a fault diagnosis result is generated (fault code: 000, text description: the equipment is operating normally and all parameters meet the rated operating conditions).

[0047] Step 203: When the alarm module determines that the fault diagnosis result meets the alarm conditions according to the preset alarm threshold, it sends the equipment warning information of the monitoring equipment to the remote end so that the remote end can be prompted by the equipment warning information of the monitoring equipment.

[0048] In this embodiment, the alarm module calls a preset alarm threshold (such as a bearing temperature alarm threshold of 85°C and a vibration 1st octave amplitude alarm threshold of 5.0 mm / s) and the motor unit operating condition (stable operation) to determine whether the fault diagnosis result (normal) meets the alarm conditions. When the alarm conditions are met, the module sends the equipment warning information of the monitoring equipment to the remote end so that the remote end can be prompted by the equipment warning information of the monitoring equipment. When the alarm conditions are not met, the module does not send the warning information to the remote end, but only synchronizes the diagnosis result to the data storage module for archiving.

[0049] It should be noted that the principle of the edge diagnostic method provided in this application embodiment to solve the technical problem is similar to that of the edge diagnostic system provided in this application embodiment. Therefore, the implementation of the edge diagnostic method provided in this application embodiment can refer to the implementation of the edge diagnostic system provided in this application embodiment, and repeated details will not be repeated.

[0050] After introducing the edge diagnostic system and method provided in the embodiments of this application, the electronic device provided in the embodiments of this application will be briefly introduced next.

[0051] The electronic device provided in this application embodiment may be, but is not limited to, edge diagnostics, etc., see [link / reference]. Figure 3 As shown, the electronic device 300 provided in this application embodiment includes at least a processor 301, a memory 302, and a computer program stored in the memory 302 and executable on the processor 301. When the processor 301 executes the computer program, it implements the edge diagnosis method provided in this application embodiment.

[0052] In one possible implementation, processor 301 can be a single processing element or a collective term for multiple processing elements. For example, processor 301 can be a central processing unit (CPU) or one or more integrated circuits configured to implement the edge diagnostic method described in the embodiments of this application. Specifically, processor 301 can be a general-purpose processor, including but not limited to CPUs, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0053] In one possible implementation, memory 302 may include a readable medium in the form of volatile memory, such as random access memory (RAM) 3021 and / or cache memory 3022, and may further include read-only memory (ROM) 3023; memory 302 may also include a program tool 3025 having a set (at least one) of program modules 3024, including but not limited to: operating subsystem, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0054] In one possible implementation, the electronic device 300 provided in this application embodiment may further include a bus 303 connecting different components (including processor 301 and memory 302). The bus 303 represents one or more types of bus structures, including memory bus, peripheral bus, local area bus, etc.

[0055] In one possible implementation, the electronic device 300 can also communicate with one or more devices that enable a user to interact with the electronic device 300 (e.g., mobile phones, computers, etc.), and / or with external devices 304 such as devices that enable the electronic device 300 to communicate with one or more other electronic devices 300 (e.g., routers, modems, etc.). This communication can be performed via an input / output (I / O) interface 305. Furthermore, the electronic device 300 can also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter 306. Figure 3 As shown, network adapter 306 communicates with other modules of electronic device 300 via bus 303. It should be understood that, although... Figure 3 As not shown, other hardware and / or software modules may be used in conjunction with electronic device 300, including but not limited to microcode, device drivers, redundant processors, external disk drive arrays, Redundant Arrays of Independent Disks (RAID) subsystems, tape drives, and data backup storage subsystems.

[0056] It should be noted that, Figure 3 The electronic device 300 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0057] Furthermore, embodiments of this application also provide a computer-readable storage medium storing computer instructions that, when executed by a processor, implement the edge diagnosis method described above in embodiments of this application. Specifically, the computer instructions may be built into or installed in a processor, enabling the processor to implement the edge diagnosis method described above in embodiments of this application by executing the built-in or installed computer instructions.

[0058] Of course, the edge diagnosis method provided in the embodiments of this application can also be implemented as a program product, which includes program code. When the program code is executed by a processor, it implements the edge diagnosis method provided in the embodiments of this application.

[0059] The program product provided in this application embodiment can be any combination of one or more readable media, wherein the readable media can be a readable signal medium or a readable storage medium, and the readable storage medium can be, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or apparatus, or any combination thereof. Specifically, more specific examples of readable storage media (a non-exhaustive list) include: electrical connections with one or more wires, portable disks, hard disks, RAM, ROM, erasable programmable read-only memory (EPROM), optical fibers, portable compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0060] The program product provided in this application embodiment can be a CD-ROM and include program code, and can also run on an electronic device. However, the program product provided in this application embodiment is not limited thereto. In this application embodiment, the readable storage medium can be any tangible medium that contains or stores a program, which can be used by or in conjunction with an instruction execution system, device, or apparatus.

[0061] It should be noted that although several units or sub-units of the device have been mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to embodiments of this application, the features and functions of two or more units described above can be embodied in one unit. Conversely, the features and functions of one unit described above can be further divided and embodied by multiple units.

[0062] Furthermore, although the operations of the method of this application are described in a specific order in the accompanying drawings, this does not require or imply that these operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

[0063] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0064] Obviously, those skilled in the art can make various modifications and variations to the embodiments of this application without departing from the spirit and scope of the embodiments of this application. Therefore, if these modifications and variations to the embodiments of this application fall within the scope of the claims of this application and their equivalents, this application also intends to include these modifications and variations.

Claims

1. An edge diagnostic system, characterized by, The edge end and the remote end are included; The edge end includes an edge terminal, different types of sensors arranged on the monitoring device for collecting different device state signals, and on-site instruments arranged on the device site; The edge terminal is internally provided with a modular acquisition card, a fault diagnosis module and an alarm module; The modular acquisition card is used for receiving device state signals collected by different types of sensors or on-site instruments; The fault diagnosis module is used for performing edge-side fault diagnosis on the monitoring device based on the device state signals to obtain a fault diagnosis result; The alarm module is used for determining, according to a preset alarm threshold, that the fault diagnosis result meets an alarm condition, and sending device early warning information of the monitoring device to the remote end; The remote end is used for prompting the device early warning information of the monitoring device.

2. The edge diagnostic system of claim 1, wherein, The modular acquisition card includes an acquisition card module corresponding to each of the on-site instruments and the different types of sensors; each acquisition card module is used for receiving device state signals collected by a corresponding type of sensor or on-site instrument; and the modular acquisition card is used for combining and adapting acquisition card modules of the monitoring device according to a device type of the monitoring device.

3. The edge diagnostic system of claim 1, wherein, The remote end includes a user terminal deployed in an internal network of a power plant; The user terminal is used for receiving and prompting the device early warning information of the monitoring device sent by the alarm module.

4. An edge diagnostic system as claimed in any one of claims 1-3, characterized in that The edge terminal is further internally provided with a data storage module; the data storage module is used for storing, when a device state of the monitoring device is normal, device state signals received by the modular acquisition card, fault diagnosis results diagnosed by the fault diagnosis module and device early warning information sent by the alarm module for a short time; and the data storage module is used for storing, when the device state of the monitoring device is abnormal, device state signals received by the modular acquisition card, fault diagnosis results diagnosed by the fault diagnosis module and device early warning information sent by the alarm module for a long time.

5. The edge diagnostic system of claim 4, wherein, The remote end includes a storage and release server deployed in the internal network of the power plant, the storage and release server is in communication connection with the data storage module; and the storage and release server is used for storing a data file package of the monitoring device transmitted by the data storage module; wherein the data file package includes device state signals received by the modular acquisition card, fault diagnosis results diagnosed by the fault diagnosis module and device early warning information sent by the alarm module.

6. The edge diagnostic system of claim 5, wherein, The remote end further includes a deep diagnosis and analysis terminal deployed in the internal network of the power plant; the deep diagnosis and analysis terminal is used for receiving the data file package of the monitoring device transmitted by the storage and release server, and performing multi-dimensional deep analysis on the data file package of the monitoring device transmitted by the storage and release server to obtain and display multi-dimensional deep analysis results.

7. The edge diagnostic system of claim 5, wherein, The remote end further comprises a remote diagnosis center, which is connected with the storage and release server through a cross-city network communication connection; the remote diagnosis center is used for receiving the data file package of the monitoring device transmitted by the storage and release server, and performing deep analysis and artificial intelligence diagnosis model training on the data file package of the monitoring device, so as to iteratively optimize the fault mode library and the fault diagnosis algorithm.

8. An edge diagnostic method, characterized by, The edge terminal applied to the edge diagnosis system of any one of claims 1-7 comprises: receiving device state signals collected by different types of sensors on the monitoring device and device field on-site instrument collected device state signals through a modular acquisition card; performing edge side fault diagnosis on the monitoring device based on the device state signals through a fault diagnosis module to obtain a fault diagnosis result; when the fault diagnosis result meets the alarm condition according to a preset alarm threshold, sending device early warning information of the monitoring device to the remote end through an alarm module, so that the remote end prompts the device early warning information of the monitoring device.

9. An electronic device, comprising: The computer readable storage medium stores computer instructions, and the computer instructions are executed by the processor to realize the edge diagnosis method of claim 8.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer instructions, and the computer instructions are executed by the processor to realize the edge diagnosis method of claim 8.

Images