Steam centripetal turbine set fault monitoring method and device, medium and program product

Abstract

The invention provides a steam centripetal turbine set fault monitoring method and device, a medium and a program product, and relates to the technical field of centripetal turbine sets, and the method comprises the steps that state parameters, including vibration signal parameters, thermodynamic performance parameters and / or process parameters, of a steam centripetal turbine set are obtained through real-time monitoring; fault features of the steam centripetal turbine unit are diagnosed according to the state parameters through a preset diagnosis algorithm; generating a corresponding structured fault code according to the fault feature; the structure of the structured fault code comprises a fault system code, a fault level code, a fault part code, a fault mode code and / or a fault additional feature code, inputting the structured fault code into a predetermined fault knowledge base, generating alarm information and a maintenance work order, and sending the alarm information and the maintenance work order to a management platform. The method solves the problem that in the prior art, it is difficult to generate a unified structured fault code according to fault features of multiple state parameters, and consequently the fault recognition efficiency is low.

Description

Technical Field

[0001] This invention relates to the field of centrifugal turbine technology, and in particular to a method, device, medium, and program product for fault monitoring of steam centrifugal turbine units. Background Technology

[0002] As a core power equipment in the energy and power fields, the operational stability of steam centrifugal turbine units directly determines the efficiency and safety of the entire production system. Once a failure occurs, it may not only cause equipment shutdown and production interruption, but also lead to safety accidents and significant economic losses. Therefore, the need for fault monitoring of the units is becoming increasingly urgent.

[0003] However, different monitored state parameters correspond to different fault characteristics. For example, abnormal vibration signals often indicate mechanical faults such as rotor imbalance and bearing wear, while abnormal thermodynamic parameters are associated with performance faults such as steam system leakage and blade efficiency decline. The dimensions and representation forms of various fault characteristics differ greatly. Existing diagnostic solutions lack unified fault feature integration and coding rules, and cannot transform the scattered fault features corresponding to multiple parameters into standardized and structured fault codes. This results in subsequent fault identification having to match the features of different parameters one by one, which not only makes it difficult to quickly locate the core of the fault, but also easily leads to the omission of fault features and confusion in identification logic. Ultimately, this results in low fault identification efficiency and fails to meet the actual needs of the unit for rapid fault response and timely maintenance. Summary of the Invention

[0004] The embodiments of the present invention provide a method, device, medium and program product for fault monitoring of steam centripetal turbine units, which aims to solve the problem that the existing technology is difficult to generate a unified structured fault code based on the fault characteristics of multiple state parameters, resulting in low fault identification efficiency.

[0005] To achieve the above objectives, in a first aspect, the present invention provides a method for fault monitoring of a steam centrifugal turbine unit, comprising the following steps: The state parameters of the steam centripetal turbine unit are obtained through real-time monitoring, including vibration signal parameters, thermodynamic performance parameters and / or process parameters; The fault characteristics of the steam centripetal turbine unit are diagnosed based on the state parameters using a predetermined diagnostic algorithm. A corresponding structured fault code is generated based on the fault characteristics. The structure of the structured fault code includes a fault system code, a fault level code, a fault location code, a fault mode code, and / or a fault additional feature code. The structured fault codes are input into a predetermined fault knowledge base to generate alarm information and maintenance work orders, which are then sent to the management platform.

[0006] Furthermore, the vibration signal parameters include shaft vibration signal and shell vibration signal; the thermodynamic performance parameters include inlet steam temperature, outlet steam temperature, pressure and / or flow rate; and the process parameters include load, speed and / or valve opening.

[0007] Furthermore, the predetermined diagnostic algorithm includes a threshold judgment algorithm, a model analysis algorithm, and / or a trend analysis algorithm, wherein, The threshold judgment algorithm obtains fault state parameters that exceed the threshold by setting an alarm limit for a single state parameter. The model analysis algorithm establishes a digital twin model of the unit, compares the measured values ​​with the predicted values, and alarms are triggered if the deviation is too large. The trend analysis algorithm employs machine learning such as LSTM time series prediction and deep learning such as CNN to identify vibration spectrum diagrams, and uses historical data to train the model to achieve early classification and warning of faults.

[0008] Furthermore, the fault system code is used to identify the main system and / or cross system to which the fault belongs; the fault level code is used to indicate the urgency of the fault; the fault location code is used to locate the faulty component; the fault mode code is used to describe the fault phenomenon and / or type; and the fault additional feature code is used to indicate additional fault feature information.

[0009] Furthermore, the predetermined fault knowledge base is configured as a structured fault coding library transformed from industry standards, expert experience, and / or historical fault cases.

[0010] Secondly, the present invention provides a steam centrifugal turbine unit fault monitoring device, including a memory and a processor, wherein the memory stores at least one program, and the at least one program is executed by the processor to implement the steam centrifugal turbine unit fault monitoring method as described above.

[0011] Thirdly, the present invention provides a computer-readable storage medium storing at least one program, wherein the at least one program is executed by a processor to implement the steam centrifugal turbine unit fault monitoring method as described above.

[0012] Fourthly, the present invention provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the steam centrifugal turbine unit fault monitoring method as described above.

[0013] The above technical solution has the following technical effects: This invention addresses the problem of low fault identification efficiency caused by the inability of existing technologies to generate unified structured fault codes based on fault characteristics of multiple state parameters. The invention utilizes real-time monitoring to acquire state parameters of the steam centrifugal turbine unit, including vibration signal parameters, thermodynamic performance parameters, and / or process parameters. A predetermined diagnostic algorithm is used to diagnose fault characteristics of the steam centrifugal turbine unit based on these state parameters. The system then inputs these structured fault codes into a predetermined fault knowledge base to generate alarm information and maintenance work orders, which are then sent to a management platform. Attached Figure Description

[0014] Figure 1 This is a schematic flowchart of a steam centrifugal turbine unit fault monitoring method according to an embodiment of the present invention.

[0015] Figure 2 This is a schematic diagram of the main system and its cross-system of a steam centrifugal turbine unit according to an embodiment of the present invention.

[0016] Figure 3 This is a schematic diagram of the structure of a steam centrifugal turbine unit fault monitoring device according to an embodiment of the present invention. Detailed Implementation

[0017] To further illustrate the various embodiments, the present invention provides accompanying drawings. These drawings are part of the disclosure of the present invention, primarily used to illustrate the embodiments and to explain the operating principles of the embodiments in conjunction with the relevant descriptions in the specification. With reference to these drawings, those skilled in the art should be able to understand other possible implementations and the advantages of the present invention. Components in the drawings are not drawn to scale, and similar component symbols are generally used to represent similar components.

[0018] The present invention will now be further described in conjunction with the accompanying drawings and specific embodiments.

[0019] Example 1: Figure 1 This is a flowchart illustrating a method for monitoring faults in a steam centrifugal turbine unit according to an embodiment of the present invention, as shown below. Figure 1 As shown, the method of this embodiment includes the following steps: The state parameters of the steam centripetal turbine unit are obtained by real-time monitoring. In one specific implementation, the state parameters include vibration signal parameters, thermodynamic performance parameters and / or process parameters. In one specific implementation, vibration signal parameters include shaft vibration and shell vibration signals. An accelerometer is used, and methods such as frequency spectrum analysis (FFT), envelope demodulation, and order analysis are employed to extract features such as bearing fault frequency, blade passage frequency, and power frequency harmonics. Thermodynamic performance parameters include inlet steam temperature, outlet steam temperature, pressure, and / or flow rate, and real-time computer group efficiency. An abnormal decrease in efficiency is an early sign of scaling, wear, or internal leakage. Process parameters are integrated with DCS data, such as load, speed, and valve opening, for correlation trend analysis.

[0020] The fault characteristics of the steam centripetal turbine unit are diagnosed based on the state parameters using a predetermined diagnostic algorithm. In one specific implementation, the fault characteristics include at least high vibration, high temperature, low pressure, imbalance, misalignment, scaling, wear, and leakage.

[0021] In one specific implementation, the predetermined diagnostic algorithm includes a threshold judgment algorithm, a model analysis algorithm, and / or a trend analysis algorithm. Different diagnostic algorithms are applicable to different state parameters. For example, the threshold judgment method is applicable to parameters closely related to safety, such as vibration, pressure, temperature, and rotational speed. The model analysis method actually integrates all parameters to form a digital twin to monitor or even predict the health status of the centripetal turbine system. Individual parameters may all be within the threshold, but the system may already be diseased.

[0022] The threshold judgment algorithm obtains fault state parameters that exceed the threshold by setting an alarm limit for a single state parameter, such as vibration value > 7.1 mm / s. The model analysis algorithm establishes a digital twin model of the unit, compares the measured values ​​with the predicted values, and alarms are triggered if the deviation is too large. The trend analysis algorithm uses machine learning such as LSTM time series prediction and deep learning such as CNN to identify vibration spectrum diagrams, and uses historical data to train the model to achieve early classification and warning of faults.

[0023] A corresponding structured fault code is generated based on the fault characteristics; in one specific implementation, the structure of the structured fault code includes a fault system code, a fault level code, a fault location code, a fault mode code, and / or a fault additional feature code. The structured fault codes are input into a predefined fault knowledge base to generate alarm information and maintenance work orders, which are then sent to the management platform. In one specific implementation, the predetermined fault knowledge base is configured as a structured fault coding library transformed from industry standards, expert experience, and / or historical fault cases.

[0024] In one specific implementation, the fault system code is used to identify the main system and / or cross-system to which the fault belongs. Figure 2This is a schematic diagram of the main system and its cross-systems of a steam centripetal turbine unit according to an embodiment of the present invention, as shown below. Figure 2 As shown, the main system and its cross-systems and their fault codes include: T: Turbine body; B: Lubrication station; C: Control system; E: Grid-connected electrical system; G: Generator; H: EH oil station; S: Gearbox; V: Valve; TB: Cross-system between turbine body and lubrication station; TG: Cross-system between turbine body and generator; TH: Cross-system between turbine body and EH oil station; TS: Cross-system between turbine body and gearbox; TV: Cross-system between turbine body and valve; CB: Cross-system between control system and lubrication system; CH: Cross-system between control system and EH oil station. Cross-systems of the station; CV: Cross-system of control system and valve; BS: Cross-system of lubrication station and gearbox; GS: Cross-system of generator and valve; EG: Cross-system of electrical system and generator; VH: Cross-system of valve and EH station; THV: Cross-system of turbine body, EH station and valve; TBS: Cross-system of turbine body, lubrication station and gearbox; TGS: Cross-system of turbine body, generator and lubrication station; CHV: Cross-system of control system, EH station and valve, and other subsystems and cross-systems.

[0025] In one specific implementation, the fault level code is used to indicate the urgency of the fault. The fault levels and their codes include: 0, Prompt: The state deviates from normal and needs to be observed; 1, Warning: The trend is deteriorating and needs to be monitored and troubleshooting prepared; 2, Fault: Performance is affected and a planned shutdown for maintenance is required; 3, Critical Fault: It is about to cause damage and needs to be shut down as soon as possible; 4, Emergency Shutdown: The protection system has been activated or the system is manually shut down immediately.

[0026] In one specific implementation, the fault location code is used to locate the faulty component. The faulty component and its code include: 01: rotor; 02: moving blade; 10: radial bearing; 11: thrust bearing; 20: speed control valve; 21: actuator; 30: shaft end seal; and other unlisted faulty components.

[0027] In one specific implementation, the fault mode code is used to describe the fault phenomenon and / or type. The fault modes and their fault codes include: VIBH: high vibration; TEMH: high temperature; PRSL: low pressure; UNBL: imbalance; MISL: misalignment; FOUL: scaling; WEAR: wear; LEAK: leakage; and other unlisted fault modes.

[0028] In one specific implementation, the fault additional feature code is used to indicate the fault additional feature information. The fault additional features and their fault codes include: AX: axial; RD: radial; 1X: 1 times the rotational frequency; 2X: 2 times the rotational frequency; BP: blade passing frequency; and other unlisted fault features.

[0029] In one specific implementation, faults can be quickly identified based on the generated structured fault codes, for example: When the structured fault code is T310-VIBH-1X, it indicates a serious fault in the turbine body, radial bearing, high vibration level, characterized by 1st harmonic frequency, which strongly indicates rotor imbalance. When the structured fault code is CHV220-PRSL---, it indicates a cross-system fault between the control system and the EH oil station and valves, and the speed control valve pressure is too low. This may indicate valve jamming or actuator failure. When the structured fault code is TBS110-TEMH---, it indicates a warning in the high-speed bearing system connected to the turbine body on the gearbox, specifically a radial bearing, indicating a high temperature. This prompts you to check the lubricating oil or cooling system.

[0030] Example 2: Figure 3 This is a schematic diagram of the structure of a smart toothbrush according to an embodiment of the present invention, as shown below. Figure 3 As shown, the device includes a processor 301, a memory 302, a bus 303, and a computer program stored in the memory 302 and executable on the processor 301. The processor 301 includes one or more processing cores. The memory 302 is connected to the processor 301 via the bus 303. The memory 302 is used to store program instructions. When the processor executes the computer program, it implements the steps in the above-described method embodiment of Embodiment 1 of the present invention.

[0031] Furthermore, as an executable solution, the smart toothbrush can be a computer unit, which can be a desktop computer, laptop, handheld computer, or cloud server, etc. The computer unit may include, but is not limited to, a processor and memory. Those skilled in the art will understand that the above-described structure of the computer unit is merely an example and does not constitute a limitation on the computer unit. It may include more or fewer components, or combine certain components, or use different components. For example, the computer unit may also include input / output devices, network access devices, buses, etc., and this embodiment of the invention does not limit this.

[0032] Furthermore, as an executable solution, the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), 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. The general-purpose processor can be a microprocessor or any conventional processor, etc. The processor is the control center of the computer unit, connecting various parts of the entire computer unit via various interfaces and lines.

[0033] The memory can be used to store the computer programs and / or modules. The processor implements various functions of the computer unit by running or executing the computer programs and / or modules stored in the memory and by calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system and at least one application program required for a function; the data storage area may store data created based on the use of the mobile phone, etc. In addition, the memory may include high-speed random access memory and may also include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital card (SD card), flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0034] Example 3: The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the method described in the embodiments of the present invention.

[0035] If the modules / units integrated in the computer unit are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), and software distribution media, etc. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction.

[0036] Example 4: The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the steam centrifugal turbine unit fault monitoring method as described above.

[0037] Although the invention has been specifically shown and described in conjunction with preferred embodiments, those skilled in the art should understand that various changes in form and detail may be made to the invention without departing from the spirit and scope of the invention as defined in the appended claims, all of which shall be within the scope of protection of the invention.

Claims

1. A method for fault monitoring of a steam centripetal turbine unit, characterized in that, Includes the following steps: The state parameters of the steam centripetal turbine unit are obtained through real-time monitoring, including vibration signal parameters, thermodynamic performance parameters and / or process parameters; The fault characteristics of the steam centripetal turbine unit are diagnosed based on the state parameters using a predetermined diagnostic algorithm. A corresponding structured fault code is generated based on the fault characteristics. The structure of the structured fault code includes a fault system code, a fault level code, a fault location code, a fault mode code, and / or a fault additional feature code. The structured fault codes are input into a predetermined fault knowledge base to generate alarm information and maintenance work orders, which are then sent to the management platform.

2. The method for fault monitoring of a steam centripetal turbine unit according to claim 1, characterized in that, The vibration signal parameters include shaft vibration signal and shell vibration signal; the thermodynamic performance parameters include inlet steam temperature, outlet steam temperature, pressure and / or flow rate; the process parameters include load, speed and / or valve opening.

3. The method for fault monitoring of a steam centripetal turbine unit according to claim 1, characterized in that, The predetermined diagnostic algorithm includes a threshold judgment algorithm, a model analysis algorithm, and / or a trend analysis algorithm, wherein, The threshold judgment algorithm obtains fault state parameters that exceed the threshold by setting an alarm limit for a single state parameter. The model analysis algorithm establishes a digital twin model of the unit, compares the measured values ​​with the predicted values, and alarms are triggered if the deviation is too large. The trend analysis algorithm uses machine learning and deep learning models to identify vibration spectrum diagrams.

4. The method for fault monitoring of a steam centripetal turbine unit according to claim 1, characterized in that, The fault system code is used to identify the main system and / or cross-system to which the fault belongs; the fault level code is used to indicate the urgency of the fault. The fault location code is used to locate the faulty component; the fault mode code is used to describe the fault phenomenon and / or type. The additional fault feature code is used to indicate additional fault feature information.

5. The method for fault monitoring of a steam centripetal turbine unit according to claim 1, characterized in that, The predetermined fault knowledge base is configured as a structured fault coding library based on industry standards, expert experience, and / or historical fault cases.

6. A fault monitoring device for a steam centrifugal turbine unit, characterized in that, The system includes a memory and a processor, wherein the memory stores at least one program, which is executed by the processor to implement the steam centrifugal turbine unit fault monitoring method as described in any one of claims 1-5.

7. A computer-readable storage medium, characterized in that, The storage medium stores at least one program, which is executed by a processor to implement the steam centrifugal turbine unit fault monitoring method as described in any one of claims 1-5.

8. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the steam centrifugal turbine unit fault monitoring method as described in any one of claims 1-5.

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