Steam turbine clutch gear meshing noise online acquisition system and method

Abstract

The invention provides a steam turbine clutch gear meshing noise online acquisition system and method, and relates to the field of equipment state monitoring, and the system comprises an acoustic sensing module which comprises at least two acoustic sensors and is disposed in a gear meshing area of an input assembly and an output assembly of a clutch; the signal processing module is connected with the acoustic sensor through a shielding cable and is used for carrying out filtering, amplification and analog-to-digital conversion on an original noise signal acquired by the acoustic sensor to obtain noise data; and the data storage and analysis module is connected with the signal processing module through an industrial Ethernet, and is used for receiving and storing the noise data and marking the acquisition time and the operation condition of the steam turbine. According to the method, the acoustic sensor is deployed in a near field and self-adaptive signal processing is combined, so that strong background interference under the variable working condition of the steam turbine is effectively eliminated, and the signal-to-noise ratio of the gear meshing characteristic signal is remarkably improved.

Description

Technical Field

[0001] This application relates to the field of equipment condition monitoring, and more specifically, to an online acquisition system and method for the meshing noise of a steam turbine clutch gear. Background Technology

[0002] Steam turbines, as core power equipment, are widely used in major industrial fields such as power plants and ships. Their associated clutches are key components for power transmission, and the meshing state of the gears directly affects the reliability of the entire transmission system. During operation, gears are prone to tooth surface wear, pitting, and even tooth breakage due to long-term exposure to alternating loads. These faults typically manifest as abnormal changes in meshing noise in their early stages. Therefore, online monitoring of clutch gear meshing noise is an effective means of achieving predictive maintenance and avoiding unplanned downtime.

[0003] Currently, there are general technologies for monitoring gearbox conditions using vibration or acoustic sensors. However, directly applying these general technologies to steam turbine clutches faces significant challenges. First, steam turbines operate under complex and variable conditions, especially under condensing, extraction, and back pressure conditions. The background noise intensity and spectral characteristics of the unit itself vary greatly, making it difficult for general noise monitoring methods to effectively eliminate this strong background interference. This results in low signal-to-noise ratios and insufficient accuracy in the extracted gear meshing characteristic signals. Second, existing technologies lack noise analysis strategies tailored to specific steam turbine operating conditions, making it difficult to establish a precise correspondence between operating conditions and noise characteristics. Consequently, they cannot effectively identify subtle anomalies in gear conditions under different operating loads. Summary of the Invention

[0004] The purpose of this application is to provide an online acquisition system and method for the meshing noise of steam turbine clutch gears, so as to achieve accurate early warning of early failures of steam turbine clutch gears and avoid unplanned downtime.

[0005] In one aspect, an online acquisition system for meshing noise of steam turbine clutch gears is provided. The system may include: an acoustic sensing module, a signal processing module, and a data storage and analysis module. The acoustic sensing module includes at least two acoustic sensors, which are deployed in the gear meshing area of ​​the input and output components of the clutch. The signal processing module is connected to the acoustic sensor via a shielded cable and is used to filter, amplify, and perform analog-to-digital conversion on the raw noise signal collected by the acoustic sensor to obtain noise data. The data storage and analysis module is connected to the signal processing module via an industrial Ethernet network. It is used to receive and store the noise data and mark the acquisition time and steam turbine operating conditions. The data storage and analysis module is also used to establish a noise benchmark model under different operating conditions based on historical data, and to quantitatively evaluate the gear meshing health status by calculating the deviation between real-time data and the current operating condition benchmark model. The operating conditions include condensation, extraction condensation, and back pressure.

[0006] In one possible implementation, the acoustic sensor is a piezoelectric acoustic sensor.

[0007] In one possible implementation, the distance between the acoustic sensor and the meshing tooth surface of the gear is 5-15 mm.

[0008] In one possible implementation, the noise data includes sound pressure level and frequency distribution.

[0009] In one possible implementation, the acoustic sensing module further includes a temperature compensation unit integrated with the acoustic sensor for correcting the noise signal acquisition gain based on the real-time temperature during clutch operation.

[0010] In one possible implementation, the signal processing module includes an adaptive filtering subunit, which has built-in interference noise models for different operating conditions of the steam turbine. The noise data is determined by comparing and eliminating interference signals that match the interference noise models.

[0011] In one possible implementation, the acoustic sensors are arranged in an array along the gear meshing line, with the spacing between adjacent sensors being an integer multiple of the length of the gear meshing base pitch.

[0012] Secondly, a method for online acquisition of meshing noise of a steam turbine clutch gear is provided, applied to any of the systems described in the first aspect, the method comprising: The acoustic sensor collects the raw noise signal generated by gear meshing in real time and transmits it to the signal processing module through a shielded cable; The signal processing module sequentially filters, amplifies, and converts the original noise signal to digital noise signal. The data storage and analysis module is connected to the signal processing module via an industrial Ethernet network to receive and store noise data and mark the acquisition time and steam turbine operating conditions. The data storage and analysis module is also used to establish noise benchmark models under different operating conditions based on historical data, and to quantitatively evaluate the gear meshing health status by calculating the deviation between real-time data and the current operating condition benchmark model. The operating conditions include condensation, extraction condensation, and back pressure.

[0013] Thirdly, an electronic device is provided, which includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; When a processor executes a program stored in memory, it implements any of the steps described in the second aspect above.

[0014] Fourthly, a computer-readable storage medium is provided, wherein a computer program is stored therein, and when executed by a processor, the computer program implements the steps of any of the methods described in the second aspect above.

[0015] This application provides an online acquisition system and method for steam turbine clutch gear meshing noise. The system includes an acoustic sensing module, a signal processing module, and a data storage and analysis module. The acoustic sensing module includes at least two acoustic sensors deployed in the gear meshing areas of the input and output components of the clutch. The signal processing module is connected to the acoustic sensors via shielded cables and is used to filter, amplify, and perform analog-to-digital conversion on the raw noise signals acquired by the acoustic sensors to obtain noise data. The data storage and analysis module is connected to the signal processing module via an industrial Ethernet network and is used to receive and store the noise data, and to mark the acquisition time and steam turbine operating conditions. The data storage and analysis module is also used to establish noise benchmark models under different operating conditions based on historical data, and to quantitatively evaluate the gear meshing health status by calculating the deviation between real-time data and the current operating condition benchmark model. The operating conditions include condensing, extraction condensing, and back pressure. This application effectively eliminates strong background interference under varying steam turbine operating conditions by deploying acoustic sensors in the near field and combining them with adaptive signal processing, significantly improving the signal-to-noise ratio of the gear meshing characteristic signals. By establishing a noise benchmark model that precisely corresponds to various operating conditions such as condensation, extraction condensation, and back pressure, and using deviation for quantitative evaluation, accurate and early diagnosis of the meshing health status of clutch gears is achieved. This overcomes the problem of inaccurate monitoring under complex operating conditions using traditional methods and provides a reliable basis for predictive maintenance. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 A schematic diagram of an online acquisition system for the meshing noise of a steam turbine clutch gear provided in this application embodiment; Figure 2 A flowchart illustrating an online acquisition method for steam turbine clutch gear meshing noise provided in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0018] The technical solutions of 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. Unless otherwise defined, the technical or scientific terms used in this application should have the ordinary meaning understood by those skilled in the art. The words "first," "second," and similar terms used in this application do not indicate any order, quantity, or importance, but are only used to distinguish different components. The words "comprising" or "including," etc., mean that the element or object preceding the word covers the element or object listed after the word and its equivalents, but do not exclude other elements or objects. The words "connected," "coupled," or "connected," etc., are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. "Up," "down," "left," "right," etc., are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0019] Steam turbines, as core power equipment, are widely used in major industrial fields such as power plants and ships. Their associated clutches are key components for power transmission, and the meshing state of the gears directly affects the reliability of the entire transmission system. During operation, gears are prone to tooth surface wear, pitting, and even tooth breakage due to long-term exposure to alternating loads. These faults typically manifest as abnormal changes in meshing noise in their early stages. Therefore, online monitoring of clutch gear meshing noise is an effective means of achieving predictive maintenance and avoiding unplanned downtime.

[0020] Currently, there are general technologies for monitoring gearbox conditions using vibration or acoustic sensors. However, directly applying these general technologies to steam turbine clutches faces significant challenges. First, steam turbines operate under complex and variable conditions, especially under condensing, extraction, and back pressure conditions. The background noise intensity and spectral characteristics of the unit itself vary greatly, making it difficult for general noise monitoring methods to effectively eliminate this strong background interference. This results in low signal-to-noise ratios and insufficient accuracy in the extracted gear meshing characteristic signals. Second, existing technologies lack noise analysis strategies tailored to specific steam turbine operating conditions, making it difficult to establish a precise correspondence between operating conditions and noise characteristics. Consequently, they cannot effectively identify subtle anomalies in gear conditions under different operating loads.

[0021] Therefore, this application provides an online acquisition system for the meshing noise of steam turbine clutch gears to achieve accurate early warning of early failures of steam turbine clutch gears and avoid unplanned downtime.

[0022] The preferred embodiments of this application are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit this application. Furthermore, the embodiments and features in the embodiments of this application can be combined with each other without conflict.

[0023] Figure 1 This is a schematic diagram of an online acquisition system for the meshing noise of a steam turbine clutch gear, provided as an embodiment of this application. Figure 1 As shown, the system may include: an acoustic sensing module, a signal processing module, and a data storage and analysis module; A. The acoustic sensing module includes at least two acoustic sensors, which are deployed in the gear meshing area of ​​the input and output components of the clutch. In a preferred embodiment, the acoustic sensor is a piezoelectric acoustic sensor. This type of sensor was chosen because of its outstanding advantages, including a wide frequency response range, high sensitivity, and robustness, which enable it to accurately capture high-frequency impact sound signals generated during gear meshing, which is crucial for subsequent analysis of minute damage to the tooth surface.

[0024] To ensure consistent acoustic signal acquisition quality and intensity, the sensor's installation position was precisely defined. The distance (d) between the acoustic sensor's probe end face and the gear meshing tooth surface was strictly set within the range of 5 mm to 15 mm. Extensive experimental verification confirmed that this distance range is crucial for achieving the optimal signal-to-noise ratio: if the distance is less than 5 mm, the sensor will be too close to the high-speed rotating gear, posing a safety risk of contamination from lubricating oil splashes or interference with the gear; if the distance is greater than 15 mm, the attenuation effect of the air medium on sound waves will be significantly enhanced, and the interference from ambient background noise will increase, resulting in insufficient effective signal strength, a decreased signal-to-noise ratio, and affecting the accuracy of subsequent analysis. Therefore, the 5-15 mm distance defined in this scheme ensures both safety and maximizes the fidelity of signal acquisition.

[0025] To further improve signal stability, the acoustic sensing module may optionally include a temperature compensation unit integrated with the acoustic sensor. This unit is configured to dynamically adjust the sensor's acquisition gain based on the real-time temperature changes within the gearbox during clutch operation, compensating for sensor sensitivity drift caused by temperature variations, thereby ensuring the comparability of noise data acquired under different operating conditions.

[0026] The acoustic sensors are arranged in an array along the gear meshing line, and the spacing between adjacent sensors is an integer multiple of the length of the gear meshing base pitch.

[0027] To further improve the accuracy and depth of signal analysis, in a preferred embodiment of this application, the acoustic sensing module adopts an array layout. Specifically, at least three acoustic sensors are arranged in a linear array at intervals along the meshing line of the gear.

[0028] The core innovation of this layout lies in the precise definition of the sensor spacing: the center distance (D) between adjacent sensors is set to be an integer multiple (nPb, where n is a positive integer, usually 1 or 2) of the meshing base pitch (Pb) of the clutch gear.

[0029] Because the spacing is an integer multiple of the base pitch, this method generates acoustic signals with a fixed phase relationship on each sensor as the same tooth passes in front of it in sequence. This arrangement synchronizes the spatial distribution of the sensor array with the temporal periodicity of gear meshing, enabling the system to accurately track the propagation of meshing impact force along the meshing line by analyzing the phase difference or time delay between multiple sensor signals.

[0030] B. The signal processing module is connected to the acoustic sensor via a shielded cable. It is used to filter, amplify, and perform analog-to-digital conversion on the raw noise signal collected by the acoustic sensor to obtain noise data. The noise data includes sound pressure level and frequency distribution.

[0031] Specifically, the raw signal is first processed by a bandpass filter. The passband range of this filter is set according to the characteristic frequencies of gear meshing noise. Its lower cutoff frequency is typically set in the kilohertz range to filter out the main background noise such as low-frequency vibrations and electromagnetic interference from the steam turbine; its upper cutoff frequency is set in the tens to hundreds of kilohertz range to cover the gear meshing frequency and its higher-order harmonics, and to filter out irrelevant high-frequency noise. This step effectively improves the signal-to-noise ratio.

[0032] The filtered signal amplitude is usually small and needs to be amplified. The module has a built-in programmable gain amplifier. The gain of this amplifier can be automatically or manually adjusted according to the sound pressure level at the scene to ensure that the signal is amplified to the optimal level range suitable for the analog-to-digital converter sampling. This avoids excessive quantization error due to a weak signal and also prevents signal overload distortion.

[0033] The amplified analog signal is converted into a digital signal by a high-precision analog-to-digital converter (ADC). The sampling rate of the ADC must satisfy the Nyquist sampling theorem and be at least twice that of the highest frequency component of the signal. To ensure the capture of high-frequency components, a sampling rate in the range of hundreds of kS / s to several MS / s is typically chosen. Simultaneously, the ADC should have sufficient resolution (e.g., 16-bit or 24-bit) to accurately reproduce the dynamic range of the signal.

[0034] The signal processing module includes an adaptive filtering subunit, which has built-in interference noise models for different operating conditions of the steam turbine. By comparing and eliminating interference signals that match the interference noise models, the noise data is determined.

[0035] In other words, the signal processing module also includes an adaptive filtering subunit. This subunit pre-stores typical background noise models of the steam turbine under different operating conditions (such as condensing, extraction-condensing, and back pressure). By comparing the input noise signal with these noise models in real time, it dynamically generates an inverse cancellation signal, thereby selectively eliminating interference components related to the background noise models and ultimately outputting a cleaner characteristic noise signal that primarily reflects the gear meshing state. This process significantly improves the accuracy of signal analysis under complex and variable operating conditions.

[0036] The digital signal obtained after the above processing is the noise data. This data not only contains the original time-domain waveform, but more importantly, through calculations within the module or by the data storage and analysis module, key feature values ​​for condition monitoring can be extracted, mainly including: Sound pressure level: used to characterize the overall intensity of noise.

[0037] Frequency distribution: Usually presented in the form of a spectrum diagram, it is used to analyze the distribution of noise energy on the frequency axis, thereby identifying characteristic frequency components such as gear meshing frequency and sidebands. This is the key basis for diagnosing abnormal gear conditions.

[0038] C. The data storage and analysis module is connected to the signal processing module via an industrial Ethernet network. It is used to receive and store noise data and mark the acquisition time and steam turbine operating conditions. The data storage and analysis module is also used to establish noise benchmark models under different operating conditions based on historical data, and to quantitatively evaluate the gear meshing health status by calculating the deviation between real-time data and the current operating condition benchmark model; the operating conditions include condensation, extraction condensation and back pressure.

[0039] Specifically, the data storage and analysis module incorporates machine learning algorithms to establish evaluation benchmarks based on historical data. During the initial operation or stabilization phase after a major overhaul, the module collects a large amount of gear noise data under known healthy conditions. Subsequently, it categorizes the data according to different operating conditions (condensing, extraction, and back pressure) and extracts feature vectors of the noise signals for each condition (such as specific frequency band energy, meshing frequency amplitude, kurtosis index, etc.), and constructs a noise benchmark model for that condition. This model can be a statistical model, such as a multivariate Gaussian model, representing the mean vector μ and covariance matrix Σ of the feature vectors under healthy conditions.

[0040] During the real-time monitoring phase, the core task of the module is to quantitatively assess the health status. The process is as follows: a. Operating condition identification: Determine the current operating condition of the steam turbine (e.g., extraction and condensation condition).

[0041] b. Model call: Call the pre-trained noise benchmark model corresponding to this working condition.

[0042] c. Deviation Calculation: Extract the feature vector of the real-time noise data and calculate its deviation (Deviation Index, DI) from the current operating condition benchmark model. This is a quantitative health indicator.

[0043] As a preferred implementation, the deviation (DI) can be calculated using Mahalanobis distance:

[0044] in, Here, μ is the real-time feature vector, and μ is the mean vector. It is the inverse of the covariance matrix of the benchmark model.

[0045] This module compares the calculated real-time deviation (DI) with a preset safety threshold for the operating condition (e.g., DI < 2 indicates healthy, 2 ≤ DI < 4 indicates caution, and DI ≥ 4 indicates an alarm). When the DI value continuously exceeds the threshold, the module generates different levels of warning information (e.g., caution, abnormal, alarm), thereby achieving quantitative, accurate, and early diagnosis of the gear meshing health status.

[0046] This application provides an online acquisition system for gear meshing noise in a steam turbine clutch. The system includes an acoustic sensing module, a signal processing module, and a data storage and analysis module. The acoustic sensing module includes at least two acoustic sensors deployed in the gear meshing areas of the clutch's input and output components. The signal processing module is connected to the acoustic sensors via shielded cables and is used to filter, amplify, and perform analog-to-digital conversion on the raw noise signals acquired by the acoustic sensors to obtain noise data. The data storage and analysis module is connected to the signal processing module via an industrial Ethernet network and is used to receive and store the noise data, and to mark the acquisition time and steam turbine operating conditions. The data storage and analysis module is also used to establish noise benchmark models under different operating conditions based on historical data, and to quantitatively assess the gear meshing health status by calculating the deviation between real-time data and the current operating condition benchmark model. Operating conditions include condensing, extraction condensing, and back pressure. This application, by deploying acoustic sensors in the near field and combining them with adaptive signal processing, effectively eliminates strong background interference under varying steam turbine operating conditions, significantly improving the signal-to-noise ratio of the gear meshing characteristic signals. By establishing a noise benchmark model that precisely corresponds to various operating conditions such as condensation, extraction condensation, and back pressure, and using deviation for quantitative evaluation, accurate and early diagnosis of the meshing health status of clutch gears is achieved. This overcomes the problem of inaccurate monitoring under complex operating conditions using traditional methods and provides a reliable basis for predictive maintenance.

[0047] Figure 2 This is a flowchart illustrating an online method for collecting meshing noise of a steam turbine clutch gear, provided as an embodiment of this application. Figure 2 As shown, the method may include: Step S210: The acoustic sensor collects the raw noise signal generated by gear meshing in real time and transmits it to the signal processing module through a shielded cable; Step S220: The signal processing module sequentially filters, amplifies, and performs analog-to-digital conversion on the original noise signal to obtain a digital noise signal; Step S230: The data storage and analysis module is connected to the signal processing module via an industrial Ethernet to receive and store noise data, and to mark the acquisition time and steam turbine operating conditions. The data storage and analysis module is also used to establish a noise benchmark model under different operating conditions based on historical data, and to quantitatively evaluate the gear meshing health status by calculating the deviation between real-time data and the current operating condition benchmark model. The operating conditions include condensation, extraction condensation, and back pressure.

[0048] This application also provides an electronic device, such as... Figure 3 As shown, it includes a processor 310, a communication interface 320, a memory 330, and a communication bus 340, wherein the processor 310, the communication interface 320, and the memory 330 communicate with each other through the communication bus 340.

[0049] Memory 330 is used to store computer programs; When the processor 310 executes the program stored in the memory 330, it performs the following steps: The acoustic sensor collects the raw noise signal generated by gear meshing in real time and transmits it to the signal processing module through a shielded cable; The signal processing module sequentially filters, amplifies, and converts the original noise signal to digital noise signal. The data storage and analysis module is connected to the signal processing module via an industrial Ethernet network to receive and store noise data and mark the acquisition time and steam turbine operating conditions. The data storage and analysis module is also used to establish noise benchmark models under different operating conditions based on historical data, and to quantitatively evaluate the gear meshing health status by calculating the deviation between real-time data and the current operating condition benchmark model. The operating conditions include condensation, extraction condensation, and back pressure.

[0050] The communication bus mentioned above can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not mean that there is only one bus or one type of bus.

[0051] The communication interface is used for communication between the aforementioned electronic devices and other devices.

[0052] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0053] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be 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, or discrete hardware components.

[0054] The implementation methods and beneficial effects of the various components of the electronic device in the above embodiments for solving the problem can be found in [reference needed]. Figure 2 The steps in the illustrated embodiments are used to implement the electronic device. Therefore, the specific working process and beneficial effects of the electronic device provided in this application will not be repeated here.

[0055] In another embodiment provided in this application, a computer-readable storage medium is also provided, which stores instructions that, when executed on a computer, cause the computer to perform an online acquisition method for steam turbine clutch gear meshing noise as described in any of the above embodiments.

[0056] In another embodiment provided in this application, a computer program product containing instructions is also provided, which, when run on a computer, causes the computer to execute any of the above embodiments of an online acquisition method for steam turbine clutch gear meshing noise.

[0057] Those skilled in the art will understand that the embodiments in this application can be provided as methods, systems, or computer program products. Therefore, the embodiments in this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the embodiments in this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0058] This application describes embodiments of methods, apparatus (systems), and computer program products according to embodiments of this application with reference to flowchart illustrations and / or block diagrams. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0059] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0060] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0061] Although preferred embodiments have been described in this application, 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 the embodiments of this application.

[0062] 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 in this application and their equivalents, then this application also intends to include these modifications and variations.

Claims

1. An online acquisition system for the meshing noise of a steam turbine clutch gear, characterized in that, The system includes: an acoustic sensing module, a signal processing module, and a data storage and analysis module; The acoustic sensing module includes at least two acoustic sensors, which are deployed in the gear meshing area of ​​the input and output components of the clutch. The signal processing module is connected to the acoustic sensor via a shielded cable and is used to filter, amplify, and perform analog-to-digital conversion on the raw noise signal collected by the acoustic sensor to obtain noise data. The data storage and analysis module is connected to the signal processing module via an industrial Ethernet network. It is used to receive and store the noise data and mark the acquisition time and steam turbine operating conditions. The data storage and analysis module is also used to establish a noise benchmark model under different operating conditions based on historical data, and to quantitatively evaluate the gear meshing health status by calculating the deviation between real-time data and the current operating condition benchmark model. The operating conditions include condensation, extraction condensation, and back pressure.

2. The system as described in claim 1, characterized in that, The acoustic sensor is a piezoelectric acoustic sensor.

3. The system as described in claim 1, characterized in that, The distance between the acoustic sensor and the meshing tooth surface of the gear is 5-15mm.

4. The system as described in claim 1, characterized in that, The noise data includes sound pressure level and frequency distribution.

5. The system as described in claim 1, characterized in that, The acoustic sensing module also includes a temperature compensation unit, which is integrated with the acoustic sensor and is used to correct the noise signal acquisition gain according to the real-time temperature during clutch operation.

6. The system as described in claim 1, characterized in that, The signal processing module includes an adaptive filtering subunit, which has built-in interference noise models for different operating conditions of the steam turbine. By comparing and eliminating interference signals that match the interference noise models, the noise data is determined.

7. The system as described in claim 1, characterized in that, The acoustic sensors are arranged in an array along the gear meshing line, and the spacing between adjacent sensors is an integer multiple of the length of the gear meshing base pitch.

8. A method for online acquisition of meshing noise of a steam turbine clutch gear, characterized in that, Applied to the system according to any one of claims 1-7, the method comprises: The acoustic sensor collects the raw noise signal generated by gear meshing in real time and transmits it to the signal processing module through a shielded cable; The signal processing module sequentially filters, amplifies, and converts the original noise signal to digital noise signal. The data storage and analysis module is connected to the signal processing module via an industrial Ethernet network to receive and store noise data and mark the acquisition time and steam turbine operating conditions. The data storage and analysis module is also used to establish noise benchmark models under different operating conditions based on historical data, and to quantitatively evaluate the gear meshing health status by calculating the deviation between real-time data and the current operating condition benchmark model. The operating conditions include condensation, extraction condensation, and back pressure.

9. An electronic device, characterized in that, The electronic device includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; A processor, when executing a program stored in memory, implements the method of claim 8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method of claim 8.

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