Stress wave acquisition transmitter

Abstract

The invention relates to an acquisition transmitter, in particular to a stress wave acquisition transmitter. A sound emission sensor transmits monitored equipment sound wave frequency to a charge amplifier in a charge form; the charge amplifier is used for amplifying the received charge and outputting a voltage signal; the amplifier power supply isolator separates output signals of the charge amplifier and transmits the output signals to the AD signal acquisition and conversion unit; the AD signal acquisition and conversion unit is used for acquiring a signal output by an amplifier power isolator at a set sampling rate, converting the signal into a digital signal and sending the digital signal to the CPU processing unit; the CPU processing unit packs the digital signal data output by the AD signal acquisition and conversion unit according to a set time interval and then transmits the digital signal data to the Ethernet chip unit, and the Ethernet chip unit converts the digital signal data into network data; according to the invention, acquisition, amplification and power supply functions are integrated, and a network transmission mode is adopted, so that the system has the characteristics of high bandwidth and fast data transmission, and multi-point synchronous transmission can be realized.

Description

Technical Field

[0001] The present invention relates to a collection and transmission device, in particular to a stress wave collection and transmission device, and belongs to the technical field of industrial products. Background Art

[0002] At present, boiler leakage accidents have become the main contradiction affecting the safe and stable operation of power generation equipment in my country. Years of operation practice have proved that most leaks develop from small leaks (such as sand holes and bubbles in materials or welds). When they reach the level that can be perceived by people, the damage caused by the leakage is already quite serious. Therefore, how to detect minor leaks on the pressure-bearing and heating surfaces of boilers at an early stage and conduct visual real-time monitoring of the leakage development trend is of great significance for properly arranging maintenance strategies, shortening maintenance time and reducing the inspection rate.

[0003] Real-time monitoring of boiler noise is an important basis for monitoring whether there is leakage after the boiler is pressurized. Whether the noise in the furnace can be continuously, accurately and timely collected (noise signals are obtained by collecting sound waves, which are stress waves) and sent out without loss is one of the keys to detection.

[0004] When monitoring boiler noise, sensors are needed to collect the noise signals inside the furnace, amplify them, and transmit them. A power supply is also required to power the sensors and amplifiers. Currently, sensors, amplifiers, and power supplies are usually arranged independently. However, the boiler monitoring environment is harsh, and a distributed arrangement affects the efficiency and reliability of noise signal collection and transmission, making it impossible to make timely leak judgments. Summary of the Invention

[0005] In view of this, the present invention provides a stress wave acquisition and transmission device, which integrates the acquisition, amplification and power supply functions into one, and adopts a network transmission method. It has the characteristics of high bandwidth and fast data transmission, and can realize multi-point synchronous transmission.

[0006] To achieve the above-mentioned and other related purposes, the present invention provides a stress wave acquisition and transmission device, comprising: an integrated acoustic emission sensor, a charge amplifier, an amplifier power supply isolator, a voltage conversion unit, a power supply module, an Ethernet chip unit, an AD signal acquisition and conversion unit, a CPU processing unit, and a network interface;

[0007] The acoustic emission sensor is electrically connected to the charge signal amplifier to transmit the monitored device sound wave frequency to the charge amplifier in the form of charge;

[0008] The charge amplifier is used to amplify the received charge and output a voltage signal;

[0009] The charge amplifier is electrically connected to the amplifier power isolator, which separates the output signal of the charge amplifier and transmits it to the AD signal acquisition and conversion unit; at the same time, the amplifier power isolator is also used to power the acoustic emission sensor and the charge signal amplifier;

[0010] The AD signal acquisition and conversion unit acquires the signal output by the amplifier power supply isolator at a set sampling rate and converts it into a digital signal and sends it to the CPU processing unit;

[0011] The CPU processing unit is connected to the network interface, and the CPU processing unit packages the digital signal data output by the AD signal acquisition and conversion unit according to the set time interval and transmits it to the Ethernet chip unit, and converts it into network data through the Ethernet chip unit;

[0012] The network interface provides POE power supply and data transmission interface; the power module is connected to the network interface through a network isolation transformer; the voltage provided by the power module is converted by the voltage conversion unit and provided to the AD signal acquisition and conversion unit, the CPU processing unit and the amplifier power isolator respectively.

[0013] In one embodiment of the present invention, the acoustic emission sensor is a piezoelectric ceramic sensor.

[0014] In one embodiment of the present invention, an indicator light unit is further included; the indicator light unit is connected to the CPU processing unit;

[0015] The indicator light unit is used to display the current working status, including whether the network connection is normal, leakage alarm and dust blockage alarm status.

[0016] In one embodiment of the present invention, stress wave acquisition and transmission devices are arranged at different positions of the object under test, and a plurality of the stress wave acquisition and transmission devices are respectively connected to a POE switch via a network interface to achieve multi-point synchronous acquisition and transmission of stress wave data.

[0017] In one embodiment of the present invention, a plurality of stress wave collection and transmission devices are arranged at the same monitoring position of the object under test.

[0018] In one embodiment of the present invention, the device comprises a housing and a charge amplifier, an amplifier power isolator and a circuit board sealed inside the housing;

[0019] The voltage conversion unit, power supply module, Ethernet chip unit, AD signal acquisition and conversion unit and CPU processing unit are integrated on the circuit board;

[0020] The charge amplifier and the amplifier power isolator are electrically connected and mounted on a support plate inside the housing;

[0021] The housing is provided with an aviation plug as a network interface.

[0022] In one embodiment of the present invention, the interior of the housing is divided into an upper space and a lower space, the circuit board is arranged in the upper space; the charge amplifier and the amplifier power isolator are arranged in the lower space.

[0023] In one embodiment of the present invention, it also includes: a host computer, which is electrically connected to the network interface of the stress wave acquisition and transmission device; the host computer is used to receive the stress wave data output by the stress wave acquisition and transmission device, and preprocess the stress wave data to obtain preprocessed stress wave data; and is used to perform feature calculation and storage on the preprocessed stress wave data to obtain stored stress wave data; and is used to perform fault detection and evaluation on the stored stress wave data to obtain stress wave data after fault detection and evaluation; and is used to judge the leakage information of the equipment based on the stress wave data after fault detection and evaluation, and feed back the leakage information of the equipment to the CPU processing unit, and output the warning type through the indicator light unit.

[0024] In one embodiment of the present invention, the method for performing feature calculation and storage on the preprocessed stress wave data to obtain stored stress wave data includes:

[0025] Performing Fourier transform on the pre-processed stress wave data to extract the time domain waveform features within the target frequency band, the extraction step comprising:

[0026] Fourier transform: convert the time domain signal x(t) to the frequency domain X(f),

[0027] Bandpass filter: In the frequency domain, the signal components are retained by setting the frequency range [f1, f2]. The components outside the frequency range, that is, those below f1 or above f2, are set to 0.

[0028] Inverse Fourier transform: restore the filtered frequency domain signal back to the time domain signal,

[0029] Decibel value calculation technology is used to quantitatively analyze the energy distribution of the waveform in a specific frequency range. The calculation steps include:

[0030] Calculate RMS value: Calculate the root mean square value of the waveform data. For a set of discrete voltage values V1, V2, ..., V n , the root mean square value calculation formula is as follows:

[0031]

[0032] Convert the RMS value to dB:

[0033] Among them, L dB Indicates the voltage level in decibels, V rms Represents the root mean square voltage, V ref Indicates the reference voltage;

[0034] The calculated dB value and its acquisition time are stored in a storage device, where the storage field includes the acquisition time and the decibel value.

[0035] In one embodiment of the present invention, performing fault detection and evaluation on the stored stress wave data to obtain stress wave data after fault detection and evaluation includes:

[0036] The stored stress wave data is preprocessed using the Savitzky-Golay filtering algorithm. The calculation formula is as follows:

[0037] Obtain a single smoothed sequence value: Use the least squares method to fit a low-order polynomial within a window, and then use the low-order polynomial to estimate the smoothed value of the data within the window. Assuming there is a set of discrete data y1, y2, ..., yn, and a sliding window size of 2m+1, then perform a smoothing operation on each data point yi:

[0038]

[0039] where a k is the filter coefficient, which is calculated by fitting a polynomial. The form of the fitting polynomial is

[0040] P(x)= a 0+a1x+...+a n x n ,

[0041] The fitting method uses the least squares method to fit the polynomial of 2m+1 data points and calculate the coefficients. The goal of the least squares method is to minimize the following objective function, that is, the sum of squared errors, to find the optimal parameters:

[0042]

[0043] Set the step size and size of the window, move the window to get the smoothed sequence, and obtain the entire smoothed sequence;

[0044] The stress wave data preprocessed by the Savitzky-Golay filter algorithm are analyzed in the following steps:

[0045] Data modeling: A first-order linear regression model is selected for the pre-processed stress wave data:

[0046] y=a0+a1x+ε,

[0047] Where x is the time variable, y is the actual decibel value, and ε is the error between the predicted value and the actual value. The model is obtained by estimating the fitting parameters through least squares.

[0048] Model prediction: After obtaining the model, multi-step prediction is performed. For the time series {(x i ,y i )} i＝1,...,n,... , make m-step predictions after the nth time node:

[0049]

[0050] get Observe the changes in the average residual of m data values to determine whether an abnormal step phenomenon occurs;

[0051] Set a preset threshold. When the average residual change exceeds the preset threshold, the system will automatically trigger an alarm mechanism to prompt the operator to perform inspection and maintenance.

[0052] As described above, the stress wave collection and transmission device of the present invention has the following beneficial effects:

[0053] (1) The stress wave acquisition and transmission device of the present invention integrates the acquisition, amplification and power supply functions into one, and adopts a network transmission method. It has the characteristics of high bandwidth and fast data transmission, and can realize multi-point synchronous transmission.

[0054] (2) The stress wave acquisition and transmission device of the present invention utilizes the historical data generated by the stored acquisition time and decibel value to perform efficient and accurate fault detection and evaluation in key application scenarios such as high-temperature and high-pressure pipeline leakage and motor anomalies.

[0055] (3) The stress wave acquisition and transmission device of the present invention integrates the function of interconnection between multiple sensors to achieve data sharing among multiple sensors and improve the accuracy of early warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 This is a principle block diagram of the stress wave acquisition and transmission device of the present invention;

[0057] Figure 2 This is a structural cross-sectional view of the stress wave collection and transmission device of the present invention;

[0058] Figure 3 This is a schematic diagram of the external structure of the stress wave collection and transmission device of the present invention.

[0059] Among them: 1-Acoustic emission sensor, 2-Charge amplifier, 3-Amplifier power isolator, 4-DC-DC 12V to 24V conversion unit, 5-DC-DC 12V to 5V conversion unit, 6-DC-DC 12V to 6V conversion unit, 7-Power module, 8-Ethernet chip unit, 9-AD signal acquisition and conversion unit, 10-CPU processing unit, 11-Network interface, 12-Indicator light board, 13-Casing, 14-Circuit board, 15-Front flange, 16-Rear flange, 17-Bakelite board, 18-Acrylic board, 19-SMA two-way connector, 20-Aviation plug. DETAILED DESCRIPTION

[0060] The following describes the embodiments of the present invention through specific examples. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments. The details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the following embodiments and features in the embodiments can be combined with each other unless they conflict.

[0061] It should be noted that the illustrations provided in the following embodiments are merely schematic illustrations of the basic concept of the present invention. Therefore, the illustrations only show components related to the present invention and are not drawn according to the number, shape, and size of components in actual implementation. In actual implementation, the type, quantity, and proportion of each component may be changed arbitrarily, and the component layout may also be more complex.

[0062] Terms such as first or second can be used to describe various components, but these components are not limited by the above terms. The above terms are used to distinguish one component from another component. For example, without departing from the scope of the concept according to the present disclosure, a first component can be referred to as a second component, and similarly, a second component can be referred to as a first component.

[0063] In addition, “connected / coupled” means that one component is directly electrically coupled to another component or indirectly electrically coupled through another component. As long as it is not explicitly stated in the sentence, the singular form may include the plural form. In addition, “include / comprise” or “include / include” used in this specification indicates that one or more components, steps, operations and elements exist or have been added. The specific structural or functional descriptions of the examples of the implementation of the concepts disclosed in this specification are merely exemplified to describe the examples of the implementation of the concepts, and the examples of the implementation of the concepts can be implemented in various forms, but these descriptions are not limited to the examples of the implementation described in this specification.

[0064] According to the concept, various modifications and changes can be applied to the examples of the embodiments, so that the examples of the embodiments will be illustrated in the drawings and described in the specification. However, the examples of the embodiments according to the concept are not limited to the specific embodiments, but include all changes, equivalents or replacements included in the spirit and technical scope of the present disclosure.

[0065] It should be understood that when an element is described as being "coupled" or "connected" to another element, the element can be directly coupled or directly connected to the other element, or can be coupled or connected to the other element through a third element. Conversely, it should be understood that when an element is referred to as being "directly coupled to" or "directly coupled to" another element, no other elements are interposed therebetween. Other expressions describing relationships between components (i.e., "between" and "directly between" or "adjacent to" and "directly adjacent to") need to be interpreted in the same manner.

[0066] The terms used in this specification are only used to describe specific examples of the embodiments and are not intended to limit the present disclosure. If there is no clear contrary meaning in the context, the singular form may include the plural form. In this specification, it should be understood that the term "including" or "having" indicates the presence of the features, quantities, steps, operations, components, parts or combinations thereof described in the specification, but cannot preclude the possibility of the presence or addition of one or more other features, quantities, steps, operations, components, parts or combinations thereof.

[0067] Unless otherwise defined, all terms used herein (including technical or scientific terms) have the same meaning as those generally understood by those skilled in the art. If terms defined in commonly used dictionaries are not clearly defined in this specification, they should be interpreted as having the same meaning as in the context of the relevant technology, and not as ideal or overly formal meanings.

[0068] Descriptions of well-known components and processing techniques may be omitted so as not to unnecessarily obscure the embodiments of the disclosure.

[0069] Throughout the specification, like reference numerals refer to like elements. Thus, even if a reference numeral is not mentioned or described with reference to one figure, it may be mentioned or described with reference to another figure. Furthermore, even if a reference numeral is not shown in one figure, it may be mentioned or described with reference to another figure.

[0070] In addition, the logic level of a signal may be different or opposite from the described logic level. For example, a signal described as having a logic "high" level may alternatively have a logic "low" level, and a signal described as having a logic "low" level may alternatively have a logic "high" level.

[0071] The following describes various embodiments of the present disclosure in detail with reference to the accompanying drawings. However, those skilled in the art will appreciate that many technical details are provided in the various embodiments of the present disclosure to facilitate a better understanding of the present disclosure. However, even without these technical details and the various variations and modifications based on the following embodiments, the technical solutions claimed in the present disclosure can still be implemented.

[0072] Example 1:

[0073] This embodiment provides a stress wave acquisition and transmission device that integrates acquisition, amplification, and power supply functions, and adopts a network transmission method. It has the characteristics of high bandwidth and fast data transmission, and can realize multi-point synchronous transmission.

[0074] like Figure 1 As shown, the stress wave acquisition and transmission device includes: an acoustic emission sensor 1, a charge amplifier 2, an amplifier power isolator 3, a voltage conversion unit, a power module 7, an Ethernet chip unit 8, an AD signal acquisition and conversion unit 9, a CPU processing unit 10 and a network interface 11;

[0075] The power module 7 is used to isolate the external voltage and convert it into 12V voltage to transmit directly to other internal units or further convert it through the voltage conversion unit and transmit it to other internal units for power supply. The purpose of using power isolation is to prevent problems with the device from causing damage to other devices.

[0076] As an example, power module 7 uses a POE (PoE) power module, which isolates the 48V voltage from an external POE switch and converts it into a 12V voltage to power other units within the transmitter. Power module 7 is connected to network interface 11 via a network isolation transformer, providing POE power and data transmission through network interface 11.

[0077] The AD signal acquisition and conversion unit 9 in the stress wave acquisition and transmission device requires a power supply voltage of 6V, the CPU processing unit 10 requires a power supply voltage of 5V, and the amplifier power isolator 3 requires a power supply voltage of 24V; based on this, the voltage conversion unit includes: a DC-DC 12V to 6V conversion unit 6 arranged between the power module 7 and the AD signal acquisition and conversion unit 9, a DC-DC 12V to 5V conversion unit 5 arranged between the power module 7 and the CPU processing unit, and a DC-DC 12V to 24V conversion unit 4 arranged between the power module 7 and the amplifier power isolator 3.

[0078] The acoustic emission sensor 1 is electrically connected to the charge signal amplifier 2 , and the acoustic emission sensor 1 transmits the monitored device sound wave frequency to the charge amplifier 2 in the form of charge.

[0079] As an example, the acoustic emission sensor 1 adopts a piezoelectric ceramic sensor, which has high sensitivity and can detect weak stress wave signals; this is very effective for detecting activities such as tiny cracks and internal defects of materials.

[0080] The charge amplifier 2 is used to amplify the received charge and output a voltage signal; as an example, the output signal of the charge amplifier 2 is a ±5V analog signal.

[0081] The charge amplifier 2 is electrically connected to the amplifier power isolator 3, which separates the charge amplifier 2 signal and transmits it to the AD signal acquisition and conversion unit 9. As a signal isolation device, the amplifier power isolator 3 is used to isolate the input, output, and operating power supply from each other. While transmitting the output signal of the charge amplifier 2, it also provides power to the acoustic emission sensor 1 and the charge signal amplifier 2.

[0082] The AD signal acquisition and conversion unit 9 acquires the signal output by the amplifier power isolator 3 at a set sampling rate (such as a 16-bit 5MHZ sampling rate) and converts it into a digital signal and sends it to the CPU processing unit 10 (using an FPGA core board).

[0083] As an example, the AD signal acquisition and conversion unit 9 and the CPU processing unit 10 adopt a parallel port data transmission mode.

[0084] The CPU processing unit 10 packages the digital signal data output by the AD signal acquisition and conversion unit 9 according to the set time interval and transmits it to the Ethernet chip unit 8, which converts it into network data and finally transmits it to the host computer through the network interface 11.

[0085] As an example, the network interface 11 adopts an RJ45 network interface, which provides a POE power supply and data transmission interface. The RJ45 network interface is connected to the host computer through a POE switch.

[0086] The above stress wave acquisition and transmission device integrates acquisition, amplification and power supply into one, adopts TCP / IP network protocol and RJ45 interface for data transmission, has the advantages of high transmission efficiency, and can make leakage judgment in time.

[0087] As an example, an indicator light unit 12 is also provided, which is connected to the CPU processing unit 10. The indicator light unit 12 is used to display the current operating status, including whether the network connection is normal, the leakage alarm and the ash blockage alarm status, etc. The leakage alarm and ash blockage alarm status are determined based on the host computer's relevant processing of the data output by the stress wave acquisition and transmission device (the host computer can calculate the sound spectrum trend when the boiler is operating abnormally based on the received data, thereby determining the leakage of the boiler equipment, etc.), and then feedback to the CPU processing unit 10. Then, the indicator light unit 12 is used to provide an on-site alarm indication, and an alarm signal can be quickly issued in the event of a fault.

[0088] The above-mentioned stress wave acquisition and transmission device is used for monitoring boiler noise, which can realize dynamic detection and real-time monitoring of boiler noise. Dynamic monitoring: Stress wave detection is a dynamic detection method. The energy detected comes from the object being measured itself, rather than provided by non-destructive testing instruments; this means that the stress wave sensor can capture the dynamic changes in the structure of the object being measured, such as the initiation and expansion of cracks, in real time. Real-time monitoring: Stress wave-based monitoring can monitor the state of the object being measured in real time, which is particularly important for online monitoring of industrial processes and early or imminent damage prediction. By continuously monitoring stress wave signals, potential safety hazards can be discovered and warned in a timely manner. At the same time, the stress wave acquisition and transmission device has high sensitivity and can detect weak stress wave signals. This is very effective for detecting activities such as tiny cracks and internal defects in materials.

[0089] Specifically, the host computer is electrically connected to the network interface of the stress wave acquisition and transmission device; the host computer is used to receive the stress wave data output by the stress wave acquisition and transmission device, and preprocess the stress wave data to obtain preprocessed stress wave data; and is used to perform feature calculation and storage on the preprocessed stress wave data to obtain stored stress wave data; and is used to perform fault detection and evaluation on the stored stress wave data to obtain stress wave data after fault detection and evaluation; and is used to judge the leakage information of the equipment based on the stress wave data after fault detection and evaluation, and feed back the leakage information of the equipment to the CPU processing unit, and output the warning type through the indicator light unit.

[0090] Example 2:

[0091] On the basis of the above-mentioned embodiment 1, a plurality of the above-mentioned stress wave acquisition and transmission devices can be used to work in coordination to improve the detection accuracy or locate the defect position.

[0092] When multiple sensors (i.e., the above-mentioned stress wave acquisition and transmission devices) work in coordination, the multiple stress wave acquisition and transmission devices are connected to the POE switch through the network interface 11 respectively, thereby realizing multi-point synchronous acquisition and transmission of stress wave data.

[0093] When used for defect location, stress wave sensors are arranged at different positions of the object to be measured (such as a boiler). The host computer can calculate the specific location of the defect by measuring the time difference and amplitude difference of the stress wave signals received by different sensors.

[0094] By placing multiple stress wave sensors at the same monitoring location and comprehensively analyzing the data from multiple sensors, the accuracy and reliability of detection can be improved, thereby providing a more comprehensive understanding of the state of the object being measured.

[0095] Specifically, the host computer is used to receive the stress wave data output by the stress wave acquisition transmitter, and preprocess the stress wave data to obtain preprocessed stress wave data.

[0096] Specifically, the collected stress wave data needs to be preprocessed to improve the accuracy of subsequent calculations. The preprocessing steps include:

[0097] Denoising: Use filters (such as low-pass, high-pass, and band-pass filters) to remove high-frequency noise.

[0098] Normalization: Normalize the signal to a standard range for uniform subsequent analysis.

[0099] Specifically, the host computer is used to perform feature calculation and storage on the preprocessed stress wave data to obtain stored stress wave data.

[0100] Specifically, performing feature calculation and storage on the preprocessed stress wave data to obtain the stored stress wave data includes:

[0101] Signal processing: Perform Fourier transform (FFT) on the stress wave signal to accurately extract the time domain waveform characteristics within the target frequency band. The extraction steps are as follows:

[0102] Fourier transform (FFT): Converts the time domain signal x(t) to the frequency domain X(f):

[0103]

[0104] Bandpass filter: In the frequency domain, the signal components are retained by setting the frequency range [f1, f2]. The components outside the frequency range (lower than f1 or higher than f2) are set to 0.

[0105] Inverse Fourier transform (IFFT): restore the filtered frequency domain signal back to the time domain signal:

[0106]

[0107] Decibel calculation: Utilize sophisticated decibel calculation technology to quantitatively analyze the energy distribution of the waveform in a specific frequency range. The calculation steps are as follows:

[0108] Calculate the RMS value: First, you need to calculate the root mean square (RMS) value of the waveform data. For a set of discrete voltage values V1, V2, ..., V n ,RMS value calculation formula is as follows:

[0109]

[0110] Convert RMS value to dB value: L dB Indicates the voltage level in decibels, V rms Represents the root mean square voltage, V ref represents the reference voltage (usually 1 volt, unless otherwise specified), and is calculated as follows:

[0111]

[0112] Decibel Value Storage: Calculated decibel values and their acquisition time are stored in a storage device. The storage fields include the acquisition time and decibel value. A circular storage strategy is used to ensure efficient use of device storage space and prevent data overflow.

[0113] Specifically, the host computer is used to perform fault detection and evaluation on the stored stress wave data to obtain stress wave data after fault detection and evaluation.

[0114] Specifically, using the historical data generated by the stored acquisition time and decibel values, we can perform efficient and accurate fault detection and assessment for key application scenarios such as high-temperature and high-pressure pipeline leaks and motor anomalies. The main steps are as follows:

[0115] Data preprocessing: Use the Savitzky-Golay filtering algorithm to preprocess the stored data. Savitzky-Golay filtering is a filtering method commonly used in signal processing and data smoothing. It smoothes data by fitting a local polynomial. The calculation formula is as follows:

[0116] Get a single smoothed sequence value: Within a window, use the least squares method to fit a low-order polynomial (usually a quadratic or cubic polynomial), and then use this polynomial to estimate the smoothed value of the data within the window. Assuming we have a set of discrete data y1, y2, ..., yn, and a sliding window size of 2m+1, then perform a smoothing operation on each data point yi:

[0117]

[0118] where a k are the filter coefficients, which are calculated by fitting a polynomial (usually a quadratic or cubic polynomial) in the form of:

[0119] P(x)=a0+a1x+...+a n x n ,

[0120] The fitting method uses the least squares method to fit the polynomial of 2m+1 data points and calculate the coefficients. The goal of the least squares method is to minimize the following objective function (i.e., the sum of squared errors) to find the optimal parameters:

[0121]

[0122] Get the entire smoothed sequence: set the step size and size of the window, and move the window to get the smoothed sequence;

[0123] Trend analysis: Use trend analysis technology to conduct in-depth research on preprocessed data. The main steps are as follows:

[0124] Data modeling: Select a first-order linear regression model for modeling based on the preprocessed data:

[0125] y=a0+a1x+ε,

[0126] Where x is the time variable, y is the actual decibel value, and ε is the error between the predicted value and the actual value. The model is obtained by estimating the fitting parameters through least squares.

[0127] Model prediction: After obtaining the model, multi-step prediction is performed. For the time series {(x i ,y i )} i＝1,...,n,... , make m-step predictions after the nth time node:

[0128]

[0129] get Observe the changes in the average residual of m data values to determine whether an abnormal step phenomenon occurs;

[0130] Threshold setting: To accurately detect faults, we need to set a reasonable preset threshold based on the characteristics of the actual application scenario. When the residual change exceeds the preset threshold, the system will automatically trigger an alarm mechanism to prompt the operator to perform inspection and maintenance.

[0131] Fault Assessment: After discovering abnormal vibration, we need to further evaluate the fault. By comparing decibel level trends in historical data, combined with the characteristics of the actual application scenario and the operating status of the equipment, we can make a preliminary assessment of the fault's severity, possible causes, and repair solutions.

[0132] Specifically, the host computer is used to determine the leakage information of the equipment based on the stress wave data after the fault detection and evaluation, and feed back the leakage information of the equipment to the CPU processing unit, and output the warning type through the indicator light unit.

[0133] The warning type can be displayed by the light color of the indicator unit, as follows:

[0134] Blue: Normal status, no action required.

[0135] Blue: Potential problem, check.

[0136] Red: Serious problem, requires urgent attention.

[0137] The stress wave acquisition and transmission device of the present invention integrates the function of interconnection between multiple stress wave acquisition and transmission devices to realize data sharing between multiple stress wave acquisition and transmission devices and improve the accuracy of early warning. The following is a detailed design scheme.

[0138] Stress wave acquisition and transmission device deployment: Multiple stress wave acquisition and transmission devices are deployed in key monitoring areas to ensure that comprehensive vibration information of the equipment can be captured.

[0139] Interconnection of stress wave acquisition and transmission devices: Connect the stress wave acquisition and transmission devices to the switch via wired means to achieve data intercommunication between the stress wave acquisition and transmission devices.

[0140] Switch selection: Choose a high-performance switch as the core device for data communication to ensure efficient and stable data transmission.

[0141] Switch configuration: Configure the switch's ports, VLAN (virtual local area network), routing and other functions to meet the data communication requirements between different stress wave acquisition transmitters.

[0142] Switch management: Configure and manage the switch through its management interface or command line interface to ensure normal network operation and data security.

[0143] Data storage: Each stress wave acquisition transmitter has a data storage function. After feature extraction of the collected data, the data is stored in real time on the local server.

[0144] Data interaction: Mutual access to historical data between different stress wave acquisition and transmission devices is achieved through switches.

[0145] Data analysis: Use fault detection and assessment methods to comprehensively analyze and process historical data collected by different stress wave acquisition and transmission devices to improve the accuracy of early warning.

[0146] Example 3:

[0147] On the basis of the above-mentioned embodiment 1 or embodiment 2, considering the special use environment of boiler noise monitoring, the acquisition device is required to have dust-proof, corrosion-proof and high-temperature-resistant properties. This embodiment provides a specific structure of the stress wave acquisition transmitter.

[0148] like Figure 2 As shown, the stress wave acquisition transmitter includes: a housing 13 and a charge amplifier 2, an amplifier power isolator 3 and a circuit board 14 sealed inside the housing 13;

[0149] The shell 13 is a hollow structure with openings at both ends, and the openings at both ends are respectively closed by a front flange 15 and a rear flange 16 ; and sealing gaskets are provided between the shell 13 and the front flange 15 and the rear flange 16 to ensure sealing performance.

[0150] The interior of the housing 13 is divided into an upper space and a lower space. A circuit board 14 is disposed in the upper space. The voltage conversion unit, power module 7, Ethernet chip unit 8, AD signal acquisition and conversion unit 9, and CPU processing unit 10 are all disposed on the circuit board 14. The charge amplifier 2 and amplifier power isolator 3 are disposed in the lower space within the housing 13. A support plate is disposed within the lower space within the housing 13. The charge amplifier 2 and amplifier power isolator 3 are each mounted on the support plate via fasteners. As an example, a stacked bakelite board 17 and an acrylic board 18 are used as the support plate.

[0151] One end of the charge amplifier 2 is connected to the acoustic emission sensor 1 mounted on the rear flange 16 , and the other end is connected to the amplifier power isolator 3 via an SMA two-way connector 19 . The amplifier power isolator 3 is electrically connected to the circuit board 14 .

[0152] like Figure 3 As shown, an aviation plug 20 is provided on the housing 13 as the network interface 11 for electrically connecting to an external POE switch.

[0153] When the indicator light unit 12 is provided, the indicator light unit 12 is mounted on the front flange 15 , and the indicator light unit 12 is electrically connected to the circuit board 14 .

[0154] In summary, the stress wave acquisition and transmission device of the present invention integrates acquisition, amplification, and power supply functions, and utilizes network transmission, offering high bandwidth, fast data transmission, and multi-point simultaneous transmission. By utilizing historical data generated from stored acquisition times and decibel values, the present invention enables efficient and accurate fault detection and assessment in critical application scenarios such as high-temperature and high-pressure pipeline leaks and motor anomalies.

[0155] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the present invention. Anyone skilled in the art may modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by one of ordinary skill in the art without departing from the spirit and technical principles disclosed herein are intended to be covered by the claims of the present invention.

Claims

1. A stress wave collection and transmission device, characterized in that: include: Integrated acoustic emission sensor, charge amplifier, amplifier power isolator, voltage conversion unit, power module, Ethernet chip unit, AD signal acquisition and conversion unit, CPU processing unit and network interface; The acoustic emission sensor is electrically connected to the charge signal amplifier to transmit the monitored device sound wave frequency to the charge amplifier in the form of charge; The charge amplifier is used to amplify the received charge and output a voltage signal; The charge amplifier is electrically connected to the amplifier power isolator, which separates the output signal of the charge amplifier and transmits it to the AD signal acquisition and conversion unit; at the same time, the amplifier power isolator is also used to power the acoustic emission sensor and the charge signal amplifier; The AD signal acquisition and conversion unit acquires the signal output by the amplifier power supply isolator at a set sampling rate and converts it into a digital signal and sends it to the CPU processing unit; The CPU processing unit is connected to the network interface, and the CPU processing unit packages the digital signal data output by the AD signal acquisition and conversion unit according to the set time interval and transmits it to the Ethernet chip unit, and converts it into network data through the Ethernet chip unit; The network interface provides POE power supply and data transmission interface; the power module is connected to the network interface through a network isolation transformer; the voltage provided by the power module is converted by the voltage conversion unit and provided to the AD signal acquisition and conversion unit, the CPU processing unit and the amplifier power isolator respectively.

2. The stress wave collection and transmission device according to claim 1, characterized in that: The acoustic emission sensor adopts a piezoelectric ceramic sensor.

3. The stress wave collection and transmission device according to claim 1, characterized in that: It also includes an indicator light unit; the indicator light unit is connected to the CPU processing unit; The indicator light unit is used to display the current working status, including whether the network connection is normal, leakage alarm and dust blockage alarm status.

4. The stress wave acquisition and transmission device according to any one of claims 1 to 3, characterized in that: Stress wave acquisition and transmission devices are arranged at different positions of the object under test, and multiple stress wave acquisition and transmission devices are connected to the POE switch through the network interface respectively to realize multi-point synchronous acquisition and transmission of stress wave data.

5. The stress wave collection and transmission device according to any one of claims 1 to 3, characterized in that: Arrange multiple stress wave collection and transmission devices at the same monitoring position of the object under test.

6. The stress wave collection and transmission device according to any one of claims 1 to 3, characterized in that: It includes a housing and a charge amplifier, an amplifier power isolator and a circuit board sealed inside the housing; The voltage conversion unit, power supply module, Ethernet chip unit, AD signal acquisition and conversion unit and CPU processing unit are integrated on the circuit board; The charge amplifier and the amplifier power isolator are electrically connected and mounted on a support plate inside the housing; The housing is provided with an aviation plug as a network interface.

7. The stress wave collection and transmission device according to claim 6, characterized in that: The interior of the shell is divided into an upper space and a lower space. The circuit board is arranged in the upper space; the charge amplifier and the amplifier power isolator are arranged in the lower space.

8. The stress wave collection and transmission device according to claim 1, characterized in that: Also includes: A host computer electrically connected to the network interface of the stress wave acquisition and transmission device; the host computer is used to receive the stress wave data output by the stress wave acquisition and transmission device, and preprocess the stress wave data to obtain preprocessed stress wave data; and is used to perform feature calculation and storage on the preprocessed stress wave data to obtain stored stress wave data; It is used to perform fault detection and evaluation on the stored stress wave data to obtain stress wave data after fault detection and evaluation; and is used to judge the leakage information of the equipment based on the stress wave data after fault detection and evaluation, and feed back the leakage information of the equipment to the CPU processing unit, and output the warning type through the indicator light unit.

9. The stress wave collection and transmission device according to claim 8, characterized in that: The method for performing feature calculation and storage on the pre-processed stress wave data to obtain stored stress wave data includes: Performing Fourier transform on the pre-processed stress wave data to extract the time domain waveform features within the target frequency band, the extraction step comprising: Fourier transform: convert the time domain signal x(t) to the frequency domain X(f), Bandpass filter: In the frequency domain, the signal components are retained by setting the frequency range [f1, f2]. The components outside the frequency range, that is, those below f1 or above f2, are set to 0. Inverse Fourier transform: restore the filtered frequency domain signal back to the time domain signal, Decibel value calculation technology is used to quantitatively analyze the energy distribution of the waveform in a specific frequency range. The calculation steps include: Calculate RMS value: Calculate the root mean square value of the waveform data. For a set of discrete voltage values V1, V2, ..., V n , the root mean square value calculation formula is as follows: Convert the RMS value to dB: Among them, L dB Indicates the voltage level in decibels, V rms Represents the root mean square voltage, V ref Indicates the reference voltage; The calculated dB value and its acquisition time are stored in a storage device, where the storage field includes the acquisition time and the decibel value.

10. The stress wave collection and transmission device according to claim 9, characterized in that: The method for performing fault detection and evaluation on the stored stress wave data to obtain stress wave data after fault detection and evaluation includes: The stored stress wave data is preprocessed using the Savitzky-Golay filtering algorithm. The calculation formula is as follows: Obtain a single smoothed sequence value: Use the least squares method to fit a low-order polynomial within a window, and then use the low-order polynomial to estimate the smoothed value of the data within the window. Assuming there is a set of discrete data y1, y2, ..., yn, and a sliding window size of 2m+1, then perform a smoothing operation on each data point yi: where a k The filter coefficient is calculated by fitting a polynomial. The form of the fitting polynomial is P(x)=a0+a1x+...+a n x n , The fitting method uses the least squares method to fit the polynomial of 2m+1 data points and calculate the coefficients. The goal of the least squares method is to minimize the following objective function, that is, the sum of squared errors, to find the optimal parameters: Set the step size and size of the window, move the window to get the smoothed sequence, and obtain the entire smoothed sequence; The stress wave data preprocessed by the Savitzky-Golay filter algorithm are analyzed in the following steps: Data modeling: A first-order linear regression model is selected for the pre-processed stress wave data: y=a0+a1x+ε, Where x is the time variable, y is the actual decibel value, and ε is the error between the predicted value and the actual value. The model is obtained by estimating the fitting parameters through least squares. Model prediction: After obtaining the model, multi-step prediction is performed. For the time series {(x i ,y i )} i＝1,...,n,... , make m-step predictions after the nth time node: get Observe the change in the average residual of m data values to determine whether an abnormal step phenomenon occurs; Set a preset threshold. When the average residual change exceeds the preset threshold, the system will automatically trigger an alarm mechanism to prompt the operator to perform inspection and maintenance.

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