Bag breaking detection device and method based on HHT resonance peak-cross correlation coefficient

Abstract

The application discloses a kind of based on HHT resonance peak-mutual correlation coefficient's bag breaking detection device and method.The present application is mainly by a Sagnac optical fiber loop and an embedded MZI constitute.Sagnac optical fiber loop is used to position the position of pulse dust cleaning type bag filter broken filter bag;Embedded MZI utilizes its sensitivity to vibration signal, for detecting the airflow signal in the pulse dust cleaning type bag filter filter bag;Photoelectric detection module is used to convert the coherent light signal returned by sensing optical fiber into electrical signal;Dual-channel data acquisition module is used for digital-to-analog conversion to the measured electrical signal collected;Signal processing and analysis module, broken filter bag airflow vibration signal is demodulated, collected and stored, and signal sample is extracted in real time online feature, broken filter bag identification and positioning.The present application has the characteristics of low cost, simple structure, high reliability, high sensitivity, detection and identification accuracy etc..

Description

Technical Field

[0001] This invention relates to the field of optical fiber technology applications, and in particular to a bag-breaking detection device and method based on HHT resonance peak-cross-correlation coefficient. Background Technology

[0002] my country has a large number of cement plants, smelters, coal-fired power plants, and other similar facilities. These industries generate a large amount of industrial exhaust gas containing dust particles during their production processes. If this exhaust gas is directly released into the atmosphere, it will cause serious air pollution. Therefore, it is necessary to treat it with dust removal and filtration.

[0003] A common method for dust removal and filtration is installing baghouse dust collectors. Their main function is to filter exhaust gas containing dust particles using filter bags, achieving clean emissions. When industrial exhaust gas containing dust particles enters the baghouse dust collector, the filter bags, made of semi-permeable material, allow air to pass through smoothly, while dust particles, due to their large size, cannot pass through. This achieves clean filtration of the dust-laden exhaust gas. However, after a period of use, filter bags will become damaged. A commercial baghouse dust collector often contains thousands of filter bags. If a filter bag is damaged and not detected in time, the lifespan of other filter bags will be rapidly shortened, leading to a collapse and resulting in severe atmospheric dust pollution and serious damage to the dust removal equipment, forcing the company to shut down.

[0004] Currently, the industry commonly uses fluorescent-assisted marking to allow workers to manually inspect inside dust collectors to locate damaged filter bags. However, this method is time-consuming, costly, and inefficient, failing to balance environmental protection requirements with economic benefits, and it cannot achieve real-time monitoring and location of filter bag damage.

[0005] To address this challenge, Chinese patent CN107024327A discloses an intelligent leak detection device and method for baghouse dust collectors. This device employs... Distributed sensing systems can detect and locate damaged filter bags in baghouse dust collectors in real time. However, user feedback indicates that these systems have high technical requirements for device specifications, complex structures, and high costs. Furthermore, their detection methods may not be suitable for pulse-jet baghouse dust collectors. In pulse-jet baghouse dust collectors, during operation, a high-pressure pulse airflow jet device directly above each filter bag periodically sprays high-pressure pulse airflow in the opposite direction to remove dust particles adhering to the outer surface of the filter bag. However, the Rayleigh backscatter signal is very weak, and the high-pressure pulse airflow easily masks the useful Rayleigh backscatter signal, making signal processing complex. Therefore, the invention's ability to detect and identify damaged filter bags in pulse-jet baghouse dust collectors requires further validation. Summary of the Invention

[0006] The purpose of this invention is to provide a method for detecting bag breakage in a distributed optical fiber bag filter based on the HHT resonance peak-cross-correlation coefficient, in order to solve the problems currently used... The distributed sensing technology solution has shortcomings, such as high device cost and complex signal processing methods, especially in solving the problem of real-time detection, identification and location of damaged filter bags in pulse cleaning bag filters widely used in the dust removal and environmental protection industry.

[0007] In a first aspect, the present invention provides a distributed fiber optic bag filter dust collector bag breakage detection device, comprising a laser, a fiber optic coupler, a photoelectric detection module, a dual-channel data acquisition module, and a signal processing and analysis module;

[0008] The light emitted from the laser source is split into two beams of equal power after passing through the first fiber coupler, wherein:

[0009] A beam of light enters the MZI through the second fiber coupler and propagates in a clockwise direction, forming an interference signal at the fourth fiber coupler. This interference signal is then output to the second photodetector module through the fourth fiber coupler, the guide fiber, and the third fiber coupler.

[0010] Another beam of light is transmitted counterclockwise along the guide fiber through the third fiber coupler, enters the MZI through the fourth fiber coupler, and forms an interference signal at the second fiber coupler. The interference signal is output to the first photodetector module through the second fiber coupler.

[0011] The first photodetector module and the second photodetector module are connected to the signal processing and analysis module through a dual-channel data acquisition module, and the signal processing and analysis module obtains the bag breaking information.

[0012] The MZI is used to detect the airflow signal inside the filter bag of a pulse-jet baghouse dust collector.

[0013] A second aspect of the present invention provides a method for detecting bag breakage in a distributed optical fiber bag filter, comprising the following steps:

[0014] Step 1: After preprocessing the acquired raw digital signal, divide it into segments according to a certain time interval to form a data sample set;

[0015] Step 2: Adaptively decompose each sample in the data sample set using the EMD empirical mode decomposition algorithm to obtain EMD components;

[0016] Step 3: Extract the EMD components IMF1 and IMF2, and calculate the cross-correlation coefficients P1 and P2 between each component and the original sample;

[0017] Step 4: Calculate the HHT resonance peak of the EMD component IMF2 to obtain the resonance peak F2 of this component;

[0018] Step 5: Combine P1, P2, and F2 to form a three-dimensional feature parameter of a signal sample;

[0019] The three-dimensional feature parameters of each signal sample are combined to form a feature parameter set of the filter bag optical fiber vibration signal;

[0020] Step 6: Import the feature parameter set into the BP neural network for detection and recognition. If the filter bag is determined to be undamaged, continue real-time detection; if the filter bag is determined to be damaged, locate the damaged filter bag through the Sagnac fiber optic ring.

[0021] A third aspect of the present invention provides a distributed fiber optic bag filter bag breakage detection device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the above-described distributed fiber optic bag filter bag breakage detection method.

[0022] In a fourth aspect, the present invention provides a computer-readable storage medium storing a computer program for executing the above-described method for detecting bag breakage in a distributed fiber optic bag filter.

[0023] The device and identification and positioning method disclosed in this invention can perform real-time online positioning of damaged filter bags in baghouse dust collectors. Compared with the prior art, the device has the characteristics of low cost, simple structure, high reliability, high sensitivity, and high detection and identification accuracy. Moreover, its identification and positioning method is universal and can be used not only for pulse cleaning baghouse dust collectors but also for general baghouse dust collectors. Attached Figure Description

[0024] Figure 1 This is a schematic diagram of the device structure of the present invention.

[0025] Figure 2 This is a structural diagram of the filter bag of a pulse-jet baghouse dust collector used in the testing of the device of this invention.

[0026] Figure 3 This is a flowchart of the method for detecting, identifying, and locating damaged filter bags in a pulse-jet baghouse dust collector using the device of the present invention.

[0027] Figure 4 This is a topology diagram of the BP neural network for damaged filter bags in a pulse-jet baghouse dust collector, as described in the present invention.

[0028] Figure 5 This is a comparison diagram of the feature differences between damaged and good filter bags in the bag filter when the feature extraction algorithm of the present invention is used in the pulseless dust removal working mode of the device of the present invention.

[0029] Figure 6 This is a comparison diagram of the feature differences between damaged and good filter bags in the bag filter when the feature extraction algorithm of the present invention is used in the pulse cleaning mode of the device of the present invention. Detailed Implementation

[0030] The technical solutions in the embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0031] like Figure 1 As shown in the figure, this application discloses a distributed optical fiber bag filter rupture detection device based on HHT resonance peak-cross-correlation coefficient, including: a laser; optical fiber couplers 1, 2, 3, and 4; an unarmored single-mode vibrating optical fiber; an optical fiber interface scanning module; photoelectric detection modules 1 and 2; a dual-channel data acquisition module; and a signal processing and analysis module, which mainly consists of a Sagnac optical fiber loop and an embedded Mach-Zehnder interferometer (MZI). The MZI, utilizing its sensitivity to vibration signals, is used to detect the airflow signal inside the filter bag of a pulse-jet bag filter. The Sagnac optical fiber loop is used to locate the damaged filter bag.

[0032] In a preferred embodiment, the narrow-linewidth laser has a linewidth <50kHz and an operating wavelength of 1550nm, serving as the optical signal source for the transmitting device; the fiber optic coupler 1 primarily splits the optical signal into equal-power beams at a 1:1 ratio; two photodetectors are used to convert the coherent optical signal returned from the sensing fiber into an electrical signal; a dual-channel data acquisition module is used to synchronously convert the two electrical signals into analog-to-digital signals; and a data processing and analysis module is used to demodulate, acquire, and store the airflow vibration signal of the damaged filter bag, and to perform real-time online feature extraction, damaged filter bag identification, and location of the signal samples.

[0033] The basic principle of this device for detection and positioning is as follows: The laser operates at a wavelength of 1550nm and has a linewidth of 50kHz. The light emitted from the laser source is split into two beams of equal power at a 1:1 ratio after passing through fiber coupler 1. One beam enters the MZI (Mechanical Zone Injection) through fiber coupler 2 and propagates clockwise, forming an interference signal at fiber coupler 4. This interference signal passes through fiber coupler 4, the guide fiber, and fiber coupler 3 before being output to photodetector module 2. The other beam propagates counterclockwise along the guide fiber through fiber coupler 3, enters the MZI through fiber coupler 4, and forms an interference signal at fiber coupler 2. This interference signal is then output to photodetector module 1 through fiber coupler 2.

[0034] When point P on the sensing fiber vibrates, the phase of the interference light in the MZI changes, denoted as . Vibration information can be obtained by demodulating the phase information. Furthermore, since the two beams of light propagating clockwise and counterclockwise travel different distances from point P to photodetector modules 1 and 2, there is a time difference Δt between the signals received by the two detectors. The vibration position x can be calculated using the following formula:

[0035]

[0036] In the above formula, L is the length of the signal arm, L1 is the length of the guide fiber, and υ is the speed of light propagation in the fiber.

[0037] In one embodiment, the MZI signal arm can be folded in sections according to the number of filter bags in the array, fixed, and then suspended at the center of the filter bags or attached to the inner surface of each filter bag in the baghouse dust collector. Multiple signal arm optical fibers can be arranged in parallel inside the filter bags according to the column sequence of the filter bag array. During operation, the device continuously scans the filter bags on each fiber optic signal arm through the fiber optic port scanning module; alternatively, a single signal arm optical fiber can be used to connect the filter bags in series. The filter bag is 3500mm long and 130mm wide. Figure 2 As shown.

[0038] This application also discloses a method for detecting, identifying, and locating damaged filter bags, employing the resonant peak method: the complex airflow signal acting on the sensing optical fiber is considered as several basic component signals. These basic signals act on the sensing optical fiber in a linear superposition manner, treating a nonlinear problem as a linear one. It is assumed that each basic component signal resonates with the sensing optical fiber when acting on it, resulting in a resonant frequency. The differences in resonant frequencies between good and damaged bags are then analyzed.

[0039] like Figure 3 As shown, the specific steps of this method are as follows:

[0040] Step 1: Arrange the above device in the pulse cleaning bag filter, and input the raw digital signal of the airflow and fiber optic vibration inside the filter bag collected by the data acquisition card into the data processing and analysis module.

[0041] Step 2: After preprocessing the acquired raw digital signal, divide it into segments according to a certain time interval to form a data sample set. Each sample signal is denoted as x(Tn). Each time interval can only contain one dust removal process.

[0042] Step 3: Adaptively decompose each sample in the data set using the EMD empirical mode decomposition algorithm to obtain the EMD components. The decomposition formula is as follows:

[0043]

[0044] The components of EMD are denoted as c. i (Tn), the residual term is denoted as c. m (Tn);

[0045] Step 4: Extract the EMD components IMF1 and IMF2, and calculate the cross-correlation coefficients P1 and P2 between each component and the original sample. The formula for calculating the Pearson cross-correlation coefficient is:

[0046]

[0047] In the formula x n and y n These represent the two variables that are cross-correlated. and Let represent the arithmetic mean of the two variables, and N represent the length of the signal sampling points.

[0048] Step 5: Calculate the HHT resonance peak of the EMD component IMF2 to obtain the resonance peak F2 of this component. This involves performing a Hilbert transform on component IMF2 to obtain its instantaneous frequency and amplitude. The resonance peak is then selected through a selection process. The calculation formula is as follows:

[0049]

[0050] Where ω(t) is the instantaneous frequency, a(t) is the instantaneous amplitude, and t is the duration of each EMD component.

[0051] The resonance peak F2 obtained by the above process can better characterize the difference between good bags and broken bags.

[0052] Step 6: Combine the three parameters P1, P2 and F2 to form a three-dimensional feature parameter of a signal sample; combine the three-dimensional feature parameters of each signal sample to form a feature parameter set of the filter bag fiber vibration signal.

[0053] Step 7: Import the feature parameter set into the BP neural network for detection and recognition. The BP neural network recognition structure is as follows: Figure 4 As shown. If the filter bag is determined to be undamaged, real-time monitoring continues. If the filter bag is determined to be damaged, the damaged filter bag is located using a Sagnac fiber optic ring, the system alarms, and the location of the damaged filter bag is given. There is a time difference Δt between the signals received by the two detectors (the distances from point P to photodetector modules 1 and 2 are different for the two beams of light propagating clockwise and counterclockwise). The vibration position x can be calculated using the following formula to achieve positioning:

[0054]

[0055] In the above formula, L is the length of the signal arm, L1 is the length of the guide fiber, and υ is the speed of light propagation in the fiber.

[0056] This application also discloses a distributed fiber optic bag filter bag breakage detection device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the above-mentioned distributed fiber optic bag filter bag breakage detection method.

[0057] This application also discloses a computer-readable storage medium storing a computer program for executing the above-described distributed fiber optic bag filter bag breakage detection method.

[0058] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory.

[0059] The processor in this application may include one or more processing cores. The processor executes instructions, programs, code sets, or instruction sets stored in memory, and calls data stored in memory to perform various functions and process data. It is understood that, for different devices, the electronic devices used to implement the above-described processor functions may also be other, and the embodiments of this application do not specifically limit them.

[0060] After implementing the above technical solution, the results obtained in this application are as follows: When the bag filter is in non-pulse cleaning mode, 16 good bag characteristic samples and 17 broken bag characteristic samples are extracted. The vibration signal characteristics of the obtained good bags and broken filter bags are as follows: Figure 5 As shown. When the bag filter is in pulse cleaning mode, 16 samples of good bag characteristics and 16 samples of broken bag characteristics are extracted. The vibration signal characteristics of the obtained good bags and broken filter bags are as follows. Figure 6 As shown, the device and detection method disclosed in this invention can effectively distinguish the characteristic differences between good bags and damaged filter bags in two different working modes of a bag filter, and more effectively detect and identify damaged filter bags. Five samples of good bags and five samples of damaged bags were randomly extracted as training samples. After mixing the remaining feature sample sets, and performing five recognitions using a BP neural network, the average recognition rate of damaged bags for non-pulse cleaning dust collectors reached 90.5%, and the average recognition rate for damaged bags for pulse cleaning dust collectors reached 91.8%, with a positioning accuracy of <20m.

[0061] In summary, the device of this invention has a simple structure, relatively low component requirements, and low cost. The damaged filter bag detection and identification method proposed based on this device can effectively distinguish the characteristic differences between damaged and good filter bags in pulse-jet baghouse dust collectors, and can effectively detect, identify, and locate damaged filter bags in pulse-jet baghouse dust collectors in real time. This device and detection method can be widely applied not only in general baghouse dust collector industries but also in related dust collection industries using pulse-jet baghouse dust collectors.

[0062] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A distributed optical fiber baghouse dust collector bag breakage detection method, the device used includes a laser, an optical fiber coupler, a photoelectric detection module, a dual-channel data acquisition module, a signal processing and analysis module; the light emitted by the laser passes through the first optical fiber coupler and is divided into two beams of equal power, wherein: one beam of light passes through the second optical fiber coupler into the MZI and is transmitted in the clockwise direction, forming an interference signal at the fourth optical fiber coupler, the interference signal is output to the second photoelectric detector module through the fourth optical fiber coupler, the guide optical fiber and the third optical fiber coupler; the other beam of light passes through the third optical fiber coupler and is transmitted in the counterclockwise direction along the guide optical fiber, enters the MZI through the fourth optical fiber coupler, and forms an interference signal at the second optical fiber coupler, the interference signal is output to the first photoelectric detector module through the second optical fiber coupler; the first photoelectric detector module and the second photoelectric detector module are connected with the signal processing and analysis module through the dual-channel data acquisition module, and the bag breakage information is obtained by the signal processing and analysis module; the MZI is used for detecting the airflow signal in the pulse cleaning type baghouse dust collector filter bag; characterized in that: step 1: after preprocessing the collected original digital signal, segmenting it at certain time intervals to form a data sample set; step 2: using the EMD empirical mode decomposition algorithm to adaptively decompose each sample in the data sample set to obtain EMD components; step 3: extracting EMD components IMF1 and IMF2, and calculating the cross-correlation coefficients P1 and P2 between the two components and the original sample; step 4: calculating the HHT resonance peak of the EMD component IMF2 to obtain the resonance peak F2 of the component; step 5: combining P1, P2 and F2 to form a three-dimensional feature parameter of a signal sample; combining the three-dimensional feature parameters of each signal sample to form a filter bag optical fiber vibration signal feature parameter set; step 6: importing the feature parameter set into the BP neural recognition network for detection and recognition, if the filter bag is determined to be undamaged, real-time detection continues, if the filter bag is determined to be damaged, the Sagnac optical fiber ring is used to locate the damaged filter bag. Each time interval in step 1 can only contain one cleaning process. The signal arms of the MZI use multiple signal arm optical fibers in a parallel manner, arranged inside the filter bag according to the column order of the filter bag array, and the optical fiber interface scanning switch is used to continuously scan each optical fiber link; or, Each filter bag is connected in series by using one signal arm optical fiber in a series manner. The optical fiber used for sensing and transmission uses an unarmored single-mode vibration optical fiber, including a signal arm, a reference arm and a guide optical fiber. The laser source is a narrow linewidth laser with a linewidth < 50 KHz and a working wavelength of 1550 nm. It comprises: a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the distributed optical fiber baghouse dust collector bag breakage detection method of claims 1-5 when executing the program. The storage medium stores a computer program for executing the distributed optical fiber baghouse dust collector bag breakage detection method of claims 1-5. ​ ​ ​ ​ ​ 2. The distributed bag failure detection method for a fiber optic baghouse according to claim 1, wherein: ​ 3. The distributed bag failure detection method for a fiber optic baghouse according to claim 1, wherein: ​ ​ 4. The distributed bag failure detection method for a fiber optic baghouse according to claim 1, wherein: ​ 5. The distributed bag failure detection method for a fiber optic baghouse according to claim 1, wherein: ​ 6. A distributed optical baghouse bag break detection apparatus, characterized by, ​ ​ 7. A computer readable storage medium characterized in that, ​

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