Abnormal vibration monitoring method for aircraft

Abstract

The invention belongs to the technical field of vibration testing of structural components, particularly relates to an abnormal vibration monitoring method for an aircraft, and solves the technical problems that an existing method is low in sampling precision and cannot realize real-time online monitoring. The sensor signal acquisition system comprises a vibration measurement module, a multi-channel switching matrix, a noise generator, an ADC sampling chip, an FPGA controller, a USB controller, an eMMC memory, an RS422 transceiver and a power management and power distribution unit. The FPGA controller compares sensor data with a set threshold value, if the sensor data exceeds the threshold value, it is considered that an abnormal vibration event occurs at the corresponding position, at the moment, data 500 ms before and after vibration is stored in the eMMC memory, and the FPGA controller analyzes the abnormal vibration data, calculates the position of a vibration source, frames the position of the vibration source and sends the frame to an upper computer.

Description

Technical Field

[0001] The present invention relates to the technical field of vibration testing of structural components, and particularly to an abnormal vibration monitoring method for an aircraft. Background Art

[0002] During the flight of an aircraft, the monitoring of abnormal vibration is one of the key research contents of aircraft condition monitoring. Vibration signals are a direct reflection of the structural health status of the aircraft. By analyzing vibration signals, potential faults and abnormalities can be effectively identified. During normal flight, the vibration acceleration on the aircraft is generally between 0.1g and 2g. Exceeding this range, especially vibration acceleration exceeding 2g, can be considered an abnormal vibration event. By installing vibration sensors and vibration monitoring systems, the vibration level can be monitored in real time, helping to detect potential structural problems of the aircraft, ensuring flight safety, and also providing data support for the subsequent design and optimization of the aircraft, thus greatly improving the safety and reliability of the aircraft.

[0003] Domestic corresponding monitoring methods for aircraft abnormal vibration monitoring have been proposed: Patent 202311027173.8 proposes a distributed multi-parameter test system applicable to aircraft. This system uses various types of sensing devices to form an array and uses a single-chip ADC to collect and analyze the array signals. This sampling method can realize the sampling of multi-channel signals on one ADC, saving a large amount of board-level space and cost. However, due to the existence of channel switch switching delay, the system needs to use a high-sampling-rate ADC to reduce the waiting time in the T / H (sample / hold) stage. However, for an ADC, there is an inherent contradiction between high sampling rate and high sampling resolution. The sampling resolution of common high-speed ADCs on the market is slightly insufficient for the sensor data that requires precise sampling, seriously affecting the recording accuracy of the sensor sampling system and also bringing limitations to the subsequent data processing and analysis.

[0004] "Han Ze. Design and Implementation of an Active Structural Health Monitoring System Based on Ultrasonic Guided Waves [D]. Shandong University, 2023" and "Guo Fangyu. Research on Guided Wave Monitoring Method for Aircraft Structure Corrosion [D]. Nanjing University of Aeronautics and Astronautics, 2018" proposed two structural health monitoring systems applicable to equipment such as aircraft. Both of them adopt the active monitoring method. The basic principle is to use a piezoelectric guided wave active excitation source to apply excitation to the structural member, and the sensor array receives the signal. By analyzing the signal, potential damage in the structural member is detected. However, this method requires a large amount of data and models to calibrate and optimize the algorithm, and the computational complexity is extremely large. It is only suitable for ground maintenance and cannot achieve real-time monitoring. In addition, the structural health monitoring system only targets after-event maintenance and lacks effective recording means for abnormal events occurring during flight. For some abnormal vibrations occurring during flight, neither the cause can be known nor can they be recorded and analyzed, and real-time online monitoring cannot be achieved. Summary of the Invention

[0005] To overcome the technical defects of the existing method with low sampling accuracy and inability to achieve real-time online monitoring, the present invention provides an abnormal vibration monitoring method for aircraft.

[0006] The present invention provides an abnormal vibration monitoring method for an aircraft, including building a sensor signal acquisition system. The sensor signal acquisition system includes a vibration measurement module, a multi-channel switching matrix, a noise generator, an ADC sampling chip, an FPGA controller, a USB controller, an eMMC memory, an RS422 transceiver, and a power management and power distribution unit. The power management and power distribution unit supplies power to the entire sensor signal acquisition system. The vibration measurement module includes multiple normal-temperature single-axis acceleration sensors, multiple normal-temperature three-axis acceleration sensors, and multiple high-temperature acceleration sensors. The normal-temperature single-axis acceleration sensors and the normal-temperature three-axis acceleration sensors are respectively installed on the wing skin and keel in a matrix form, and the high-temperature acceleration sensors are installed on the surfaces of the left engine and the right engine. The output signals of the high-temperature acceleration sensors are processed by a sensor signal transmitter and then transmitted to the multi-channel switching matrix. The output signals of the normal-temperature single-axis acceleration sensors and the normal-temperature three-axis acceleration sensors are directly transmitted to the input end of the multi-channel switching matrix. The multi-channel switching matrix includes a group of analog switch arrays controlled by the FPGA controller, and under the control of the FPGA controller, the signals of any input channel of the multi-channel switching matrix can be transmitted to the output channel of the multi-channel switching matrix. The output signal of the multi-channel switching matrix is sent to the ADC sampling chip. The output end of the noise generator is connected to the input end of the ADC sampling chip. The noise generator includes a resistor, an operational amplifier, and a low-pass filter. The random white noise generated by the thermal disturbance of the resistor is amplified by the operational amplifier and then processed by the low-pass filter to generate a narrow-band disturbance signal. The original analog input signal obtained by superimposing the narrow-band disturbance signal and the output signal of the multi-channel switching matrix is input to the ADC sampling chip for sampling. The FPGA controller processes the sampling result output by the ADC sampling chip through a FIR high-pass filter to filter out the narrow-band disturbance signal added in the early stage. The finally obtained data is the sampling result after disturbance processing. The output signal of the ADC sampling chip enters the FIFO pre-trigger memory of the FPGA controller after being filtered and framed by the FPGA controller. For the multi-channel sensor data obtained by each sampling of the ADC sampling chip, the FPGA controller respectively compares it with a set threshold. If the sensor data of a certain channel exceeds the threshold, it is considered that an abnormal vibration event occurs at the corresponding position. When an abnormal vibration event is detected, the data of 500 ms before and after the occurrence of the abnormal vibration is stored in the eMMC memory as abnormal vibration data. The FPGA controller uses a positioning method based on the RSSI algorithm to analyze the abnormal vibration data, calculate the position of the abnormal vibration source, frame the calculated vibration source position, and send it to the host computer through the RS422 transceiver.

[0007] The steps of the positioning method based on the RSSI algorithm are as follows:

[0008] S1. Assume that the amplitude attenuation of the abnormal vibration data conforms to the exponential attenuation model:

[0009] ,

[0010] In the formula , represents the signal amplitude received by the -th sensor; represents the signal amplitude of the vibration source; represents the distance from the vibration source to the -th sensor; represents the attenuation coefficient, which is determined through experiments;

[0011] S2. Given that the layout position of the -th sensor is ( ), by analyzing the magnitudes of the signal amplitudes received by multiple sensors, establish a system of equations to solve for the position and amplitude of the vibration source; assume the position of the vibration source is ( ), according to the amplitude attenuation model in step S1, the distance from the vibration source to the -th sensor is expressed as:

[0012] ,

[0013] S3. Using the triangulation method, for the -th sensor, the position of the vibration source satisfies the formula :

[0014] ,

[0015] Expand the non-linear equation in this formula and convert it into a linear equation:

[0016] ,

[0017] Subtract the position formula of the vibration source relative to the -th sensor from the position formula of the vibration source relative to the 1st sensor to eliminate the and terms, and after simplification, we get:

[0018] ,

[0019] Then, for sensors, we can obtain linear equations, and write these systems of equations in matrix form:

[0020] ,

[0021] Formula In the formula, is the coefficient matrix; where:

[0022] ,

[0023] ,

[0024] ,

[0025] Finally, the least squares method is used to solve:

[0026] ,

[0027] Formula In the formula, is the transpose matrix of is the inverse matrix of ; the solved vector p is the vibration source position ( ).

[0028] The core function of this method is to monitor the vibration status of the aircraft in real time during flight, and identify and locate the vibration source in real time when an abnormal vibration event occurs. The vibration measurement module can sense the vibration of the airframe and convert the vibration signal of the structure into an electrical signal. The sensor signal transmitter is used to convert the charge signal output by the acceleration sensor into a voltage signal suitable for backend acquisition and analysis. The multi-channel switching matrix includes multiple input channels and one output channel, and is internally composed of a group of analog switch arrays controlled by external signals.

[0029] The noise generator is used to generate random perturbation signals. The thermal noise of a resistor is generated by the thermal perturbation of the charge carriers inside the resistor. This irregular electron movement will generate tiny voltage fluctuations at both ends of the resistor. Since these voltage fluctuations are random, they belong to white noise and are close to a Gaussian distribution. The thermal noise voltage (root mean square value) of the resistor can be calculated according to the following formula:

[0030] , in the formula is the Boltzmann constant, usually taking the value of = 1.38×10 -23 J / K; is the temperature (unit is Kelvin, K); is the resistance value (unit: ohm, Ω); is the noise bandwidth (unit: hertz, Hz), that is, the measured frequency range. and The change of affects the noise in the form of square root, so the amplified resistor-derived noise source is stable. Therefore, the present invention uses a simple large resistor combined with an operational amplifier to generate Gaussian white noise, which is easy to operate and has low cost. The cut-off frequency of the low-pass filter in the noise generator is about one-tenth of the sampling frequency, which is much lower than the input signal bandwidth. At this time, the narrowband disturbance signal and the input signal are independent of each other in the frequency domain, which is convenient for filtering the disturbance signal in the later sampling result.

[0031] The ADC sampling chip is responsible for sampling the sensor signals from the multi-channel switching matrix and converting the analog signals into digital signals. The FPGA controller, as the main control module of the entire system, controls the entire sampling, storage, analysis, and transmission processes. During the sampling process of the ADC sampling chip for the output signals of the multi-channel switching matrix, harmonic spurs will be caused due to coherent sampling and channel switching problems, resulting in distortion of the ADC sampling chip and increasing the sampling error. In order to minimize this error as much as possible, the present invention proposes a method of injecting out-of-band disturbance to reduce the sampling error of the ADC sampling chip. The sampling error is mainly reflected in the deterioration of the dynamic performance of the ADC sampling chip. Among them, SFDR (spurious-free dynamic range) is one of the important indicators of the dynamic performance of the ADC sampling chip.

[0032] During the sampling and quantization process of the ADC sampling chip, due to its own defects (such as DNL error, which refers to the difference between the actual codeword and the ideal codeword), the quantization codewords are of different qualities, and the quantization results are good or bad. Especially when the sampling dynamic range and the amount of sampling data are large, the "bad codewords" caused by the DNL error have periodic repeatability, resulting in harmonic proliferation and destroying the spurious-free dynamic range of the signal. The main principle of the out-of-band disturbance technology is that the disturbance signal is a random signal and has no correlation with the input signal. The disturbance signal and the input signal are superimposed together at the input end of the ADC sampling chip, which destroys the periodicity of the quantization error of the ADC sampling chip, averages the DNL error, disperses and normalizes the DNL error of the ADC sampling chip, makes the quantization process of the ADC sampling chip more linear, and finally makes the coherent spurious signals spread within the background noise. Although this method will increase the background noise of the ADC sampling chip and cause a slight decrease in SNR, it helps to improve the quality of the effective signal, significantly improves the SFDR of the ADC sampling chip, and greatly improves the sampling accuracy of the ADC sampling chip. High-precision signal sampling provides reliable data support for the later vibration positioning method based on amplitude change.

[0033] In the present invention, the USB controller is used to transmit sampled data to the host computer and receive the configuration file sent by the host computer, so as to achieve two-way communication between the FPGA controller and the host computer through the USB controller; the eMMC memory is used to store data of different sensors collected by the device, including normal data and abnormal data corresponding to abnormal vibration events; the RS422 transceiver is used to feedback the vibration position identification and positioning result. The FPGA controller controls the two-way communication of data between it and the USB controller through the USB transmission control module inside it; the FPGA controller controls the operation of the eMMC memory through the eMMC read-write control module inside it to complete the data read-write function. The task management and storage control module inside the FPGA controller is used to control the storage task management, USB data transmission and read-write scheduling functions of the eMMC memory. After the system is started, the FPGA controller first reads the device information from the eMMC memory through the task management and storage control module and initializes the device. After the initialization is completed, the FPGA controller enters the waiting state. In the waiting state, when the FPGA controller receives the successful enumeration instruction of the USB controller, the FPGA controller enters the data transmission state and waits for the host computer to send a read data instruction or a write data instruction. When the FPGA controller receives the read data instruction, the task management and storage control module of the FPGA controller controls the eMMC read-write control module inside the FPGA controller to read the stored data from the eMMC memory, and then sends the data to the host computer through the USB controller; if the FPGA controller receives the write data instruction, the data received by the USB controller is written into the corresponding storage location of the eMMC memory as a new configuration file.

[0034] The positioning algorithm based on the RSSI (Received Signal Strength Indicator) algorithm mainly estimates the position by analyzing the attenuation characteristics of the signal strength. In practical applications, the positioning algorithm based on the RSSI algorithm has relatively low requirements for time synchronization and mainly relies on the accurate measurement of the signal amplitude. Using this positioning algorithm to calculate the vibration source position provides data support for the later maintenance and optimization of the aircraft, and improves the safety and reliability of the aircraft flight.

[0035] Preferably, the sensor signal transmitter includes a charge-voltage conversion circuit, a filtering circuit, and an amplification and limiting circuit. The input charge signal is first converted into a voltage signal through the charge-voltage conversion circuit, and the voltage signal is then filtered by the filtering circuit to remove low-frequency interference signals and high-frequency resonance signals. Finally, the amplified and limited circuit amplifies and limits the filtered voltage signal to adapt to the input voltage range of the ADC sampling chip. The amplification and limiting circuit includes a signal amplification circuit and a signal limiting circuit.

[0036] Preferably, the ADC sampling chip includes a first-stage ADC module with 6 bits, a second-stage ADC module with 7 bits, a DAC module, and an amplifier component. The original analog input signal is first converted into a digital signal by the first-stage ADC module and then converted into a new analog signal by the DAC module. The new analog signal is subtracted from the original analog input signal to obtain the residual signal after the first-stage conversion. The residual signal is adjusted in gain by the amplifier component and then enters the second-stage ADC module for conversion to obtain a digital signal. Finally, the digital signal obtained by the conversion of the first-stage ADC module and the digital signal obtained by the conversion of the second-stage ADC module are added together to obtain the final conversion result.

[0037] Preferably, the threshold value set in the FPGA controller is the vibration acceleration value of 2g.

[0038] The technical solution provided by the present invention has the following technical effects compared with the prior art: In the present invention, the perturbation technology method is used to improve the sampling accuracy of the ADC sampling chip, and based on the abnormal vibration recognition algorithm based on threshold discrimination and the positioning algorithm based on the RSSI algorithm, during the process of the aircraft performing flight tasks, the real-time acquisition and storage of vibration signals, the recognition of abnormal vibration events, and the real-time positioning of the abnormal vibration source are realized, solving the problems existing in other monitoring methods under the existing technical conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The drawings here are incorporated into the specification and form a part of this specification, showing the embodiments consistent with the present invention and used together with the specification to explain the principles of the present invention.

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, other drawings can also be obtained based on these drawings without creative efforts.

[0041] Figure 1 It is a schematic flowchart of a sensor signal acquisition system described in an embodiment of the present invention;

[0042] Figure 2 It is a signal processing diagram after injecting out-of-band perturbation into the analog input signal in an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0043] In order to be able to more clearly understand the above-mentioned objects, features, and advantages of the present invention, the following will further describe the solution of the present invention. It should be noted that, without conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other.

[0044] In the description, it should be noted that the terms "installed", "connected", and "coupled" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two components. For those of ordinary skill in the art, the specific meanings of the above terms can be understood according to specific situations.

[0045] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways different from those described herein; obviously, the embodiments in the specification are only a part of the embodiments of the present invention, rather than all of the embodiments.

[0046] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0047] In one embodiment, as Figure 1As shown, an abnormal vibration monitoring method for an aircraft is disclosed, which includes building a sensor signal acquisition system. The sensor signal acquisition system includes a vibration measurement module, a multi-channel switching matrix, a noise generator, an ADC sampling chip, an FPGA controller, a USB controller, an eMMC memory, an RS422 transceiver, and a power management and power distribution unit. The power management and power distribution unit supplies power to the entire sensor signal acquisition system; the vibration measurement module includes multiple normal-temperature single-axis acceleration sensors, multiple normal-temperature three-axis acceleration sensors, and multiple high-temperature acceleration sensors. The normal-temperature single-axis acceleration sensors and the normal-temperature three-axis acceleration sensors are respectively installed on the wing skin and the keel in a matrix form, and the high-temperature acceleration sensors are installed on the surfaces of the left engine and the right engine. The output signals of the high-temperature acceleration sensors are processed by a sensor signal transmitter and then transmitted to the multi-channel switching matrix. The output signals of the normal-temperature single-axis acceleration sensors and the normal-temperature three-axis acceleration sensors are transmitted to the input end of the multi-channel switching matrix. The sensor signal transmitter includes a charge-voltage conversion circuit, a filtering circuit, and an amplification and limiting circuit. The input charge signal is first converted into a voltage signal through the charge-voltage conversion circuit, and the voltage signal is then filtered by the filtering circuit to remove low-frequency interference signals and high-frequency resonance signals. Finally, the filtered voltage signal is amplified and limited by the amplification and limiting circuit to adapt to the input voltage range of the ADC sampling chip; the multi-channel switching matrix includes a group of analog switch arrays controlled by the FPGA controller, and under the control of the FPGA controller, the signals of any input channel can be transmitted to the output channel of the multi-channel switching matrix. The output signal of the multi-channel switching matrix is sent to the ADC sampling chip. The output end of the noise generator is connected to the input end of the ADC sampling chip. The noise generator includes a resistor, an operational amplifier, and a low-pass filter. The random white noise generated by the thermal disturbance of the resistor is amplified by the operational amplifier and then processed by the low-pass filter to generate a narrowband disturbance signal, such as Figure 2As shown in the figure, the original analog input signal obtained by superimposing the narrowband disturbance signal and the output signal of the multi-channel switching matrix is input into the ADC sampling chip for sampling; the ADC sampling chip includes a 6-bit first-stage ADC module, a 7-bit second-stage ADC module, a DAC module, and an amplifier component. The original analog input signal is first converted into a digital signal by the first-stage ADC module and then converted into a new analog signal by the DAC module. The new analog signal is subtracted from the original analog input signal to obtain the residual signal after the first-stage conversion. The residual signal enters the second-stage ADC module for conversion to obtain a digital signal after gain adjustment by the amplifier component. Finally, the digital signal obtained by the conversion of the first-stage ADC module and the digital signal obtained by the conversion of the second-stage ADC module are added to obtain the final conversion result; the FPGA controller processes the sampling result output by the ADC sampling chip through a FIR high-pass filter to filter out the narrowband disturbance signal added in the early stage. The finally obtained data is the sampling result after disturbance processing. The output signal of the ADC sampling chip enters the FIFO pre-trigger memory of the FPGA controller after filtering and framing processing by the FPGA controller; for the multi-channel sensor data obtained by each sampling of the ADC sampling chip, the FPGA controller compares it with the set threshold respectively. If the sensor data of a certain channel exceeds the threshold, it is considered that an abnormal vibration event has occurred at the corresponding position. When an abnormal vibration event is detected, the data of 500 ms before and after the occurrence of the abnormal vibration is stored in the eMMC memory as abnormal vibration data. The FPGA controller uses a positioning method based on the RSSI algorithm to analyze the abnormal vibration data, calculate the position of the abnormal vibration source, and frame the calculated vibration source position, and send it to the host computer through the RS422 transceiver;

[0048] The steps of the positioning method based on the RSSI algorithm are as follows:

[0049] S1. Assume that the amplitude attenuation of the abnormal vibration data conforms to the exponential attenuation model:

[0050] ,

[0051] Formula In, represents the signal amplitude received by the th sensor; represents the signal amplitude of the vibration source; represents the distance from the vibration source to the th sensor; represents the attenuation coefficient, which is determined by experiments;

[0052] S2. It is known that the layout position of the th sensor is ( ) By analyzing the magnitudes of the signals received by multiple sensors and establishing a system of equations, the position and magnitude of the vibration source can be solved. Assume the position of the vibration source is ([[]] ), according to the amplitude attenuation model in step S1, the distance from the vibration source to the th sensor is expressed as:

[0053] ,

[0054] S3. Using the triangulation method, for the th sensor, the position of the vibration source satisfies the formula :

[0055] ,

[0056] Expand the non-linear equation in this formula and convert it into a linear equation:

[0057] ,

[0058] Use the position formula of the vibration source relative to the th sensor to subtract the position formula of the vibration source relative to the 1st sensor, eliminate and terms, and after simplification, we get:

[0059] ,

[0060] Then, for sensors, linear equations can be obtained. Write these systems of equations in matrix form:

[0061] ,

[0062] In the formula , is the coefficient matrix; where:

[0063] ,

[0064] ,

[0065] ,

[0066] Finally, use the least squares method to solve:

[0067] ,

[0068] Formula In is the transpose matrix of is the inverse matrix of; the solved vector p is the vibration source position ( ).

[0069] The core function of the method described in the present invention is to monitor the vibration condition of the aircraft in real time during flight, and to identify and locate the vibration source in real time when an abnormal vibration event occurs. The vibration measurement module can sense the vibration of the airframe and convert the vibration signal of the structure into an electrical signal. The sensor signal transmitter is used to convert the charge signal output by the acceleration sensor into a voltage signal suitable for backend acquisition and analysis. The multi-channel switching matrix includes multiple input channels and one output channel, and is internally composed of a group of analog switch arrays controlled by external signals.

[0070] The noise generator is used to generate random perturbation signals. The thermal noise of a resistor is generated by the thermal perturbation of the charge carriers inside the resistor. This irregular electron movement will generate tiny voltage fluctuations at both ends of the resistor. Since these voltage fluctuations are random, they belong to white noise and are close to a Gaussian distribution. The thermal noise voltage (root mean square value) of the resistor can be calculated according to the following formula:

[0071] , in the formula is the Boltzmann constant, usually taking the value of = 1.38×10 -23 J / K; is the temperature (unit is Kelvin, K); is the resistance value (unit is ohm, Ω); is the noise bandwidth (unit is Hertz, Hz), that is, the measurement frequency range. and The changes of

[0072] The ADC sampling chip is responsible for sampling the sensor signals from the multi-channel switching matrix and converting the analog signals into digital signals. The FPGA controller, as the main control module of the entire system, controls the entire sampling, storage, analysis, and transmission processes. During the sampling process of the output signals of the multi-channel switching matrix by the ADC sampling chip, harmonic spurs will be caused due to coherent sampling and channel switching problems, resulting in distortion of the ADC sampling chip and increasing the sampling error. To minimize this error as much as possible, the present invention proposes a method of injecting out-of-band perturbation to reduce the sampling error of the ADC sampling chip. The sampling error is mainly reflected in the deterioration of the dynamic performance of the ADC sampling chip. Among them, SFDR (Spurious Free Dynamic Range) is one of the important indicators of the dynamic performance of the ADC sampling chip.

[0073] During the sampling and quantization process of the ADC sampling chip, due to its own defects (such as DNL error, which refers to the difference between the actual codeword and the ideal codeword), the quantization codewords are of different qualities, and the quantization results are good or bad. Especially when the sampling dynamic range and the amount of sampling data are large, the "bad codewords" caused by the DNL error have periodic repeatability, resulting in harmonic proliferation and destroying the spurious free dynamic range of the signal. The main principle of the out-of-band perturbation technology is that the perturbation signal is a random signal and has no correlation with the input signal. At the input end of the ADC sampling chip, the perturbation signal and the input signal are superimposed together, which destroys the periodicity of the quantization error of the ADC sampling chip, averages the DNL error, disperses and normalizes the DNL error of the ADC sampling chip, makes the quantization process of the ADC sampling chip more linear, and finally achieves that the coherent spurious signals are scattered within the background noise. Although this method will increase the background noise of the ADC sampling chip and slightly decrease the SNR, it helps to improve the quality of the effective signal, significantly improve the SFDR of the ADC sampling chip, and greatly enhance the sampling accuracy of the ADC sampling chip. High-precision signal sampling provides reliable data support for the later vibration positioning method based on amplitude change.

[0074] In the present invention, the USB controller is used to transmit sampled data to the host computer and receive the configuration file sent by the host computer, so as to achieve two-way communication between the FPGA controller and the host computer through the USB controller; the eMMC memory is used to store data of different sensors collected by the device, including normal data and abnormal data corresponding to abnormal vibration events; the RS422 transceiver is used to feedback the vibration position identification and positioning result. The FPGA controller controls the two-way communication of data between it and the USB controller through the USB transmission control module inside it; the FPGA controller controls the eMMC memory to work through the eMMC read / write control module inside it to complete the data read / write function. The task management and storage control module inside the FPGA controller is used to control the storage task management, USB data transmission, and read / write scheduling functions of the eMMC memory. After the system is started, the FPGA controller first reads the device information from the eMMC memory through the task management and storage control module and initializes the device. After the initialization is completed, the FPGA controller enters the waiting state. In the waiting state, when the FPGA controller receives the USB controller enumeration success instruction, the FPGA controller enters the data transmission state and waits for the host computer to send a data read instruction or a data write instruction. When the FPGA controller receives the data read instruction, the task management and storage control module of the FPGA controller controls the eMMC read / write control module inside the FPGA controller to read the stored data from the eMMC memory, and then sends the data to the host computer through the USB controller; if the FPGA controller receives the data write instruction, the data received by the USB controller is written into the corresponding storage location of the eMMC memory as a new configuration file.

[0075] The positioning algorithm based on the RSSI (Received Signal Strength Indicator) algorithm mainly estimates the position by analyzing the attenuation characteristics of the signal strength. In practical applications, the positioning algorithm based on the RSSI algorithm has low requirements for time synchronization and mainly relies on the accurate measurement of the signal amplitude. Using this positioning algorithm to calculate the vibration source position provides data support for the later maintenance and optimization of the aircraft, and improves the safety and reliability of the aircraft flight.

[0076] The above are only specific implementation manners of the present invention, enabling those skilled in the art to understand or implement the present invention. Although the above embodiments have been described in detail with reference to the foregoing, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some or all of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the above embodiments, and they should all be covered by the protection scope of the claims.

Claims

1. An abnormal vibration monitoring method for an aircraft, characterized in that, Including building a sensor signal acquisition system, the sensor signal acquisition system includes a vibration measurement module, a multi-channel switching matrix, a noise generator, an ADC sampling chip, an FPGA controller, a USB controller, an eMMC memory, an RS422 transceiver, and a power management and power distribution unit. The power management and power distribution unit supplies power to the entire sensor signal acquisition system; the vibration measurement module includes multiple room-temperature single-axis acceleration sensors, multiple room-temperature three-axis acceleration sensors, and multiple high-temperature acceleration sensors. The room-temperature single-axis acceleration sensors and the room-temperature three-axis acceleration sensors are respectively installed on the wing skin and keel in a matrix form, and the high-temperature acceleration sensors are installed on the surfaces of the left engine and the right engine. The output signals of the high-temperature acceleration sensors are processed by a sensor signal transmitter and then transmitted to the multi-channel switching matrix. The output signals of the room-temperature single-axis acceleration sensors and the room-temperature three-axis acceleration sensors are directly transmitted to the input end of the multi-channel switching matrix. The multi-channel switching matrix includes a group of analog switch arrays controlled by the FPGA controller, and under the control of the FPGA controller, the signals of any input channel of the multi-channel switching matrix can be transmitted to the output channel of the multi-channel switching matrix. The output signal of the multi-channel switching matrix is sent to the ADC sampling chip. The output end of the noise generator is connected to the input end of the ADC sampling chip. The noise generator includes a resistor, an operational amplifier, and a low-pass filter. The random white noise generated by the thermal disturbance of the resistor is amplified by the operational amplifier and then processed by the low-pass filter to generate a narrowband disturbance signal. The original analog input signal obtained by superimposing the narrowband disturbance signal and the output signal of the multi-channel switching matrix is input to the ADC sampling chip for sampling; The FPGA controller processes the sampling results output by the ADC sampling chip through a FIR high-pass filter to filter out the narrowband disturbance signal added in the early stage. The finally obtained data is the sampling result after disturbance processing. The output signal of the ADC sampling chip enters the FIFO pre-trigger memory of the FPGA controller after being filtered and framed by the FPGA controller; for the multi-channel sensor data obtained by each sampling of the ADC sampling chip, the FPGA controller respectively compares it with a set threshold. If the sensor data of a certain channel exceeds the threshold, it is considered that an abnormal vibration event has occurred at the corresponding position. When an abnormal vibration event is detected once, the data of 500 ms before and after the occurrence of the abnormal vibration is stored in the eMMC memory as abnormal vibration data. The FPGA controller uses a positioning method based on the RSSI algorithm to analyze the abnormal vibration data, calculate the position of the abnormal vibration source, and frame the calculated vibration source position and send it to the upper computer through the RS422 transceiver; The steps of the positioning method based on the RSSI algorithm are as follows: S1. Assume that the amplitude attenuation of the abnormal vibration data conforms to the exponential attenuation model: ， Formula In represents the signal amplitude received by the th sensor; represents the signal amplitude of the vibration source; represents the distance from the vibration source to the th sensor; represents the attenuation coefficient, which is determined through experiments; S2. Given that the installation position of the th sensor is ( ), by analyzing the magnitudes of the signals received by multiple sensors and establishing a system of equations, the position and magnitude of the vibration source can be solved; assuming that the position of the vibration source is ( ), according to the amplitude attenuation model in step S1, the distance from the vibration source to the th sensor is expressed as: ， S3. Using the triangulation method, for the th sensor, the position of the vibration source satisfies the formula : ， Expand the formula for the non-linear equation and convert it into a linear equation: ， Using the position formula of the vibration source relative to the th sensor to subtract the position formula of the vibration source relative to the 1st sensor, eliminating the and terms, after simplification, we get: ， Then, for sensors, we can obtain linear equations. Write these equations in matrix form: ， Formula In is the coefficient matrix; where: ， ， ， Finally, use the least squares method to solve: ， Formula In is the transposed matrix of is the inverse matrix of; the solved vector p is the vibration source position ( ).

2. The abnormal vibration monitoring method for an aircraft according to claim 1, wherein The sensor signal transmitter includes a charge-voltage conversion circuit, a filtering circuit, and an amplification and limiting circuit. The input charge signal is first converted into a voltage signal by the charge-voltage conversion circuit, and the voltage signal is then filtered by the filtering circuit to remove low-frequency interference signals and high-frequency resonance signals. Finally, the filtered voltage signal is amplified and limited by the amplification and limiting circuit to adapt to the input voltage range of the ADC sampling chip.

3. The abnormal vibration monitoring method for an aircraft according to claim 1, characterized in that The ADC sampling chip includes a 6-bit first-stage ADC module, a 7-bit second-stage ADC module, a DAC module, and an amplifier component. The original analog input signal is first converted into a digital signal by the first-stage ADC module and then converted into a new analog signal by the DAC module. The new analog signal is subtracted from the original analog input signal to obtain the residual signal after the first-stage conversion. The residual signal enters the second-stage ADC module for conversion to obtain a digital signal after gain adjustment by the amplifier component. Finally, the digital signal obtained by the conversion of the first-stage ADC module and the digital signal obtained by the conversion of the second-stage ADC module are added together to obtain the final conversion result.

4. A method for abnormal vibration monitoring of an aircraft according to claim 1, characterized in that, The threshold value set in the FPGA controller is the vibration acceleration value of 2g.

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