Aviation cable insulation defect online identification and positioning method

Abstract

The invention discloses an aviation cable insulation defect on-line identification and positioning method comprising the following steps: generating a corresponding m sequence and a sine wave according to a center frequency requirement of an incident signal, and carrying out 1: 1 modulation to generate an enhanced spread spectrum time domain reflection method incident signal; injecting an incident signal into the cable, collecting a reflected signal, and calculating a time domain correlation operation waveform according to the reflected signal; eliminating inherent interference waves by using reference wave comparison, and performing time-frequency analysis on the time-domain correlation operation waveform after the interference waves are eliminated by using a smooth pseudo Wigner distribution algorithm; performing time-frequency domain cross correlation operation according to a time-frequency analysis result, performing insulation defect identification according to a time-frequency domain cross correlation operation amplitude, and calculating an insulation defect positioning distance according to a time difference of a time-frequency domain cross correlation operation waveform at a cable head end position and an insulation defect position. The method solves the problems that a traditional reflection method is weak in insulation defect identification capability and low in positioning precision, and is of great significance for guaranteeing safe operation of a cable system.

Description

Technical Field

[0001] The present invention belongs to the field of nondestructive detection of insulation defects of wires and cables, and specifically relates to an online identification and positioning method for insulation defects of aviation cables based on enhanced spread spectrum time domain reflectometry. Background Art

[0002] In the field of modern aviation, the cable system is the core component of the aircraft electrical system, responsible for transmitting power and signals. Since aviation cables need to work stably under extreme temperature, humidity, pressure and mechanical stress, the reliability of their insulation performance is crucial. Insulation defects may lead to power loss, signal interference, and even fire, posing a threat to flight safety. Therefore, online identification and positioning of aviation cable insulation defects is a key link in aircraft maintenance and safe operation. Traditional cable detection methods, such as visual inspection and voltage testing, have limitations such as low efficiency, low accuracy, and inability to monitor in real time. The insulation status of the cable can be effectively detected by measuring insulation resistance, leakage current and dielectric loss tangent, but these methods cannot realize the positioning function of cable insulation defects. With the development of electronic technology and signal processing technology, detection technology based on reflection method has emerged. This method has become a current research hotspot due to its advantages of non-destructive and online operation. The reflection method identifies and locates defects in the cable by injecting specific signals into the cable and analyzing the reflected signals. However, the traditional reflection method has limitations in identifying small defects and accurately locating them, and is easily affected by inherent noise and interference, making it impossible to achieve online identification and positioning of insulation defects. Summary of the invention

[0003] The purpose of the present invention is to propose an online identification and positioning method for aviation cable insulation defects in order to address the problems of weak cable insulation defect recognition ability and low positioning accuracy of the traditional reflection method. The method has the advantages of strong recognition ability, high and stable positioning accuracy, and is of great significance to improving the safety of aviation cables and the maintenance efficiency.

[0004] In order to achieve the above-mentioned invention object, the present invention specifically adopts the following technical solutions:

[0005] The first step is to generate the corresponding m-sequence and sine wave according to the center frequency requirements of the incident signal required for insulation defect identification and location, and perform 1:1 modulation to generate the enhanced spread spectrum time domain reflectometry incident signal;

[0006] The second step is to inject an incident signal into the cable and collect a reflected signal, and calculate a time domain correlation operation waveform from the reflected signal;

[0007] The third step is to use the reference wave to compare and eliminate the inherent interference wave;

[0008] The fourth step is to use the smoothed pseudo-Wigner distribution algorithm to perform time-frequency analysis on the time-domain correlation operation waveform after eliminating the interference wave;

[0009] The fifth step is to perform time-frequency domain cross-correlation operation based on the time-frequency analysis results, identify insulation defects based on the amplitude of the time-frequency domain cross-correlation operation waveform, and calculate the insulation defect positioning distance based on the time difference between the time-frequency domain cross-correlation operation waveform at the cable head end position and the insulation defect position.

[0010] The beneficial effects of the present invention are as follows:

[0011] 1. The present invention significantly improves the recognition capability and positioning accuracy of aviation cable insulation defects by using spread spectrum signals and advanced signal processing technology. By using the incident signal generated by m-sequence and sine wave modulation, combined with reference wave comparison and smoothed pseudo-Wigner distribution time-frequency analysis, this method can effectively eliminate the inherent interference wave from the reflected signal and increase the amplitude of the computational waveform characterizing the insulation defect, thereby accurately identifying and locating the cable insulation defect, greatly improving the reliability and practicality of defect detection.

[0012] 2. The present invention utilizes time domain correlation operations and time-frequency domain cross-correlation operations, which can extract reflection responses under the influence of noise and other signals in the system without interference, and has online real-time monitoring capabilities, which makes the maintenance of aviation cables more efficient and timely. Through the signal injection, acquisition and processing process, the method can continuously monitor the cable status and respond quickly when an abnormality is found. Once an insulation defect is detected, the analysis of the time-frequency domain cross-correlation operation waveform can accurately calculate the location of the defect, thereby providing maintenance personnel with clear directions, speeding up maintenance, reducing downtime caused by cable failures, and significantly improving the operating efficiency and safety of aviation systems.

[0013] The present invention not only improves the accuracy and reliability of the detection technology, but also enhances the maintenance capability of aviation cables through real-time monitoring, which has important practical value for improving aviation safety standards and reducing operating costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 is a flow chart of Example 1;

[0015] Figure 2 Generate a schematic diagram for the incident signal;

[0016] Figure 3 It is the time domain correlation operation waveform;

[0017] Figure 4 is the smoothed pseudo-Wigner distribution of defect locations;

[0018] Figure 5 It is the cross-correlation operation waveform in the time-frequency domain;

[0019] Figure 6 Comparison of waveform amplitude at defect location using two methods;

[0020] Figure 7 Comparison of insulation defect location distances using two methods. DETAILED DESCRIPTION

[0021] Embodiment 1

[0022] This embodiment takes the insulation defect identification and location of AWG12 aviation cable as an example, and provides an online identification and location method for insulation defects of aviation cables based on enhanced spread spectrum time domain reflectometry. The implementation process is as follows: Figure 1 As shown, the following steps are included:

[0023] Step S01: Generate an incident signal for enhanced spread spectrum time domain reflectometry.

[0024] Specifically, the center frequency of the incident signal required for identification and location of aviation cable insulation defects is set to 62.5 MHz, and the corresponding m-sequence and sine wave are generated. According to formula (1), the m-sequence and the sine wave are modulated 1:1 to generate the incident signal of the enhanced spread spectrum time domain reflectometry method, as shown in Figure 2 shown.

[0025] s(t)=A·m(t)·sin(2πf c t) (1)

[0026] Where s(t) is the incident signal at time t, A is the amplitude of the incident signal, and f c is the frequency of the incident signal, t is the time, s(t) is the signal value at time t, and m(t) is the m-sequence signal.

[0027] Step S02: injecting an incident signal into the cable and collecting a reflected signal, and calculating a time domain correlation operation waveform.

[0028] Specifically, a 2 cm insulation damage defect is made at a position 5.49 m away from the head end of the AWG12 aviation cable. The AWG12 aviation cable works under 270 V DC. The incident signal is injected into the cable using a signal generator, and the reflected signal is collected using an oscilloscope. According to formula (2), the time domain correlation operation waveform is calculated from the reflected signal, as follows: Figure 3 shown.

[0029]

[0030] Where f tc (t) is the time domain correlation operation value at time t, s(t-τ) is the incident signal value at time t-τ, and τ is the time delay.

[0031] Step S03: Eliminate the inherent interference waveform by comparing with the reference wave.

[0032] Specifically, according to the following formula (3), the time domain correlation operation waveform calculated under the cable defect scenario is correlated with the reference wave to obtain the correlation coefficient, and the correlation coefficient is used to adjust the original waveform to eliminate the interference wave. By comparing with the reference wave, it can be seen that the reflected waveform 10m away from the cable head end is caused by the cable load. When identifying and locating insulation defects, the waveform here is an inherent waveform and is ignored.

[0033]

[0034] Where y(t) is the time domain correlation operation waveform after eliminating interference, and r(t) is the reference wave.

[0035] Step S04: using a smoothed pseudo-Wigner distribution algorithm to perform time-frequency analysis on the time-domain correlation operation waveform after eliminating the interference wave.

[0036] Specifically, the smoothed pseudo-Wigner distribution algorithm defined by formula (4) is used to perform time-frequency analysis on the time-domain correlation operation waveform after eliminating the interference wave, and the smoothed pseudo-Wigner distribution of the defect position is obtained as follows: Figure 4 shown.

[0037]

[0038] Where s * (t) is the complex conjugate of the incident signal s(t), j is the imaginary unit, g(τ) is the smoothing window function, and τ1 and τ2 are independent integration variables.

[0039] Step S05: Perform a cross-correlation operation in the time-frequency domain, identify insulation defects based on the cross-correlation operation waveform in the time-frequency domain, and calculate the insulation defect location distance.

[0040] Specifically, according to formula (5), a time-frequency domain cross-correlation operation is performed to obtain the operation waveform as follows: Figure 5 As shown, insulation defects are identified by the amplitude of the time-frequency domain cross-correlation operation waveform. According to formula (6), the defect location distance is calculated by the time difference between the time-frequency domain cross-correlation operation waveform at the cable head end position and the insulation defect position.

[0041]

[0042] Where E s is the total energy of the incident signal, E r is the total energy of the reflected signal, SPWVD s (t,f c ) is the smoothed pseudo-Wigner distribution of the incident signal, SPWVD r (t,fc ) is the smoothed pseudo-Wigner distribution of the reflection signal.

[0043]

[0044] Where dis is the defect location distance, v is the propagation speed of the incident signal, and Δt is the time interval between wave peaks.

[0045] The online identification and positioning method of aviation cable insulation defects based on enhanced spread spectrum time domain reflection method in this embodiment improves the sensitivity and anti-interference ability of detection by using spread spectrum signals and advanced signal processing algorithms. The method uses m-sequence and sine wave for modulation to generate an incident signal with good autocorrelation, which enhances the penetration and resolution of the signal. The center frequency of the incident signal is much higher than the original signal of the cable, thereby realizing the online detection of insulation defects. The change of insulation defect recognition ability and measurable distance is achieved by adjusting the center frequency, and it has high adaptability. By smoothing the pseudo-Wigner distribution to perform time-frequency analysis on the reflected signal and performing time-frequency domain cross-correlation operations, the insulation defects of aviation cables can be more accurately identified and located.

[0046] The above insulation defects were identified and located 54 times using the traditional reflection method and the enhanced spread spectrum time domain reflection method of this embodiment. The 54 insulation defect identification and location results are as follows: Figure 6 and Figure 7 As shown. The fluctuation range of the waveform amplitude of the traditional reflection method at the defect position is 0.008~0.024, and the fluctuation range of the waveform amplitude of the enhanced spread spectrum time domain reflection method at the defect position is 0.5384~0.5526. The fluctuation range of the positioning distance of the traditional reflection method is 5.0270~5.7125, and the positioning distance of the enhanced spread spectrum time domain reflection method is stable at 5.4840. The average absolute error of the positioning results of the traditional reflection method is 0.22m, and the average absolute error of the positioning results of the enhanced spread spectrum time domain reflection method is 0.006m. The comparison results show that the waveform amplitude of the enhanced spread spectrum time domain reflection method at the defect position is larger, and the positioning distance is more accurate and stable.

Claims

1. An online identification and location method for insulation defects of aviation cables based on enhanced spread spectrum time domain reflectometry, characterized in that: The following steps are involved: The first step is to generate the corresponding m-sequence and sine wave according to the center frequency requirements of the incident signal required for insulation defect identification and location, and perform 1:1 modulation to generate the enhanced spread spectrum time domain reflectometry incident signal; The second step is to inject an incident signal into the cable and collect a reflected signal, and calculate a time domain correlation operation waveform from the reflected signal; The third step is to use the reference wave to compare and eliminate the inherent interference wave; The fourth step is to use the smoothed pseudo-Wigner distribution algorithm to perform time-frequency analysis on the time-domain correlation operation waveform after eliminating the interference wave; The fifth step is to perform time-frequency domain cross-correlation operation based on the time-frequency analysis results, identify insulation defects based on the amplitude of the time-frequency domain cross-correlation operation waveform, and calculate the insulation defect positioning distance based on the time difference between the time-frequency domain cross-correlation operation waveform at the cable head end position and the insulation defect position.

2. The method for online identification and location of insulation defects of aviation cables based on enhanced spread spectrum time domain reflectometry as claimed in claim 1, characterized in that: The center frequency requirement of the signal required for insulation defect identification and location is: the longer the cable under test, the lower the center frequency; the m sequence is modulated with the sine wave in a 1:1 ratio to generate the enhanced spread spectrum time domain reflectometry incident signal as shown in formula (1): s(t)=A·m(t)·sin(2πf c t) (1) Where s(t) is the incident signal, A is the incident signal amplitude, and f c is the frequency of the incident signal, and m(t) is the m-sequence signal.

3. The method for online identification and location of insulation defects of aviation cables based on enhanced spread spectrum time domain reflectometry as claimed in claim 1, characterized in that: The time domain correlation operation waveform is shown in formula (2): Where s(t) is the incident signal, f tc (t) is the time domain correlation calculation value at time t, and τ is the delay.

4. The method for online identification and location of insulation defects of aviation cables based on enhanced spread spectrum time domain reflectometry as claimed in claim 1, characterized in that: The method of eliminating the inherent interference wave by comparing with the reference wave is specifically as follows: The time domain correlation operation waveform calculated under the cable defect scenario is correlated with the reference wave according to the following formula to obtain the correlation coefficient, which is used to adjust the original waveform to eliminate the interference wave: Where y(t) is the time domain correlation operation waveform after eliminating interference, f tc (t) is the time domain correlation calculation value at time t, and r(t) is the reference wave.

5. The method for online identification and location of insulation defects of aviation cables based on enhanced spread spectrum time domain reflectometry as claimed in claim 1, characterized in that: The smoothed pseudo-Wigner distribution of the incident signal s(t) is defined by formula (4): Where s * (t) is the complex conjugate of the incident signal s(t), j is the imaginary unit, g(τ) is the smoothing window function, τ1 and τ2 are independent integral variables, and f c is the incident signal frequency.

6. The method for online identification and location of insulation defects of aviation cables based on enhanced spread spectrum time domain reflectometry as claimed in claim 1, characterized in that: The time-frequency domain cross-correlation operation waveform is calculated by formula (5): Where E s is the total energy of the incident signal, E r is the total energy of the reflected signal, SPWVD s (t,f c ) is the smoothed pseudo-Wigner distribution of the incident signal, SPWVD r (t,f c ) is the smoothed pseudo-Wigner distribution of the reflected signal, f c is the incident signal frequency.

7. The method for online identification and location of insulation defects of aviation cables based on enhanced spread spectrum time domain reflectometry as claimed in claim 1, characterized in that: The insulation defect location distance is calculated by formula (6): Where dis is the defect location distance, v is the propagation speed of the incident signal, and Δt is the time interval between wave peaks.

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