Water supply pipeline leakage identification method based on linear prediction cepstrum coefficients and lyapunov indexes
A linear prediction and cepstral coefficient technology, applied in pipeline systems, mechanical equipment, gas/liquid distribution and storage, etc., can solve the problem of low leakage identification accuracy, avoid excessive dependence, improve identification accuracy, cost saving effect
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specific Embodiment approach 1
[0028] Specific implementation mode one: combine figure 1 , figure 2 To illustrate this embodiment, the water supply pipeline leakage identification method based on linear predictive cepstrum coefficient and lyapunov index given in this embodiment specifically includes the following steps:
[0029] Step 1: Collect the environmental background noise signal when the pipeline is not connected to water, and then collect the sound signal when the pipeline is normal and the sound signal when the pipeline is leaking under the same environmental background. The collection process is as figure 1 As shown, the acceleration sensor 3 is connected to the valve plug 2 on the water pipe 1, the acceleration sensor 3 converts the collected sound signal into an electrical signal, and then transmits the electrical signal to the charge amplifier 4 for amplification, and then passes the dynamic acquisition analyzer 5 Connect to host computer 6 and carry out follow-up analysis;
[0030] Step 2,...
specific Embodiment approach 2
[0034] Specific embodiment two: the difference between this embodiment and specific embodiment one is that the calculation process of the lyapunov index specifically includes:
[0035] A1. Calculate the time delay τ of the signal S through the autocorrelation function method;
[0036] A2, seek its average period T' by Fourier transform to signal S;
[0037] A3. Calculate the correlation dimension c of the signal S, and then determine the embedding dimension m;
[0038] A4. Use the time delay τ, the average period T′, and the embedding dimension m to perform phase space reconstruction on the signal S to be measured, and obtain the reconstructed signal phase space Y(t i ); i=0,...,n; where, t 0 Indicates the starting point of the time series, t n is the end point of the time series;
[0039] A5. Calculate the starting point Y(t of the phase space of the reconstructed signal 0 ) and its nearest neighbor Y 0 (t 0 ) distance L 0 ;
[0040] A6. Track the time evolution of t...
specific Embodiment approach 3
[0048] Embodiment 3: This embodiment differs from Embodiment 2 in that the dimension m>2c+1 in step A3.
[0049] Other steps and parameters are the same as those in Embodiment 1 or 2.
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