Method and device for detecting and assessing deposit in metal pipe
A technology for deposits and metal pipes, applied in pipeline systems, gas/liquid distribution and storage, mechanical equipment, etc., can solve problems such as undetectable by endoscopes, destructive testing, etc., and solve equipment failures or safety accidents , accurate detection and broad prospects
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[0022] Example 1: as figure 1 As shown, the detection device provided by the present invention includes an excitation device 1, a microphone 2, and a computer 3 with a data acquisition card. The computer 3 is equipped with acquisition software for signal recording, and is used for wavelet packet analysis, time domain analysis and Signal processing software and neural network expert diagnosis system that can calculate the characteristic parameters of each frequency band energy value, time domain signal duration and / or spectrum maximum resonance frequency amplitude of wavelet packet analysis. The microphone 2 is fixed on the metal elbow and is located at the normal incident position of the sound wave. The microphone 2 and the data acquisition card are respectively connected with the computer host. In order to excite the sound wave reflecting the impulse response characteristics of the structural body in the metal elbow, the excitation device in this embodiment 1 can use a steel ...
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[0023] Example 2: The specific steps for detecting and evaluating the deposits in the metal elbow with the detection device of Example 1 are as follows:
[0024] 1) Hit the lower part A of the metal elbow with a steel hammer to excite a sound wave that can reflect the impulse response characteristics of the structure body;
[0025] 2) Use the microphone and the data acquisition card to transmit and collect the excited sound wave signal;
[0026] 3) Record the sound with the acquisition software and store it in the computer;
[0027] 4) Perform wavelet packet analysis, time domain analysis and / or spectrum analysis on the signal, and calculate the characteristic parameters of each frequency band energy value, time domain signal duration and / or the maximum resonance frequency amplitude of the spectrogram of the wavelet packet analysis;
[0028] 5) Input the characteristic parameters into the neural network expert diagnosis system for identification, qualitatively and quantitativ...
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