ACOUSTIC RESONANCE DIAGNOSTIC METHOD FOR DETECTING STRUCTURAL DEGRADATION AND SYSTEM APPLYING THE SAME
An acoustic resonance diagnostic method for detecting structural degradation is provided. The method includes steps as follows: Firstly, a training model is built using a deep neural network. At least two training acoustic signals are inputted to the training model to carry a training. A diagnostic model is built according to a result of the training using a convolutional neural network. An under-test sound wave signal is captured from an under-test section of an under-test structure through direct contact, non-contact, or indirect contact. A structural degradation state of the under-test section is determined according to the under-test sound wave signal through the diagnostic model.
1 . An acoustic resonance diagnostic method for detecting structural degradation, comprising:
building a training model using a deep neural network (DNN);
inputting at least two training acoustic signals to the training model to carry a training;
building a diagnostic model according to a result of the training using a convolutional neural network (CNN);
capturing an under-test sound wave signal from an under-test section of an under-test structure; and
determining a structural degradation state of the under-test section according to the under-test sound wave signal through the diagnostic model.
2 . The acoustic resonance diagnostic method for detecting structural degradation according to claim 1 , wherein each of the at least two training acoustic signals and the under-test sound wave signal has a time waveform.
3 . The acoustic resonance diagnostic method for detecting structural degradation according to claim 2 , wherein before building the diagnostic model, the method further comprises a filtering step, comprising:
performing a time domain to frequency domain conversion to convert the time waveform into a frequency-domain waveform; and
capturing a part of the frequency-domain waveform to obtain a frequency band.
4 . The acoustic resonance diagnostic method for detecting structural degradation according to claim 3 , wherein the diagnostic model comprises:
a plurality of feature labels whose feature values add up to 1.
5 . The acoustic resonance diagnostic method for detecting structural degradation according to claim 3 , wherein the frequency-domain waveform has a frequency band between 30 Hz˜1600 Hz.
6 . The acoustic resonance diagnostic method for detecting structural degradation according to claim 1 , wherein the under-test sound wave signal is captured by a sound wave sensing unit which is in contact with or separated from the under-test section.
7 . The acoustic resonance diagnostic method for detecting structural degradation according to claim 6 , wherein the at least two training acoustic signals are captured from at least two sensing positions of the under-test structure by the sound wave sensing unit.
8 . The acoustic resonance diagnostic method for detecting structural degradation according to claim 4 , wherein the step of determining the structural degradation state of the under-test section comprises determining the type of the under-test structure according to the feature values and determining whether the under-test section leaks.
9 . The acoustic resonance diagnostic method for detecting structural degradation according to claim 1 , wherein the step of carrying the training comprises:
performing a normalization treatment on the at least two training acoustic signals, determining whether the at least two training acoustic signals is a transient signal whose waveform changes dramatically or is a steady signal whose waveform is stable and gentle;
selecting a plurality of steady signals from the at least two training acoustic signals, inputting the plurality of steady signals to a deep autoencoder based on a deep convolutional network, extracting a plurality of features and pre-selecting a plurality of feature labels; and
verifying whether the plurality of steady signals that have been treated with a compression process and a decompression process of the deep autoencoder.
10 . The acoustic resonance diagnostic method for detecting structural degradation according to claim 1 , wherein the step of carrying the training comprises:
inputting a verification sound wave signal to the convolutional neural network of the diagnostic model to be used as a verification data to test whether the diagnostic model can successfully detect a transient state.
11 . An acoustic resonance diagnostic system for detecting structural degradation, comprising:
a sound wave sensing unit, used to capture an under-test sound wave signal from an under-test section of an under-test structure;
an acoustic resonance diagnostic module, used to perform the following steps:
building a training model using a deep neural network;
inputting at least two training acoustic signals to the training model to carry a training;
building a diagnostic model according to a result of the training using a convolutional neural network; and
determining a structural degradation state of the under-test section according to the under-test sound wave signal through the diagnostic model; and
a communication module used to signal-connect the sound wave sensing unit to the acoustic resonance diagnostic module.
12 . The acoustic resonance diagnostic system for detecting structural degradation according to claim 11 , further comprising a signal filter used to obtain a frequency band from a time waveform of each of the at least two training acoustic signals and the under-test sound wave signal.
13 . The acoustic resonance diagnostic system for detecting structural degradation according to claim 11 , wherein the sound wave sensing unit is in contact with or is separated from the under-test section.
14 . The acoustic resonance diagnostic system for detecting structural degradation according to claim 11 , wherein the sound wave sensing unit has a global positioning system (GPS).
15 . The acoustic resonance diagnostic system for detecting structural degradation according to claim 11 , further comprising a hand-held device signal-connected to the acoustic resonance diagnostic module.
16 . The acoustic resonance diagnostic system for detecting structural degradation according to claim 11 , wherein the training model, comprising:
performing a normalization treatment on the at least two training acoustic signals, determining whether the at least two training acoustic signals is a transient signal whose waveform changes dramatically or is a steady signal whose waveform is stable and gentle;
selecting signals from the at least two training acoustic signals, and inputting the plurality of steady signals to a deep autoencoder based on a deep convolutional network, extracting a plurality of features and pre-selecting a plurality of feature labels; and
verifying whether the plurality of steady signals that have been treated with a compression process and a decompression process of the deep autoencoder.
17 . The acoustic resonance diagnostic system for detecting structural degradation according to claim 11 , wherein the training model, comprising:
inputting a verification sound wave signal to the convolutional neural network of the diagnostic model to be used as a verification data to test whether the diagnostic model can successfully detect a transient state.