Patent ID: 12203890

DETAILED DESCRIPTION

The present disclosure provides an acoustic resonance diagnostic method for detecting structural degradation and a system applying the same capable of sensing structural degradation at a remote end and enabling the on-site leakage inspectors to effectively interpret the state of an under-test structure and provide a prompt inspection. For the object, technical features and advantages of the present disclosure to be more easily understood by anyone ordinary skilled in the technology field, a number of exemplary embodiments are disclosed below with detailed descriptions and accompanying drawings.

It should be noted that these embodiments are for exemplary and explanatory purposes only, not for limiting the scope of protection of the invention. The invention can be implemented by using other features, elements, methods and parameters. The preferred embodiments are merely for illustrating the technical features, not for limiting the scope of protection. Anyone skilled in the technology field of the invention will be able to make suitable modifications or changes based on the specification disclosed below without breaching the spirit of the invention. The identical elements of the embodiments are designated with the same reference numerals.

Referring toFIG.1, a configuration diagram of an acoustic resonance diagnostic system10for detecting structural degradation according to an embodiment of the present disclosure is shown. The acoustic resonance diagnostic system for detecting structural degradation10includes a sound wave sensing unit11, an acoustic resonance diagnostic module12, and a communication module13used to signal-connect the sound wave sensing unit11to the acoustic resonance diagnostic module12.

The sound wave sensing unit11is used to capture an under-test sound wave signal14kfrom an under-test section14sof an under-test structure14. In some embodiments of the present disclosure, the under-test structure14can be realized by (but is not limited to) a pipe structure, such as oil pipe, water pipe or other pipe structure for transporting liquid or gas. The under-test structure14can be realized by a solid structure, such as floor structure, road filling structure, steel-bone structure, or other structure capable of generating an acoustic signal through acoustic resonance. In one embodiment, the sound wave sensing unit11can capture an under-test sound wave signal14kfrom the under-test section14sof the under-test structure14through non-contact or from a distance. In one embodiment, the sound wave sensing unit11can capture an under-test sound wave signal14kfrom the under-test section14sof the under-test structure14through direct contact or indirect contact.

In one embodiment of the present disclosure, the sound wave sensing unit11can be realized by (but is not limited to) a portable high-sensitivity piezoelectric sensor, with which the leakage inspectors can detect different positions of the under-test structure14(pipe structure) to capture the under-test sound wave signal14kfrom the under-test structure14(pipe structure). In the present embodiment, the under-test sound wave signal14kcan be realized by (but is not limited to) a time waveform.

The sound wave sensing unit11does not directly contact the under-test structure14(pipe structure) but is separated from the under-test structure14(pipe structure) by a distance, that is, the sound wave sensing unit11does not contact the under-test structure14. In one embodiment, the sound wave sensing unit11measures the under-test structure14through direct contact or indirect contact. In another embodiment of the present disclosure, the sound wave sensing unit11can be realized by (but is not limited to) several acoustic sensors directly fixed at different positions or sections of the under-test structure14(pipe structure). The sound wave sensing unit11further includes a global positioning system (GPS) for positioning the captured under-test sound wave signal14kand further transmitting the captured under-test sound wave signal14kto the acoustic resonance diagnostic module12or the control center through wired or wireless communication of the communication module13to be stored in the database15.

The acoustic resonance diagnostic system10for detecting structural degradation further includes a signal filter16used to perform a filtering step to obtain a frequency band from each sound wave signal of the under-test structure14(pipe structure). In one embodiment of the present disclosure, the filtering step performed by the signal filter16includes: performing a time domain to frequency domain conversion to convert the time waveform of each sound wave signal into a frequency-domain waveform; capturing a part of the frequency band of the frequency-domain waveform for the acoustic resonance diagnostic module12to perform an acoustic resonance diagnosis. In some embodiments of the present disclosure, the frequency band used for acoustic resonance diagnosis is substantially between 10 Hz˜1,800 Hz and preferably is between 30 Hz˜1,600 Hz. The signal filter16performs a time domain to frequency domain conversion which converts the original time waveform of the sound wave signal14winto a frequency-domain waveform. Then, the signal filter16captures a part of the frequency-domain waveform and makes the filtered sound wave signal14whave a frequency band between 200 Hz˜700 Hz.

In one embodiment of the present, the filtering step includes subsequently performing a discrete square wave fast Fourier transform (FFT) and a Mel frequency cepstrum (MFC) analysis on the sound wave signal captured by the sound wave sensing unit11by the filter16. For example, the number of filters (signal filter16) is 30, the Mel frequency cepstrum coefficient (MFCC) is 20 dimensions, the frequency band is between 0 Hz˜44,100 Hz, the Fourier transform has 2,048 points, the size of the audio frame used in the audio file is 5 seconds (s). To avoid dramatic change between the audio frames, every two audio frames overlap by 20 milliseconds (ms). The three axes of the spectrogram200as depicted inFIG.2respectively are amplitude, frequency and time.

FIG.2is obtained as follows. After the signal is filtered by the signal filter16, the filtered signal can further be divided into 5 frequency bands according to the time axis (second), and each frequency band is further equally divided into 2,000 segments, and the frequencies and amplitudes of each segment are converted into two-dimensional vectors to form a 5 (time)×2,000 (frequency and amplitude) matrix (a collection of three-dimensional vectors). The above step is repeatedly performed on the signal data obtained through on-site inspection, and each time the measured sound wave signal is converted into an item of matrix data. At the end, about 430,000 items of 5×2,000 matrix data can be obtained. Then, the 430,000 items of 5×2,000 matrix data are stored in the database15according to the time sequence and are used as training data (serving as the training acoustic signal14tand the verification sound wave signal14v) to carry a training to establish the acoustic resonance diagnostic module12.

Besides, each sound wave signal14wmust firstly be normalized before the data of the sound wave signal14wcan be trained using a deep learning algorithm. In the present embodiment, the normalization method can be such as min-max normalization. At a particular time point n, the readings obtained through 13 times of sampling form a 13×1 vector (or one-dimensional array) x[n]∈R13×1The maximum and minimum of each reading respectively form 13×1 vectors xmin[n]∈R13×1and xmax[n]∈R13×1. The vector x[n] is normalized according to formula (1):

xnorm[n]=x[n]-xmin[n]xmax[n]-xmin[n].(1)

The normalized reading xnorm[n−1] obtained at the previous time point is subtracted from the reading xnorm[n] obtained at the current time point, using difference methods (DM), which can be expressed as formula (2):
xdiff[n]=|xnorm[n]−xnorm[n−1]|∈R13×1,  (2)

wherein, xdiffis a difference signal.

Next, the sum of the difference signal xdiffis calculated, and a threshold value is set, which can be expressed as formula (3):

∑n=216xdiff[n]T[11…1]T>threshold(3)

If the sum of the difference signal is greater than the threshold value, it can be determined that the inputted sound wave signal14wis a transient signal whose waveform changes dramatically; otherwise, it can be determined that the inputted sound wave signal14wis a steady signal whose waveform is stable and gentle. The normalized sound wave signal14wincludes a normalized training acoustic signal14tand a verification sound wave signal14v, which can represent a transient signal and a steady signal respectively.

The acoustic resonance diagnostic module12is used to perform an acoustic resonance diagnostic method for detecting structural degradation.FIG.3is a flowchart of an acoustic resonance diagnostic method using the acoustic resonance diagnostic system10as depicted inFIG.1to detect structural degradation according to an embodiment of the present disclosure. The acoustic resonance diagnostic method includes steps as follows: Firstly, a training model12tbased on a deep neural network (DNN) is built through unsupervised learning (step S31). Then, at least two training acoustic signals14t(stored in the database15) are inputted to the training model12tto carry a training (step S32) (the normalized sound wave signal14wincludes a normalized training acoustic signal14tand a verification sound wave signal14v). Then, a diagnostic model12mbased on a convolutional neural network (CNN) is built according to a result of the training (step S33). Then, the under-test sound wave signal14kis inputted to the diagnostic model12mto determine the structural degradation state of the under-test section14s(step S34).

To put it in greater details, the training model12tof the acoustic resonance diagnostic module12may include a deep autoencoder400based on a deep convolutional network.FIG.4is a block diagram illustrating the deep autoencoder400according to an embodiment of the present disclosure. The structure of the deep autoencoder400can be divided into an encoder401and a decoder402, which are respectively used to compress and decompress the training acoustic signal14t. In the present embodiment, the deep autoencoder400is built based on multiple full-connection layers, the encoder401has the maximum number of Input neurons on the inputting layer, and the number of neurons on the hidden layers of the encoder401diminishes layer by layer. Features of the original training acoustic signal14tcan be extracted through linear transformation or highly nonlinear transformation or the dimensionality reduction of the data.

The decoder402decompresses the output code of the encoder401to restore the inputted data. In other words, the input data and the output data of the deep autoencoder400would be the same. In the present embodiment, since the full-connection layers only accept the input of one-dimensional array, thus the sound wave signal14wwith a 5×2,000 input matrix, prior to being inputted to the deep autoencoder400, should be flattened as 10,000 one-dimensional arrays. For example, the number of neurons on each layer of the encoder401diminishes from 10,000 to 5,000 and 2,500. The encoder401has three continuous full-connection layers. The decoder402has three continuous full-connection layers, respectively having 2,500, 5,000 and 10,000 neurons. At last, 10,000 values are outputted.

The training of the acoustic resonance diagnostic module12includes the following steps: Firstly, 80% of the sound wave signal14wstored in the database15(for example, the data of the sound wave signal14wthat are classified as transient data14tand obtained according to formulas (2) and (3)) are selected and inputted into the deep autoencoder400of the training model12tfor extracting feature values through the encoder401, whereby a plurality of representative features Z can be extracted from the original training acoustic signal14tand several feature labels12bare pre-selected. Through adjustment, it can be verified that the transient data having been treated with a compression process and a decompression process of the deep autoencoder400still possess excellent restoration performances. In the present embodiment, through the feature-extraction performed by the deep autoencoder400, the training acoustic signal14tbasically can be classified into four feature labels12b, namely, leakage frequency, metal frequency, ambient frequency (environmental frequency) and noise frequency.

Afterwards, a diagnostic model12mincluding a convolutional autoencoder is built according to the feature labels12bof the training model12tusing a convolutional neural network. The remaining 20% of the sound wave signal14w(for example, the remaining data of the sound wave signal14wthat are classified as steady data14vand obtained according to formulas (2) and (3)) are inputted to the convolutional autoencoder of the diagnostic model12mand used as verification data (also called as the verification sound wave signal14v) to test whether the diagnostic model12mcan successfully detect the transient state. The criterion for determining the transient state is whether the error between the signal restored by the convolutional autoencoder of the diagnostic model12mand the original signal is over a predetermined threshold value (the signal to noise ratio: 500). If so, the inputted training data are determined as transient data. In the present embodiment, the algorithms used by the convolutional autoencoder include the k-means clustering algorithm.

The output result of the diagnostic model12mis compared with the verification data, and the weights and the number of feature labels12bof the diagnostic model12mare adjusted to complete the training of the acoustic resonance diagnostic module12. After the training is completed, the sum of the feature values of the feature labels12bof the diagnostic model12mis equivalent to 1. In the present embodiment, the 4 feature labels respectively are: leakage frequency, metal frequency, ambient frequency and noise frequency.

After the training of the acoustic resonance diagnostic module12is completed, the under-test sound wave signal14kis inputted to the diagnostic model12mof the acoustic resonance diagnostic module12, and the pipe structure and the current structure state of the under-test section14sof the under-test structure14(pipe structure) from which the under-test sound wave signal14kis captured can be determined according to the feature value outputted by each of the feature labels12bof the diagnostic model12m.

In some embodiments of the present disclosure, when the diagnostic model12mdetermines that the under-test section14sof the under-test structure14(pipe structures) from which the under-test sound wave signal14kis captured leaks, the acoustic resonance diagnostic module12can further compare the frequency band of the under-test sound wave signal14kwith the historical data of several sound wave signals with identical pipe structures but different leakage features in terms of acoustic frequency offset and amplitude variation, wherein the historical data are stored in the database15. Thus, relative position of the structural degradation feature14din the under-test section14sof the under-test structure14(pipe structure) can be recognized, and the degeneration of the structural degradation feature14dcan be estimated.

The communication module13can be realized by a wired or wireless communication device used to signal-connect the sound wave sensing unit11to the acoustic resonance diagnostic module12for transmitting the sound wave signal captured by the sound wave sensing unit11to the acoustic resonance diagnostic module12for determination. The communication module13may be, for example, a base station of 4G/5G.

In some embodiments of the present disclosure, the communication module13may further include a plurality of hand-held devices131, respectively held by the on-site leakage inspectors or the experts at a remote end. The communication module13can transmit the sound wave signal (for example, the training acoustic signal14tand/or the under-test sound wave signal14k) captured by the sound wave sensing unit11and/or the result determined by the acoustic resonance diagnostic module12(for example, the probability of outputting the label12b) to the on-site leakage inspectors or the experts at the remote end for their reference. Since different users can real-timely get the current state of the under-test structure, the performance of on-site inspection can be effectively improved and the operation safety of the under-test structure14(pipe structure) can be assured.

Meanwhile, through the hand-held device131of the communication module13, the on-site leakage inspectors and the experts at the remote end can provide correction advice or instruction to the acoustic resonance diagnostic module12to correct or update the diagnostic model12mof the acoustic resonance diagnostic module12according to their individual authority.

In some embodiments of the present disclosure, the acoustic resonance diagnostic system for detecting structural degradation10further includes a human-machine interface17for integrating the operation procedures of the sound wave sensing unit11, the acoustic resonance diagnostic module12and the communication module13as an integrated monitoring management clouds platform. In the present embodiment, the communication module13can transmit the diagnosis result obtained by the acoustic resonance diagnostic module12, the sound wave signal (for example, the frequency tracing graph and the spectrogram) captured by the sound wave sensing unit11, the inspection position of the sound wave sensing unit11and the marking of leakage point on the map to be directly displayed on the user's computer in the form of graphs through a graphical user interface (GUI).

In some embodiments of the present disclosure, when the under-test section14sis determined to be in a leakage state, the historical data of several sound wave signals with identical pipe structure but different structural degradation (leakage) features14dcan be compared to generate a frequency tracing graph, a spectrogram, and a category diagnostic result, and the position of the structural degradation (leakage) feature14din the under-test section14scan be marked. The historical data are stored in the database15.

When the under-test section14sis determined to be in a leakage state, the frequency band of the under-test sound wave signal14khaving been processed with the time domain to frequency domain conversion will have at least one characteristic frequency (peak). Referring toFIG.5, a schematic diagram of frequency band of an under-test sound wave signal14kin a leakage state according to an embodiment of the present disclosure is shown. In the present embodiment, the under-test section14sis determined as a metal tube in a leakage state according to the label value outputted from the feature label12bof the diagnostic model12m, and the frequency band of the under-test sound wave signal14krespectively generates characteristic frequencies501aand501bat frequencies 290 Hz and 580 Hz.

Then, the characteristic amplitude values of the characteristic frequencies501aand501band their characteristic frequencies are compared with a plurality of amplitude vs position (length) curves of the under-test sections14swith identical structural degradation (leakage) feature14dbut different feature positions obtained from different under-test sections14s, that are stored in a database15, to determine the position of the structural degradation (leakage) feature14din the under-test section14s.FIG.6is a graph of several amplitude (dB) vs position (length) curves of the under-test sections14swith identical structural degradation (leakage) feature14dbut different feature positions, that are stored in the database15, according to an embodiment of the present disclosure.

In the present embodiment, an amplitude (mdB) vs position (length, meter) curve601can be obtained from the database15according to the characteristic frequencies501aand501b. The amplitude vs position (length) curve601represents an amplitude vs position (length) curve corresponding to the frequency of 580 Hz. Then, since the crest position of the curve601converted according to the characteristic amplitude value 340 dB of the characteristic frequency501bis close to the crest position at 4/8 L of the pipe length, relative position of the structural degradation (leakage) feature14dcan be marked as 4/8 L of the pipe length of the under-test section14s.

According to the characteristic amplitude values of the characteristic frequencies501aand501b, a plurality of amplitude vs degeneration curves601corresponding to specific characteristic frequencies in a database15can be compared to estimate the degeneration degree of the structural degradation (leakage) feature14d. Referring toFIG.7, a graph of several amplitude vs position (length) curves corresponding to specific characteristic frequencies, that are stored in the database15, according to an embodiment of the present disclosure is shown. The curves Q1to Q16respectively represent the amplitude vs degeneration relationship of different defect sizes. For example, for the intersection between the horizontal dotted line and the vertical dotted lines ofFIG.7, it can be estimated according to the sum of the characteristic frequency302(580 Hz) that the current structural degradation (leakage) feature14dof the under-test section14shas a degeneration degree of about 70%.

Then, through the human-machine interface17of the acoustic resonance diagnostic system10for detecting structural degradation, the acoustic resonance diagnostic result can be directly displayed on the user's computer in the form of a graph and stored in a monitoring management clouds platform. Also, through the communication module13, the on-site leakage inspectors or the experts at the remote end can real-timely grasp the current state of the under-test structure and share the inspection information and historical records.

FIG.8is a configuration diagram illustrating an acoustic resonance diagnostic system80for detecting structural degradation according to another embodiment of the present disclosure. The acoustic resonance diagnostic system80is similar to the acoustic resonance diagnostic system10as depicted inFIG.1except that the acoustic resonance diagnostic system80further includes a plurality of sound wave sensing units81a-81ffixed adjacent to the under-test structure84(pipe structure) and at least one vibration generator88.

In some embodiments, the sound wave sensing units81a-81fmay directly contact the under-test structure84(pipe structure). For example, the sound wave sensing units81may be respectively fixed on a flood meter, a valve and/or a pipeline branch of the under-test structure84(pipe structure). Alternatively, the sound wave sensing units81a-81fmay not directly contact the under-test structure84(pipe structure) but is separated from the under-test structure84(pipe structure) by a distance. For example, the under-test structure84(pipe structure) is buried underground, and the sound wave sensing units81may be disposed on the ground above the under-test structure84(pipe structure).

In the present embodiment, each of the sound wave sensing units81is fixed on one of the flood meters that is disposed in a user terminal of a pipe system, and each two adjacent ones of the sound wave sensing units81a-81are separated from each other for a distance (such as, 10 m). In an embodiment, the sound wave sensing units81a-81could be disposed on fire hydrants so as to sense leakage signals within the fire water pipes. However, the arrangement of the sound wave sensing units81and the distance between adjacent two of them are not limited. Any device that is fixed locally and can be used to capture under-test sound wave signals84kgenerated around the under-test structure84may not breach the spirit of the invention.

Each of the sound wave sensing units81a-81fhas a communication device (not shown) communicating with the communication module13and used to signal-connect the corresponding sound wave sensing unit81to the acoustic resonance diagnostic module12through the communication module13. In some embodiments of the present disclosure, the communication module13can transmit the sound wave signal84kcaptured by the sound wave sensing units81to the acoustic resonance diagnostic module12and/or the experts (through the human-machine interface17) at the remote end for their reference.

The acoustic resonance diagnostic system80further includes a data pre-processing module87using artificial intelligence (AI) to filter the data invalid for detecting structural degradation from the under-test sound wave signals84k, so as to reduce the computing load and improve the performance (such as, the diagnostic accuracy) of the acoustic resonance diagnostic module12.

For example, in the present embodiments, the data preprocessing module87applies a machine learning, such as support vector machine (SVM) learning to remove or substitute the data invalid for detecting structural degradation from the under-test sound wave signals84kfrom the three-dimensional vectors (the 5 (time)×2,000 (and amplitude) matrix as shown inFIG.2) processed by the signal filter16.

In detail, when the acoustic resonance diagnostic module12receives the under-test sound wave signal84k, the signal filter16performs a time domain to frequency domain conversion (such as, a discrete square wave fast Fourier transform (FFT) and/or a Mel frequency cepstrum (MFC) analysis) to convert the original time waveform of the under-test sound wave signal84kinto a three axes (amplitude, frequency and time) spectrogram200as depicted inFIG.2, which is regarded as 2D/3D feature planes. That is, the signal filter16transforms and splits the under-test sound wave signal84k. Then, the data preprocessing module87removes the environment noises.

The frequency-domain waveform of the under-test sound wave signal84kprocesses by the signal filter16(seeFIG.2) is then compared with the historical frequency-domain waveforms with various leakage features that are previously collected, well trained, provided by the historical data stored in the database15, wherein the historical data of several sound wave signals varies with identical pipe structures but different leakage features in terms of acoustic frequency offset and amplitude variation, so as to select a plurality of frequency segment of the frequency-domain waveform of the under-test sound wave signal84k. The historical data includes historical frequency-domain waveforms.

In some embodiments of the present disclosure, the training model12tmay classify the historical frequency-domain waveforms with various leakage features into several groups each of which has a characteristic curve representing different prototypes of structural degradation. An 2D graphic comparison process is then performed by an AI tool comparing the frequency-domain waveform of the under-test sound wave signal84kwith the that of the historical frequency-domain waveforms to select a plurality of frequency segments, wherein the characteristic curves of the selected frequency segments match portions of the characteristic curves in the historical frequency-domain waveforms.

FIG.9is a diagram illustrating the characteristic curves102and103of the historical frequency-domain waveforms with different prototypes of structural degradation (such as, the leakage of 10 mm diameter and the leakage of 25 mm in a metal tube) provided by the historical data stored in the database15and the frequency-domain waveform103of the under-test sound wave signal84k. Wherein, there are five frequency segments104-108in which the frequency-domain waveform103of the under-test sound wave signal84kmatch the characteristic curves102and103of the historical frequency-domain waveforms. The data valid for detecting structural degradation from the under-test sound wave signals84kcorresponding to the five frequency segments104-108can be thus selected form the three-dimensional vectors, as shown inFIG.2.

Subsequently, a SVM learning, using learning algorithms including classification and regression analysis is performed to classify the data involved in the five frequency segments104-108of the frequency-domain waveform103into a plurality groups, and the group having extreme values or peaks may be removed from the selected data corresponding to the five frequency segments104-108according to a predetermine threshold value.

For example,FIG.10Ais a diagram illustrating the maximum margin hyperplane11, the positive hyperplane12and the negative hyperplane13of the SVM algorithm performing in the selected frequency segment105(with the frequency range from 600 Hz to 800 Hz) and projected on a time-amplitude plane of theFIG.2. In the present embodiment, the maximum margin hyperplane11of the SVM algorithms is represented by the equation, y=0 (wherein y is the amplitude); the positive hyperplane12of the SVM algorithms is represented by the equation, y=1500; and the negative hyperplane13of the SVM algorithm of the SVM algorithms is represented by the equation, y=−1500.

As shown inFIG.10A, portions of the data in the selected frequency segment105having amplitude greater than 1500 dB or less than −1500 dB may be classified as invalid groups14(such as, the environmental frequency groups including a metal frequency group, an ambient frequency and a noise frequency group) by the SVM algorithm and then removed from the selected data valid for detecting structural degradation corresponding to the frequency segment105(seeFIG.2andFIG.10B). The other selected data invalid for detecting structural degradation can be also removed from the selected data corresponding to the other frequency segments104and106-108respectively by the same way.

It should be appreciated that, the number of the data remained in each one of the selected frequency segments104-108must greater than a threshold. In the present embodiment, any one of the selected frequency segments104-108that has remaining data less than 60%, the remained data of the corresponding frequency segment also should be voided. Alternatively, in some other embodiments, the removed data can be substituted by the data copied from the valid data in the same frequency segment. Such that the number of the data in an individual frequency segment would not be reduced after the SVM learning.

The remaining data in the selected frequency segments104-108inFIG.9are then reunion to form a fitting-three-dimensional vectors (not shown), and then transmitted to the diagnostic model12mto determine the structural degradation state of the under-test structure84(pipe structure). The structural degradation states are referred to as pipe thinning and/or leakage.

In the present embodiment, the diagnostic model12mmay compare the characteristic curves of the leaking pipes provided by the historical data stored in the database15with that of the fitting-three-dimensional vectors to determine the structural degradation state of the under-test structure84(pipe structure) and to identify the frequency bands (the frequency range104-108enclosed by the dotted frame) associated with the feature labels12b. When the under-test structure84(pipe structure) is determined to be in a leakage state, the position of the structural degradation (leakage) feature84d(the distance of the structural degradation (leakage) feature84dseparated from the working sound wave sensing units81a) can be established by the method as described inFIGS.5and6.

By removing the classified data groups that are resulted by the environment noises from the under-test sound wave signals84kcollected by the sound wave sensing units81a-81c, the data more related to the sound wave signals generated by the under-test structure84(pipe structure) can be inputted into the acoustic resonance diagnostic module12for determining the structural degradation state and the leakage position, while the computing load of the acoustic resonance diagnostic module12can be significant reduced. Therefore, the performance (such as, the diagnostic accuracy) of the acoustic resonance diagnostic module12can be improved.

However, when too few valid data are remained by the data pre-processing module87and/or when the amplitude values of the under-test sound wave signals84kcollected by the sound wave sensing units81a-81cis too low, the performance (e.g., the diagnostic accuracy) of the acoustic resonance diagnostic module12may be deteriorated. Thus, an active acoustic resonance diagnostic method triggered by the vibration generator88is provided to improve the performance (e.g., the diagnostic accuracy) of the acoustic resonance diagnostic module12.

In the present embodiment, the vibration generator88is fixed on or disposed adjacent to the under-test structure84(pipe structure) for providing at least one sound vibration on the under-test structure84(pipe structure) with an amplitude value greater than that of the sound wave signal previously collected by the sound wave sensing units81a-81c. The sound wave signals (such as, the sound of leakage) resulted by the under-test structure84(pipe structure) and the eigenvectors thereof can be reinforced. The reinforced sound wave signals and the eigenvectors thereof can be then inputted into the diagnostic model12mto perform the diagnostic process as mentioned above to determine the structural degradation state of the under-test structure84(pipe structure). Therefore, the performance (e.g., the diagnostic accuracy) of the acoustic resonance diagnostic module12can be improved correspondingly.

For example,FIG.11Ais a diagram illustrating the characteristic curves (frequency-amplitude curves)111and112of the leaking pipe provided by the historical data stored in the database15and that of the fitting-three-dimensional vectors without reinforced by the sound vibration triggered by the vibration generator88during the acoustic resonance diagnostic process.FIG.11Bare diagrams illustrating the characteristic curves (frequency-amplitude curves111and113) of the leaking pipe provided by the historical data stored in the database15and that of the fitting-three-dimensional vectors with reinforced by the sound vibration triggered by the generator88during the acoustic resonance diagnostic process.

It can be indicated that the amplitude values within the selected frequency sections (the frequency range enclosed by the dotted frame)115and116associated with the feature labels12bfor determining the structural degradation state can be significantly reinforced by the sound vibration triggered by the vibration generator88.

In some embodiment, the sound vibration triggered by the vibration generator88may have a frequency114(the frequency range enclosed by the dotted circle) to form a standing wave in the under-test structure84(pipe structure). The frequency114of the standing wave can be estimated using the following equation:

fn=2⁢nVL
wherein fnis the frequency of the standing wave; L is the distance between the working sound wave sensing units81aand the vibration generator88; V is speed of sound; and n is the mode number of the standing wave.

Subsequently, when the under-test structure84(pipe structure) is determined to be in a leakage state, an amplitude (mdB) vs position (length, meter) curve (such as the position curve601depicted inFIG.6) can be obtained from the database15according to the frequency114of the standing wave; and the leakage position can be identified by referring the amplitude of the characteristic frequencies that occurs in the frequency band associated with the leakage (as depicted inFIG.5) to the position (length) inFIG.6. Thereby, a position of the structural degradation (leakage) feature84drelated of the distance L between the working sound wave sensing units81aand the vibration generator88can be marked.

Alternatively, in some embodiments of the present disclosure, the relative position of the structural degradation (leakage) feature84dcan be marked by referring to the relationship between sound pressure change rate and the unit wavelength of the standing wave.

For example,FIG.12Ais a bar chart illustrating4sound pressure change rate of the sound vibrations with different frequencies (73.4 Hz, 95.1 Hz, 96 HZ and 111.5 HZ) and the relative positions of the leakage features (i.e. the distance of 29.82 m, 43.16 m, 68.76 m and 93.26 m that are measured form the vibration generator88to the leakage features) that cause the sound pressure change.FIG.12BtoFIG.12Eare diagrams illustrating the relationship between sound pressure change rates and unit wavelengths of the standing waves with different frequencies (73.4 Hz, 95.1 Hz, 96 HZ and 111.5 HZ) respectively.

According toFIG.12AtoFIG.12E, it can be proved that the distance (29.82 m, 43.16 m, 68.76 m and 93.26 m) can be estimated by referring to the relationship between sound pressure change rate and unit wavelength of the standing wave; and the distance measured from the vibration generator88to the leakage feature can be estimated a N×λ; wherein λ is the unit wavelength of the standing wave; and N is a real number greater than 1.

In some embodiments of the present disclosure, the active acoustic resonance diagnostic method is initiated and controlled by the human-machine interface17. For example, in the present embodiment, when the number of the three-dimensional vectors filtered by the signal filter16are removed by the data pre-processing module87greater than 60% or the amplitude values of the under-test sound wave signals84kcollected by the working sound wave sensing units81ais lower than 20 dB, the vibration generator88adjacent to the working sound wave sensing units81acan be activated to provide the vibration according to the instruction that is filed by the human-machine interface17and transmitted through the through wired or wireless communication of the communication module13.

It should appreciate that, the number of the vibration generator88and the position on which the vibration generator88is disposed may not be limited, and any device, component that can trigger a vibration on the under-test structure84(pipe structure) according to the instruction of the human-machine interface17may not breaching the spirit of the invention.

As disclosed in above embodiments, the present disclosure provides an acoustic resonance diagnostic system and an acoustic resonance diagnostic method for detecting structural degradation capable of real-timely remotely detecting the degradation state of an under-test structure (such as pipe thinning and leakage) using a sound wave signal through contact or non-contact. The dynamic audio capturing module remotely captures the acoustic vibration generated by the under-test structure (such as the pipe wall), senses the change in the stiffness and quality of the under-test structure, and integrates the acoustic vibration to the acoustic resonance diagnostic module through the IoT technology and the cloud computing to build a diagnostic model using a deep learning algorithm, and further synchronically performs leakage recognition, leakage diagnosis and leakage positioning on the under-test sound wave signal to remotely monitor the degradation state.

Furthermore, the acoustic resonance diagnostic module is communication-connected to a plurality of hand-held devices or backend platforms, such that different users can real-timely get a real-time information of the under-test structure (pipe) to help the on-site leakage inspectors interpreting the state of the under-test structure (pipe) more effectively and provide a prompt inspection. Meanwhile, engineers can remotely sense the current state of the structure (pipe) and correctly detect the leakage without visiting the site in person and checking the audio with a stethoscope. Thus, human errors or misjudgments can be reduced, and the operation safety of the under-test structure (pipe) can be assured.

It will be apparent to those skilled in the art that various modifications and variations can be made to the disclosed embodiments. It is intended that the specification and examples be considered as exemplary only, with a true scope of the disclosure being indicated by the following claims and their equivalents.