WO2017199455A1 - Dispositif de détermination de fuite d'eau et procédé de détermination de fuite d'eau - Google Patents

Dispositif de détermination de fuite d'eau et procédé de détermination de fuite d'eau Download PDF

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Publication number
WO2017199455A1
WO2017199455A1 PCT/JP2016/084359 JP2016084359W WO2017199455A1 WO 2017199455 A1 WO2017199455 A1 WO 2017199455A1 JP 2016084359 W JP2016084359 W JP 2016084359W WO 2017199455 A1 WO2017199455 A1 WO 2017199455A1
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Prior art keywords
determination
water leakage
data
sound wave
sound
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English (en)
Japanese (ja)
Inventor
貴博 蛭間
西村　修
江波戸　明彦
佐藤　義之
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Toshiba Corp
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Toshiba Corp
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Priority to JP2018518067A priority Critical patent/JP6790086B2/ja
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M3/00—Investigating fluid-tightness of structures
    • G01M3/02—Investigating fluid-tightness of structures by using fluid or vacuum
    • G01M3/04—Investigating fluid-tightness of structures by using fluid or vacuum by detecting the presence of fluid at the leakage point
    • G01M3/24—Investigating fluid-tightness of structures by using fluid or vacuum by detecting the presence of fluid at the leakage point using infrasonic, sonic or ultrasonic vibrations

Definitions

  • Embodiments of the present invention relate to a water leakage determination device and a water leakage determination method.
  • the current procedure for investigating water leakage using a water leak detector is that a water meter meter or other investigator first obtains data for a water pipe leak survey using the water leak detector during meter reading. For example, a time integration rate (a ratio of a signal exceeding a certain level in a certain time) and a vibration sound (leakage sound) are recorded as a sound wave file. Each sound file is associated with a recording point (position of a water pipe).
  • the secondary judgment is a hearing judgment by the survey engineer and depends on the experience and skills of the engineer. Even when the number of sound wave files obtained by the primary determination is enormous, it is necessary to listen to all sound sources, which requires a lot of labor.
  • the problem to be solved by the present invention is to provide a water leakage determination device and a water leakage determination method for more efficiently and accurately detecting water leakage.
  • the water leakage determination device of the embodiment includes a reception unit that receives sound wave data, an extraction unit that extracts data of a steady section with less noise from the sound wave data, a designation unit that specifies a threshold value, and water leakage from the data of the steady section.
  • a determination unit that obtains a ratio including a signal indicating characteristics and compares the ratio with the threshold value to determine the presence or absence of water leakage.
  • the water leakage determination method of the embodiment is a water leakage determination method in a water leakage determination device that determines the presence or absence of water leakage from sound wave data, and extracts data of a steady section with less noise from the sound wave data, and determines the presence or absence of water leakage A threshold value is designated, and a ratio including a signal indicating the characteristic of water leakage is obtained from the data of the steady section, and the presence or absence of water leakage is determined by comparing the ratio with the threshold value.
  • FIG. 6 is a diagram showing combinations of first to fourth determination means.
  • FIG. 1 is a block diagram illustrating a configuration of a water leakage determination device 1 according to the first embodiment.
  • the water leakage determination apparatus 1 includes a reception unit 2 that receives sound wave signal data, an extraction unit 3 that extracts a steady section with less noise from the input sound wave signal data, and a sound wave signal extracted by the extraction unit 3 It has the determination part 4 which determines whether the characteristic of water leakage is contained in data using two determination means, and the output part 5 which outputs a determination result. Furthermore, the designation
  • the reception unit 2 is a location where, for example, acoustic signal data such as flowing water flowing through a water pipe measured by a water meter meter reader or an investigator is input.
  • the sound wave signal data may be data represented by the WAV format that can be handled by a general personal computer / electronic device. Also, any sound wave signal data in another format may be used as long as it can be converted into the WAV format. For example, compressed data such as ADPCM, MP3, and WMA may be used.
  • the accepting unit 2 may accept a file directly by USB or the like, or may accept data via an external medium such as an SD card. Further, data may be received via wireless communication such as Wi-Fi or Bluetooth.
  • the sound wave signal data includes many noises such as traffic noise from cars and trains, outdoor noise from air conditioners, and human voices due to the influence of the surrounding measurement environment. It is.
  • the sound wave signal data is mainly given as a time series waveform.
  • the water leaking sound from a water pipe is a steady sound including a frequency component up to 3 kHz and a frequency component of 1 kHz to 2 kHz being dominant.
  • Sound waves that are likely to be misjudged as leaked sounds include the sound of water flowing out of a faucet when using water supplies, and the rotating sound of water meters.
  • sound waves caused by a motor such as an air conditioner can be used. The frequency characteristics in the state where these noises are mixed are greatly deviated from the original frequency characteristics of the leaked sound.
  • the extraction unit 3 is a part that extracts a waveform of a component with less noise from the sound wave signal data input from the reception unit 2. That is, out of the time-series waveform of the sound wave signal data, a sudden vibration component having a large amplitude or a waveform in a steady section that is not a waveform portion having a large amplitude that is periodically superimposed on the waveform is extracted.
  • FIG. 2 (a) is a diagram showing a time-series waveform when a leak sound is detected.
  • the horizontal axis is time (seconds), and the vertical axis is the signal level that is amplitude.
  • FIG. 2B shows frequency characteristics when a time series waveform of 10 seconds, which is the entire time, is subjected to fast Fourier transform (FFT).
  • FFT fast Fourier transform
  • the frequency characteristics obtained by performing the FFT using a waveform having a low noise for example, a steady section of 2 seconds, avoiding a waveform having a large noise of about 1.6 sec in the time series waveform of FIG.
  • a clear feature is confirmed in the range of 1 kHz to 2 kHz.
  • FIG. 3 (a) is a diagram showing a time-series waveform when traffic noise is detected. Here, it is assumed that the water leakage sound is not superimposed.
  • FIG. 3B shows the frequency characteristics when the time series waveform of 10 seconds, which is the entire time, is FFTed.
  • FIG. 3C shows the frequency characteristics when FFT is performed using the waveform at the designated portion that is the stationary section of the time series waveform of FIG. It can be seen that no clear feature has been confirmed between 1 and 2 kHz as compared with the waveform of FIG.
  • a spectral entropy method can be used as a method for extracting a stationary section.
  • the spectrum entropy method is a feature quantity representing the whiteness of a signal, which is obtained by calculating a signal spectrum as a probability distribution and assuming information entropy.
  • the spectrum such as white noise is uniform, the value is high, and when the spectrum is non-uniform such as a voice signal or traffic noise, the value is low.
  • the spectral entropy method is generally represented by the following formula (1).
  • S f is the amplitude spectrum of the frequency component f obtained by FFT of the input signal.
  • P f is the existence probability of the frequency component f.
  • H indicates spectral entropy. H is a high value for a white signal with a uniform spectrum, and a low value for a colored signal with a non-uniform spectrum such as an audio signal or traffic noise.
  • FIG. 4 shows a time-series waveform and a waveform obtained by converting the time-series waveform by the spectral entropy method.
  • the steady section of the graph shown by the spectral entropy method is flat at a high value, and the part of the external fluctuation noise has a low value. It is possible to clearly distinguish the stationary section and the external fluctuation noise part.
  • ⁇ This extracts only the components that are regarded as stationary sections, avoiding local fluctuation noise points.
  • the extraction of the steady section is performed using a threshold value. That is, a section that exceeds the set threshold and is regarded as a steady section is extracted.
  • the threshold for drawing the steady section can be arbitrarily set.
  • the time width to extract can also be set arbitrarily.
  • the sound wave signal data is continuous. This is because if the continuity of the time-series waveform of the sound wave signal data is lost, a different spectrum is obtained when FFT is performed.
  • the total of the stationary sections that are the time to be extracted is 1 second
  • it may be extracted in a fractional manner for a total of 1 second.
  • FFT Fast Fourier Transform
  • the threshold value to be set may be lowered. As a result, the section determined to be equal to or greater than the threshold becomes long, and it is easy to extract a continuous signal that is regarded as a steady section.
  • FIG. 5 is a diagram illustrating an example of extracting a stationary section from a target time-series waveform using a spectrum entropy method.
  • the first row in the figure is the target time series waveform. From this, a stationary section with a specified time width is extracted.
  • the second row in the figure is a waveform converted by the spectral entropy method.
  • a broken line is an arbitrarily determined threshold value.
  • the third row in the figure shows the determination result when the section above the threshold is 1 and the section below the threshold is 0.
  • a portion of “1” that is a section equal to or greater than the threshold is a place extracted as a steady section. From this, a section having an arbitrarily set time width is extracted. If there is no section longer than an arbitrary time width and a continuous steady section is required, the threshold may be set low.
  • the threshold value for extracting the stationary interval may be the total average value of the spectral entropy of the extraction target interval, the frequency distribution may be calculated and the median value may be used, or any numerical value determined by the user May be given directly.
  • Extraction by the extraction unit 3 is mainly performed by a CPU (Central Processing Unit) of a computer.
  • CPU Central Processing Unit
  • the determination unit 4 determines whether or not a feature due to water leakage is included in the steady section of the sound wave signal data extracted by the extraction unit 3.
  • FIG. 6 is a block diagram illustrating details of the determination unit of the water leakage determination device according to the present embodiment.
  • the presence or absence of water leakage is determined by the second determination unit 8 different from the first determination unit 7 and the first determination unit, using the time series waveform of the steady section. By combining these determination results, secondary determination of the sound wave signal data can be easily performed.
  • the first determination means performs FFT on the sound wave signal data in the stationary section extracted by the extraction unit 3, and calculates the ratio of the frequency component due to water leakage in an arbitrarily defined frequency range. Furthermore, the presence or absence of water leakage is determined by setting a threshold value for this ratio.
  • FIG. 7 shows an example when an arbitrary frequency range including a frequency component due to water leakage is set from the FFTed waveform. Since the frequency component resulting from water leakage is characterized by a range of approximately 1 kHz to 2 kHz, for example, a range of approximately 700 Hz to 3 kHz is set as the frequency range.
  • the Euclidean distance is calculated as the calculation of the ratio of frequency components due to water leakage.
  • the Euclidean distance D of the frequency characteristic of the sound wave signal data in the steady section extracted by the extraction unit is obtained with respect to the frequency characteristic when the sound wave input level is minimum, that is, the lower limit data of the dynamic range.
  • the Euclidean distance D is obtained by the following equation (2).
  • X i is the frequency characteristic data of the steady section extracted by the extraction unit.
  • Pi is frequency characteristic data at the lower limit of the dynamic range.
  • i is an array of the 1st to d-th frequencies.
  • the frequency characteristic data of the lower limit (minimum value) of the dynamic range is interpreted as so-called background noise frequency characteristic data.
  • the water leakage is determined. If the amplitude value is only the sound wave signal data of the stationary section extracted by the extraction unit, the frequency component that shows a large amplitude as an absolute value is large even in the lower limit data of the dynamic range, and may not actually be a large input. It is preferable to take a difference between the frequency characteristic of the stationary section and the lower limit data of the dynamic range.
  • the method of determining the lower limit data (minimum value) of the dynamic range may use a sound wave signal recorded in a place where background noise is low, or extract the lowest value of each frequency of a plurality of sound wave signal data to be input, The lower limit data may be combined by combining them.
  • the determination unit When determining the minimum value of the dynamic range from a plurality of sound wave signal data to be input, all the files are read once before entering the judgment unit block. FFT is performed each time a file is read, and the dynamic range information already stored for each frequency is compared with the FFT value. If the value is small, the dynamic range information is overwritten. This is performed on the entire sound wave signal data file to determine the minimum value of the dynamic range.
  • the determination unit stores the obtained frequency characteristic of the lower limit of the dynamic range in a storage unit such as a memory, and calculates the Euclidean distance according to Equation (2) using the stored lower limit data.
  • an integral value in an arbitrary frequency range with respect to a frequency characteristic obtained by FFTing a stationary section of sound wave signal data and an integrated value in an arbitrary frequency range with respect to a frequency characteristic having a minimum dynamic range.
  • the area of the difference between the frequency characteristic obtained by performing FFT on the steady section and the frequency characteristic having the minimum dynamic range is the ratio between the case where the entire frequency range is used and the case where the frequency range is an arbitrary frequency range.
  • the determination of the threshold value can be arbitrarily set by the user based on past water leakage judgment data and empirical rules.
  • the first determination means performs a smoothing process as an approximation method of frequency characteristics in order to reduce the data processing amount before performing the threshold determination based on the Euclidean distance. This is because the amount of data processing becomes enormous when performing FFT simply.
  • FIG. 8 is a diagram in which the frequency characteristics in the case of 1/3, 1/6, and 1/12 octave are overwritten.
  • the 1/12 octave band is the most detailed graph with the most data points.
  • the value of N is preferably determined by a trade-off with the number of data points.
  • FIG. 9 is a comparison diagram between the case of smoothing by moving average and the case of smoothing by 1/12 octave band.
  • the peak portion becomes dull due to the averaging process of a plurality of points, but smoothing in the 1/12 octave band can clearly reproduce the peak shape.
  • 1 / N octave-processed data instead of simple frequency conversion data at the time of determination based on the Euclidean distance, high-speed computation can be performed with a small number of data points while leaving appropriate frequency information.
  • the designation unit 6 in the first determination means inputs the upper and lower frequencies of an arbitrary frequency range.
  • the frequency component due to water leakage is 1 kHz or more and 2 kHz or less, and therefore the frequency range is set in the range of 700 Hz or more and 3 kHz or less, for example.
  • the values of the upper limit frequency and the lower limit frequency can also be determined from a plurality of sound wave signal data that is surely a water leakage sound.
  • a frequency with a large contribution may be extracted from an average value of frequency-converted ones, or a frequency can be obtained by calculating a frequency distribution.
  • the designation unit 6 designates a threshold value (%) for water leakage determination with respect to the Euclidean distance D (%).
  • An arbitrary value can be designated as the threshold value, and the threshold value can be obtained from the relationship between the frequency range and the Euclidean distance D from a plurality of sound wave signal data that is clearly a water leakage sound. From the average value of the values of D when the frequency range is changed, a threshold that can be determined as water leakage can be inferred.
  • the specification of parameters by the specification unit 6 may be performed using an external terminal such as a PC or a mobile phone, or may be directly input by attaching a monitor or a touch panel.
  • an external terminal such as a PC or a mobile phone
  • data is transferred using the Internet, Wi-Fi, Bluetooth, or the like.
  • you may specify using the display etc. of the output part 5 mentioned later.
  • the second determination means 8 of the determination unit 4 is means for determining water leakage mainly in a time series region.
  • a high-pass filter (HPF) is applied to the sound wave signal data in the stationary section extracted by the extraction unit 3, and the ratio (time integration rate) of the total time of signals having a determination amplitude E or higher that is arbitrarily set.
  • HPF high-pass filter
  • the time interval of the sound wave signal data for performing water leakage determination can be arbitrarily selected.
  • the time integration rate (%) is expressed by the following equation (3).
  • FIG. 10 is a time-series waveform of the sound wave signal data after the HPF is applied. An example is shown in which the total time exceeding arbitrarily determined amplitude ⁇ E is calculated as a time integration rate (%).
  • the frequency characteristics of the septic tank sound and motor sound that are likely to be erroneously detected by water leakage sound mainly consist of peaks in the drive power supply frequency and its multiple frequencies, and greatly affect low frequency components.
  • FIG. 11 is a diagram showing frequency characteristics of septic tank sound and motor sound. Both frequency characteristics have a peak at a low frequency.
  • the frequency characteristics of the water leakage sound are different because they have characteristics in a relatively high frequency range of 1 kHz to 2 kHz.
  • the HPF the low frequency power supply driving frequency and its multiple frequency components that are likely to be erroneously detected are reduced, and the septic tank sound and the motor sound are hardly detected with the time integration rate. As a result, it is possible to narrow down the number of suspected water leaks. Note that it is possible to arbitrarily determine which frequency of the power supply driving frequency is reduced.
  • the designation unit 6 of the second determination means specifies the value of the determination amplitude E described above.
  • a threshold value (%) for determining whether there is water leakage is specified for the time integration rate (%).
  • the power supply frequency is input. Since there are two power supply frequencies in Japan, the 50 Hz region and the 60 Hz region, the power frequency is selected depending on the measurement location. By HPF, it is possible to arbitrarily input up to what order component of the power supply frequency the peak sound is reduced.
  • the water leakage determination unit of the determination unit 4 two types are described as the water leakage determination unit of the determination unit 4, but these determination units may be applied individually or in series or in parallel. It may be applied. In the case of determining in series, for example, after the presence or absence of water leakage is determined by the first determination means, the presence or absence of water leakage is determined again by the second determination means for the sound wave signal data determined to have water leakage. Which determination means is performed first can be arbitrarily set. When applied in parallel, the determination results of the first determination unit and the second determination unit may be expressed by either logical sum or logical product. As the determination result, the logical product is a stricter determination result.
  • each of the first determination means and the second determination means may be determined alone or in combination in series or in parallel.
  • the determination result in the determination unit is output to the output unit.
  • the execution of the determination algorithm in the determination unit is mainly performed by a CPU of a computer.
  • FIG. 12 shows the result when water leakage is determined in the water pipe using the first determination means and the second determination means.
  • the investigator performed the judgment by the algorithm of the first judging means and the second judging means for the 103 sound wave signal data that were primarily judged by the detector.
  • the leaked water data for answering was 24 cases that were judged as leaking by hearing from 103 cases.
  • FIG. 13 shows the determination threshold value of the first determination means, the determination threshold value of the second determination means, and the determination amplitude value (voltage).
  • the determination threshold of the first determination means was set to 20%, and more than that was determined to be water leakage.
  • the determination threshold value of the second determination means was set to 40%, and more than that was determined to be water leakage.
  • the threshold setting the one with the highest determination accuracy was selected from past water leakage determination data and empirical rules.
  • the determination amplitude value was determined by fitting from the detector data. The determination threshold value and the determination amplitude value can be arbitrarily selected according to the measurement object and the measurement environment.
  • the output unit 5 is a part that outputs the determination result of the determination unit 4.
  • the output unit 5 may be a display for displaying a figure or a table.
  • a display of a portable terminal such as a personal computer, a notebook computer, or a mobile phone, a tablet terminal, or the like may be used.
  • the determination result data may be received and displayed using the Internet, Wi-Fi, Bluetooth, or the like.
  • the display of the output unit 5 is displayed as table data as shown in FIG.
  • each determination means may be displayed in the column direction, and the number of water leakage determinations or comparison data with the audibility determination may be displayed in the row direction.
  • you may display with a pie chart, a bar graph, etc. instead of table
  • the threshold value of the specifying unit 6 may be specified on the display of the output unit 5 or the like.
  • FIG. 14 is a block diagram illustrating a detailed configuration of the determination unit of the water leakage determination device 1 according to the second embodiment.
  • the water leakage determination device includes third determination means in the determination unit.
  • the other configuration is the same as that of the water leakage determination device of the first embodiment.
  • 3rd determination means detects the presence or absence of a repetitive sound of a fixed period with respect to the sound wave signal data of the stationary section extracted by the extraction unit. This is effective for discriminating sound waves that are likely to be erroneously determined as leaking sounds.
  • FIG. 15 is a comparison of time-series waveforms and frequency characteristics of water leakage sound and water water sound (sound when water is used).
  • FIG. 15 (a) shows a time series waveform of leaked sound and its frequency characteristics.
  • FIG. 15B shows a time-series waveform of the used water sound and its frequency characteristics.
  • Both the water leak sound and the water noise used have a characteristic that the frequency characteristic includes a frequency component up to 3 kHz and the contribution of the component of 1 kHz to 2 kHz is large, so that it is difficult to distinguish the water sound from the frequency characteristic.
  • the time-series waveform is characterized in that the water leakage sound is a time-series waveform such as white noise (random noise), while the water noise used is a repetitive sound with a fixed period.
  • white noise random noise
  • the water used has such characteristics because the water meter, which is the recording location, rotates when the water supply is used, so that such a sound is easily superimposed.
  • a method of detecting the time series waveform of meter rotation sound stored in advance in a database by comparing it with the time series waveform of the input sound wave signal data, or for the input sound wave signal data For example, a method of extracting by examining the similarity of waveforms by dividing each window length. In this case, the overlap amount of the window length can be arbitrarily set.
  • sound types that cannot be distinguished from each other in the frequency characteristics can be determined from the time-series waveform.
  • the third determination means may be applied not to the sound wave signal data of the stationary section extracted by the extraction unit but to the sound wave signal data before being extracted by the extraction unit. This is because it is conceivable that repeated signals are removed through the extraction unit.
  • the third determination means of the determination unit may be used alone. Further, it may be used in combination with the first determination unit or the second determination unit according to the first embodiment.
  • the third determining means is preferably applied to the sound wave signal data that could not be determined as water leakage by the first and second determining means. For example, when used in a combined determination with the first and second determination means, it is preferably used in the first or last flow of the serial determination.
  • the pattern of FIG. 16 can be considered.
  • the determination using the first to third determination means a total of 38 combinations can be considered including the single determination. It is good to select the combination of the determination means according to the situation which performs water leak determination. For example, when the number of data to be determined is large and it is desired to narrow down to as few as possible, the first to third determination means may be applied in parallel to output the logical product of the determination results. In addition, in the case of an area where tap water is frequently used in advance, the third determining means is applied first, and then the first and second determining means are applied in parallel to output the logical product of the results. May be.
  • the threshold value used by the first determination unit and the second determination unit is determined by the user in advance, it may be automatically set in the apparatus. In that case, it is good to set using the database which stored multiple sound wave signal data at the time of past water leakage.
  • the algorithm (first and second determination means) according to the present embodiment is applied to each of the past sound wave signal data to reversely calculate the threshold value of each determination means.
  • the back-calculated threshold value, frequency characteristics, and time series waveform after HPF are stored in a database in a state linked to past sound wave signal data.
  • a threshold value associated with the similar waveform is applied. If there is no similar waveform, the average value of the threshold values in the database is applied. Thereby, a threshold value can be determined for each sound wave signal data.
  • a correlation coefficient may be calculated, or general machine learning may be used.
  • Machine learning is a logic constructed using an algorithm that solves a classification problem. Examples of these algorithms include decision trees, random forests, SVM (Support Vector Machine), and neural networks. An algorithm combining these algorithms may be used.
  • the database is stored in a hard disk, USB memory, ROM or the like. Further, the sound wave signal data at the time of past water leakage may be acquired by having a database in an external server or the like and accessing it via the Internet or the like.
  • FIG. 17 is a block diagram illustrating a detailed configuration of the determination unit 4 of the water leakage determination device 1 according to the third embodiment.
  • the water leakage determination device includes a fourth determination unit 10 in the determination unit 4.
  • the other configuration is the same as that of the water leakage determination device of the first embodiment.
  • the fourth determination means 10 calculates an autocorrelation function for the sound wave signal data (also referred to as a time-series waveform) in the stationary section extracted by the extraction unit 3 and compares it with the threshold value specified by the specification unit 6. Determine the presence or absence of water leakage. Specifically, an envelope of the autocorrelation function is obtained, and a predetermined predetermined line segment is compared with the envelope. A ratio at which the envelope becomes larger than a predetermined line segment is obtained and compared with a threshold value designated by the designation unit 6. For example, the case where the ratio becomes larger than a threshold value is determined as sound wave signal data with water leakage.
  • the predetermined line segment and the threshold value are arbitrarily determined based on empirical rules, past water leakage data, and the like.
  • the predetermined line segment is not necessarily limited to a straight line, and includes a curve and the like. Further, the comparison with the threshold value is not limited to a ratio, and may be an area formed by an envelope or an inclination of the envelope.
  • the determination unit in FIG. 17 may include third determination means.
  • the autocorrelation function is an evaluation of the degree of coincidence between signal data of two sections cut out from the same sound wave signal data.
  • the presence or absence of water leakage is determined by evaluating the degree of coincidence of the periodic components included in the sound wave signal data in the steady section.
  • An envelope is a line segment that shares a tangent with a plurality of curves, and takes a straight line or a curved shape depending on the type of the curve in contact.
  • an envelope of a plurality of curves having vertices includes a line segment that connects the vertices.
  • the following formula (4) is a formula showing an autocorrelation function.
  • x (n) is an input signal
  • r (m) is an autocorrelation function
  • N is an autocorrelation data length.
  • FIG. 18 shows an example of a detailed block diagram of the fourth determination means 10.
  • an autocorrelation function of sound wave signal data in a stationary section is obtained.
  • an absolute value is obtained for the obtained autocorrelation function, and the calculation range is limited to the positive time direction.
  • an envelope for the autocorrelation function is derived.
  • the Hilbert transform is used to calculate the envelope.
  • linear approximation is performed on the envelope. When this approximate line is regarded as a linear function, it is preferable to obtain it by the least square method. Water leakage is determined based on the obtained approximate straight line.
  • FIG. 19 shows an example of a determination procedure when a test signal is used.
  • FIG. 19A shows a time-series waveform of a 50 Hz Sin wave.
  • FIG. 19B is a graph of the autocorrelation function derived based on Expression (4).
  • FIG. 19C is a graph showing the absolute value of the autocorrelation function. Furthermore, it is limited to a graph of time in the positive direction. As shown in FIG. 19D, in the graph of the time in the positive direction, the inclination becomes lower right and tends to converge to 0 as the time increases. An envelope is derived for this graph. Further, an approximate straight line of this envelope is taken.
  • FIG. 19D shows a graph of time in the positive direction.
  • 19E is a graph showing an approximate straight line between the waveform of the autocorrelation function and the envelope. Since the graph shape is a target in the positive and negative time directions, a graph in the negative time direction may be used as the calculation range. In that case, since the start point changes from 0 on the time axis in the negative direction, the slope of the graph is downward. In the case of a single frequency time-series waveform as shown in FIG. 19A, the shape of the obtained envelope and the approximate straight line are almost the same. Since there are signals of a plurality of frequency components, the envelope and the approximate straight line are generally different in shape.
  • FIG. 20 shows an example of a determination procedure in the case of a time-series waveform including a fluctuating sound.
  • the time-series waveform including the fluctuating sound is a time-series waveform whose amplitude is locally increased at a predetermined time.
  • FIGS. 20B and 20C are signals obtained by obtaining an autocorrelation function of the time series waveform and taking an absolute value.
  • FIG. 20 (d) shows the result of deriving the envelope of the waveform of FIG. 20 (c).
  • the envelope in this case has a curved shape, and therefore has a shape different from the approximate straight line of the envelope shown in FIG. 20 (e).
  • FIG. 21 shows an example of a time-series waveform when a signal having a plurality of frequency components is included.
  • FIG. 21A shows a 50 Hz sine wave
  • FIG. 21B shows a time-series waveform of a 1 kHz sine wave
  • FIG. 21C shows a time series waveform in which a 50 Hz sine wave (amplitude ratio 50%) and a 1 kHz sine wave (amplitude ratio 20%) are superimposed.
  • FIG. 21 (d) shows a time series waveform of a random sound that is white noise.
  • FIG. 21E shows a waveform in which white noise and a 50 Hz sine wave (amplitude ratio 50%) are superimposed.
  • FIG. 21F shows a waveform in which white noise and a 50 Hz sine wave (amplitude ratio 20%) are superimposed. It is a waveform.
  • FIGS. 22 (a) to 22 (f) are derived from the autocorrelation functions of the time series waveforms of FIGS. 21 (a) to 21 (f), their respective envelopes and approximate lines.
  • FIGS. 22 (a) and 22 (b) the time series waveforms in FIGS. 21 (a) and 21 (b) show a sharp drop in the autocorrelation function, while the white waveform in FIG. In the time series waveform of noise, the value of the autocorrelation function is remarkably small, and the slope of the envelope is almost zero.
  • the magnitude of the slope of the graph changes depending on the mixing ratio of the amplitude.
  • the water leakage sound has the characteristics of white noise (random sound).
  • periodic components such as sine waves are dominant in the septic tank sound and motor sounds of vending machines and outdoor units.
  • the autocorrelation function of the sound wave signal data indicating a single peak sound has a slope in which the envelope or approximate line has a downward slope.
  • the autocorrelation function of the sound wave signal data in which a plurality of peak sounds in FIG. 23B is superimposed results in convergence to zero while the envelope changes.
  • the autocorrelation function of the sound wave signal data indicating the water leakage sound in FIG. 23C has a result that the slope of the envelope is nearly zero.
  • the slope of the envelope of the autocorrelation function (or an approximate straight line of the envelope) is relatively large, and the sound signal of white noise that indicates water leakage sound
  • the slope of the autocorrelation function envelope is small.
  • the magnitude of the envelope slope varies depending on the amplitude ratio of the two signals.
  • an arbitrary threshold value may be set to perform the leak determination process.
  • FIG. 24 is a diagram illustrating an example of a determination process in which a predetermined line segment and a threshold value are set.
  • a predetermined line segment is represented by black (solid line)
  • an envelope is represented by gray (solid line). Determination is performed at a rate that the envelope becomes larger than a predetermined line segment.
  • the threshold is set to 20%, and the determination condition is set so that there is a possibility of water leakage instead of the peak sound if the ratio of the envelope larger than the predetermined line segment (time integration rate) is 20% or less.
  • the ratio in which the envelope is larger than the predetermined line segment is a time integration rate, and is expressed by, for example, the following formula (5).
  • Time integration rate% time when the envelope exceeds a predetermined line segment / total time (time until convergence to 0) ⁇ 100 (5)
  • the time integration rate is 94.5% and 69.6%, and the result exceeds the threshold value 20% specified by the specifying unit 6. Determined as data.
  • the time integration rate is 1.285%, which is 0.6%, which is lower than the threshold value 20%.
  • the determination of water leakage compared to the threshold value is not limited to the time integration rate, but may be performed by time integration of the envelope of the autocorrelation function and a predetermined line segment and comparing the magnitudes thereof. This means that the area created by each of the envelope and the predetermined line segment is compared.
  • FIG. 25 shows the result of comparing the size of the area created by each of the envelope line and the predetermined line segment.
  • the area formed by the envelope When the area formed by the envelope is larger than the area formed by the predetermined line segment, it is determined as sound wave signal data of peak sound instead of leaking sound. When the area formed by the envelope is smaller than the area formed by the predetermined line segment, it is determined that there is a suspicion of leaking sound. In this case, the area created by the predetermined line segment is designated as the threshold value.
  • the determination of water leakage in comparison with a threshold value may be made by comparing the slope of the envelope (or an approximate straight line of the envelope) and the slope of the predetermined line segment. In this case, the slope of the predetermined line segment is designated as the threshold value.
  • the above-mentioned time integration rate derived based on the envelope of the autocorrelation function, the area formed by the envelope, and the slope of the envelope are also referred to as judgment values.
  • FIG. 26 shows the result when water leakage is determined from the autocorrelation function based on one of the time integration rate, the area created by the envelope, or the slope of the envelope.
  • the determination was performed based on either the time integration rate, the area created by the envelope, or the slope of the envelope.
  • the leaked water data for answering was 24 cases that were judged as leaking by hearing from 103 cases.
  • the number of refinements using the time integration rate was 45
  • the number of refinements using the area created by the envelope was 43
  • the number of refinements using the slope of the envelope was 44.
  • the number of correct answers included was 21 (out of 24), and in all cases, a high correct answer rate was shown.
  • the algorithm of the fourth determining means there was almost no difference even if water leakage determination was made based on any of the time integration rate, the area created by the envelope, or the slope of the envelope. What is necessary is just to select suitably according to the situation which leaks, the data acquired in the past, etc.
  • the fourth determination means it is possible to narrow down the data with the possibility of water leakage sound from a large number of sound wave signal data with high probability.
  • the pattern of FIG. 27 can be considered as a combination of the first, second, and fourth determination means.
  • a total of 38 combinations can be considered including the single determination.
  • the combination of the second, third, or fourth determination means may be the pattern shown in FIG.
  • the combination of the second, third, and fourth determination means is the same pattern combination as the combination of the first, second, and fourth determination means.
  • the pattern of FIG. 29 can be considered as a combination of the first, third, or fourth determination means.
  • the first to fourth determination means may be combined.
  • the pattern of FIG. 30 can be considered.
  • the pattern of these combinations can be selected as appropriate according to the situation where water leakage is determined. For example, when it is desired to narrow down the data having the possibility of water leakage from a large amount of sound wave signal data, the sound wave signal data may be narrowed down by connecting the first to fourth determination means in series. Also, the logical product of the determination results of the first to fourth determination means may be taken. Further, the logical product of the determination results of the two logical products excluding the above-described combinations may be taken.
  • the threshold value used in the fourth determination means is determined in advance by the user, it may be automatically set in the apparatus. In that case, a method similar to the method described in the first and second determination means can be used.
  • a server that includes a plurality of sound wave signal data measured by a detector for detecting sound waves (vibration sound) by a water leak inspection investigator such as a water pipe, including the water leak determination algorithm (extraction unit, determination unit) of the present embodiment To the Internet.
  • the server performs water leakage determination using the transferred sound wave signal data.
  • the server forwards the water leak judgment result to the investigator's detector. Thereby, it becomes possible for the investigator to determine water leakage only with a detector equipped with communication means such as the Internet without carrying a high-performance and large-scale device.

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Abstract

La présente invention aborde le problème consistant à fournir un dispositif de détermination de fuite d'eau et un procédé de détermination de fuite d'eau pour détecter de manière plus efficace et précise une fuite d'eau. Un dispositif de détermination de fuite d'eau selon un mode de réalisation comprend une unité de réception pour recevoir des données d'onde sonore, une unité d'extraction pour extraire des données pour une section régulière ayant peu de bruit à partir des données d'onde sonore, une unité de spécification pour spécifier un seuil, et une unité de détermination pour déterminer un rapport de l'inclusion d'un signal présentant une caractéristique de fuite d'eau à partir des données de section régulière et déterminer s'il existe une fuite d'eau en comparant le rapport avec le seuil.
PCT/JP2016/084359 2016-05-19 2016-11-18 Dispositif de détermination de fuite d'eau et procédé de détermination de fuite d'eau Ceased WO2017199455A1 (fr)

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JPWO2019220609A1 (ja) * 2018-05-18 2021-06-10 日本電気株式会社 異常検出装置、異常検出方法及びプログラム
JP2022090864A (ja) * 2020-12-08 2022-06-20 株式会社東芝 データ処理装置、データ処理方法、及びプログラム
KR102454925B1 (ko) * 2022-03-28 2022-10-17 주식회사 위플랫 인공 지능을 이용한 누수 여부 판별 및 누수 위치 추정 장치 및 그 방법
JP2023132377A (ja) * 2022-03-10 2023-09-22 国立大学法人 新潟大学 機械学習による漏洩現象の同定方法及び同定システム
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WO2020095538A1 (fr) * 2018-11-08 2020-05-14 株式会社日立製作所 Procédé de détection de fuite d'eau, système de détection de fuite d'eau et terminal de capteur utilisé dans lesdits procédé et système
JP2020076646A (ja) * 2018-11-08 2020-05-21 株式会社日立製作所 漏水検知方法、漏水検知システム、及び、それに用いるセンサ端末
KR102036642B1 (ko) * 2019-06-15 2019-11-26 주식회사 에스씨솔루션 누수관리 시스템 및 이를 이용한 누수정보 제공 방법
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JP2022090864A (ja) * 2020-12-08 2022-06-20 株式会社東芝 データ処理装置、データ処理方法、及びプログラム
JP7451387B2 (ja) 2020-12-08 2024-03-18 株式会社東芝 データ処理装置、データ処理方法、及びプログラム
JP2023132377A (ja) * 2022-03-10 2023-09-22 国立大学法人 新潟大学 機械学習による漏洩現象の同定方法及び同定システム
JP7604412B2 (ja) 2022-03-10 2024-12-23 国立大学法人 新潟大学 機械学習による漏洩現象の同定方法及び同定システム
KR102454925B1 (ko) * 2022-03-28 2022-10-17 주식회사 위플랫 인공 지능을 이용한 누수 여부 판별 및 누수 위치 추정 장치 및 그 방법
CN117512940A (zh) * 2022-07-27 2024-02-06 湖北美的洗衣机有限公司 进水异常监测及控制方法、装置、介质及处理设备
JP2024129574A (ja) * 2023-03-13 2024-09-27 株式会社日立製作所 データ処理装置、データ処理方法、およびデータ処理プログラム
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WO2025220350A1 (fr) * 2024-04-18 2025-10-23 株式会社日立ハイテク Dispositif de diagnostic et procédé de diagnostic

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