WO2024004114A1

WO2024004114A1 - Conductor abnormality sensing device and conductor abnormality sensing method

Info

Publication number: WO2024004114A1
Application number: PCT/JP2022/026134
Authority: WO
Inventors: 佑紀岡南
Original assignee: 三菱電機株式会社
Priority date: 2022-06-30
Filing date: 2022-06-30
Publication date: 2024-01-04

Abstract

Provided is a conductor abnormality sensing device comprising a pulse generating circuit (3) for applying an electrical pulse to a conductor, a waveform detection circuit (4) for sensing a reflection of the applied electrical pulse from the conductor and outputting reflection waveform data, and a control circuit (2) that performs computational processing of the waveform data outputted from the waveform detection circuit and determines abnormality of the conductor, the control circuit being provided with a pulse control unit (21) that controls transmission timing of the electrical pulse, a discrete wavelet conversion unit (221) that performs discrete wavelet conversion of the reflection waveform data to calculate a detail coefficient, a coefficient adjustment unit (222) that adjusts the detail coefficient, an inverse discrete wavelet conversion unit (223) that reconstructs a reflection waveform by performing inverse discrete wavelet conversion using the adjusted detail coefficient, a data comparison unit (224) that calculates a difference between the reconstructed waveform and a reference waveform, and an abnormality determination unit (225) that determines abnormality of the conductor on the basis of the calculated difference.

Description

Conductor abnormality detection device and conductor abnormality detection method

The present disclosure relates to conductor abnormality detection technology.

In conductors such as communication cables or power lines, disconnections, short circuits, or half-disconnections may occur due to deterioration over time. Note that a half-broken wire refers to a state where the wire is about to break. If any of these abnormalities occur, normal communication will no longer be possible or power will not be supplied. Additionally, if a short circuit occurs, a fire may occur. In order to prevent such situations from occurring, a technology is required to detect abnormalities in conductors. Patent Document 1 discloses a detection technique using Time Domain Reflectometry in order to detect an abnormality such as an open cable failure in a vehicle power supply system.

International Publication No. 2021/235228

According to the detection technology disclosed in Patent Document 1, the characteristic impedance of the target wiring is measured by injecting a high-speed pulse or step signal into the target wiring and observing the reflected waveform or transmitted waveform of the signal. An abnormality in the wiring is detected based on whether the measured impedance is within a predetermined normal range (paragraphs 0043 to 0054 of Patent Document 1).

However, Patent Document 1 does not mention the case where noise exists on the wiring. In the method of Patent Document 1, even if some noise is superimposed on the observed reflected waveform or transmitted waveform, the characteristic impedance cannot be accurately measured because there is no mechanism for removing the noise. Therefore, there is a problem in that the characteristic impedance deviates from the true value due to superimposed noise, and there is a possibility that an abnormality in the wiring is detected incorrectly.

The present disclosure was made in recognition of such problems, and aims to provide a conductor abnormality detection technique that can detect abnormalities in conductors in a noisy environment.

One aspect of the conductor abnormality detection device according to the embodiment of the present disclosure includes a pulse generation circuit for applying an electric pulse to a conductor, and detecting reflection of the applied electric pulse from the conductor, and reflecting waveform data of the detected reflection. A conductor abnormality detection device comprising: a waveform detection circuit for outputting the waveform data; and a control circuit for processing the waveform data output from the waveform detection circuit to determine abnormality in the conductor, the control circuit includes a pulse control unit that controls the transmission timing of the electric pulse, a discrete wavelet transform unit that performs discrete wavelet transform on the reflected waveform data to calculate detailed coefficients of the reflected waveform data, and adjusts the calculated detailed coefficients. a coefficient adjustment section, an inverse discrete wavelet transform section that reconstructs a reflected waveform by performing an inverse discrete wavelet transform using the adjusted detailed coefficients, and a difference between the reconstructed waveform and a reference waveform. It includes a data comparison section and an abnormality determination section that determines whether the conductor is abnormal based on the calculated difference.

According to the conductor abnormality detection device according to the embodiment of the present disclosure, an abnormal location of a conductor in a noisy environment can be detected.

1 is a diagram illustrating a configuration example of a conductor abnormality detection device according to a first embodiment; FIG. FIG. 3 is a diagram illustrating a configuration example of a data processing unit according to the first embodiment. FIG. 2 is a schematic diagram of level 2 decomposition in discrete wavelet transform. FIG. 2 is a schematic diagram of level 2 reconstruction in inverse discrete wavelet transform. 1 is a diagram showing an example of a hardware configuration of a control circuit according to a first embodiment; FIG. 1 is a diagram showing an example of a hardware configuration of a control circuit according to a first embodiment; FIG. 5 is a flowchart showing the operation of the conductor abnormality detection device according to the first embodiment. FIG. 3 is a schematic diagram showing a reflected waveform from a cable abnormality location. 5 is a diagram showing an example of a reflected waveform observed by the waveform detection circuit in the first embodiment. FIG. 5 is a diagram showing an example of a waveform reconstructed by the data processing unit in the first embodiment. FIG.

Hereinafter, various embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. Note that components given the same or similar symbols in the drawings have the same or similar configurations or functions, and overlapping explanations of such components will be omitted.

Embodiment 1.
<Configuration>
The configuration of the conductor abnormality detection device 1 according to the first embodiment will be explained. As shown in FIG. 1, the conductor abnormality detection device 1 includes a control circuit 2, a pulse generation circuit 3, and a waveform detection circuit 4. The conductor abnormality detection device 1 is used by being connected to a cable 5, which is an example of a conductor to be detected.

(control circuit)
The control circuit 2 includes, as functional units, a pulse control unit 21 that controls the operation of the pulse generation circuit 3, and a data processing unit 22 that processes the received digital signal. Details of the functional units of the data processing unit 22 will be described later. In order to implement these functional units, the control circuit 2 includes processing circuitry as a hardware configuration. The processing circuitry, even if it is a dedicated processing circuit 2A as shown in FIG. 5A, executes the program stored in the memory 2C as shown in FIG. 5B. It may be the processor 2B.

When the processing circuit is a dedicated processing circuit 2A, the dedicated processing circuit 2A is, for example, a single circuit, a composite circuit, a programmed processor, a parallel programmed processor, or an application specific integrated circuit (ASIC). , FPGA (field-programmable gate array), or a combination of these. The plurality of functional units of the control circuit 2 may be realized by a plurality of separate processing circuits, or the plurality of functional units may be realized by a single processing circuit.

When the processing circuit is the processor 2B, the functional parts of the control circuit 2 are realized by software, firmware, or a combination of software and firmware. Software and firmware are written as programs and stored in the memory 2C. The processor 2B implements each functional unit by reading and executing programs stored in the memory. Examples of the memory 2C include non-volatile or Includes volatile semiconductor memory, magnetic disks, flexible disks, optical disks, compact disks, minidisks, and DVDs.

(Pulse generation circuit)
The pulse generation circuit 3 is a circuit for applying an electric pulse to a conductor. In the first embodiment, the pulse generation circuit 3 generates an electric pulse (hereinafter simply referred to as a "pulse") in response to a control signal from the pulse control section 21, and transmits the generated pulse to the cable 5. Note that the pulse transmission format is set to be a single-end method or a differential method depending on the cable 5. For example, if the cable 5 is a twisted pair cable, the pulse transmission format is a differential method. The pulse width is preferably short compared to the propagation delay time of the cable 5. For example, if the dielectric constant of the insulator covering the signal line in the cable 5 to be detected is 2.2, the propagation delay time per unit length is 4.94 ns/m. If the cable length is 10 m, the propagation delay time will be 49.4 ns, so it is desirable that the pulse width be about 1/10 of this time or less, that is, 5 ns or less.

(Waveform detection circuit)
The waveform detection circuit 4 is a circuit for detecting the reflection of an applied electric pulse from a conductor. In the first embodiment, the waveform detection circuit 4 observes the reflected voltage at the pulse transmission end of the conductor abnormality detection device 1. The pulse transmitted from the pulse generation circuit 3 is reflected at the impedance mismatch point of the cable 5, and the reflected waveform is observed by the waveform detection circuit 4 at the transmission end. The waveform detection circuit 4 is realized using, for example, an AD converter. It is desirable that the sampling rate of the AD converter is at least faster than the time width of the pulse.

(Data processing section)
Next, the configuration of the data processing section 22 will be explained with reference to FIG. 2. The data processing unit 22 includes a discrete wavelet transform unit 221 that performs discrete wavelet transform on the reflected waveform data transmitted from the waveform detection circuit 4, and a coefficient adjustment unit 222 that modifies the values of the approximate coefficients and detailed coefficients obtained after data conversion. An inverse discrete wavelet transform unit 223 reconstructs time waveform data by performing an inverse discrete wavelet transform on the corrected coefficients, a data comparison unit 224 calculates the difference between the reference data and the reconstructed data, and detects cable abnormality based on the difference data. It includes an abnormality determination section 225 that determines the presence or absence of abnormality and the location of the abnormality, and a storage section 226 that temporarily stores reference data.

Here, the discrete wavelet transform is a signal processing technique that decomposes a given signal into several signals having different levels of resolution. For the signal f(t)∈L ² (R), the approximation f _j+1 (t) with resolution j+1 is the approximation f _j (t) with resolution j and the details g _j ( t) can be orthogonally decomposed into

When f _j+1 (t), f _j (t), and g _j (t) are expanded using the scaling function φ (t) or the wavelet function Ψ (t), respectively, as shown in equations (2) to (4), Focus on the expansion coefficient.

At this time, an approximate coefficient sequence with resolution j+1 c _j+1 = {c _j+1 [k]} an approximate coefficient sequence with one resolution lower than _k∈Z c _j = {c _j [k]} _k∈Z and detailed coefficient sequence d _j = {d _j [k]} _kεZ is determined by Equation (5) and Equation (6), respectively.

Here, the sequence {l[k]} _k∈Z and {h[k]} _k∈Z are filters determined by a scaling function and a wavelet function, and are called a low frequency filter coefficient and a high frequency filter coefficient, respectively. Moreover, the superscript bar represents a complex conjugate. Equations (5) and (6) are the decomposition algorithm from resolution j+1 to resolution j. This decomposition algorithm is called discrete wavelet transform.

Conversely, the sequence c _j+1 can be determined from the sequence c _j and the sequence d _j as shown in equation (7).

This equation (7) is the reconstruction algorithm from resolution j to resolution j+1. This reconstruction algorithm is called inverse discrete wavelet transform.

Using the discrete wavelet transform, c ₀ is decomposed into two sequences c _-1 and d _-1 . This decomposition is called level 1 decomposition. Further, the sequence c _-1 is called a level 1 approximation coefficient, and the sequence d _-1 is called a level 1 detailed coefficient. The discrete wavelet transform starts from level 0 and is decomposed to level N (N is an integer of 1 or more) depending on the situation. FIG. 3 shows a schematic diagram of level 2 decomposition, and FIG. 4 shows a schematic diagram of level 2 reconstruction.

The filter coefficients {l[k]} _k∈Z and {h[k]} _k∈Z are determined by what kind of wavelet waveform is adopted, that is, what kind of mother wavelet is adopted. Examples of mother wavelets include the Daubcy wavelet and the Meyer wavelet. Here, the Haar wavelet is used as an example of the mother wavelet. In the case of Haar wavelet, the filter coefficients are given by equation (8) and equation (9).

Next, the operation of the conductor abnormality detection device 1 will be explained according to FIG. In step S1, the pulse generation circuit 3 applies a pulse to the cable 5 to be measured according to a command from the pulse control section 21. If there is an abnormality in the cable 5, such as a break, short circuit, or half-break, the characteristic impedance of the cable 5 changes at the abnormal location, so that the applied pulse is reflected at the abnormal location. If the abnormality is a disconnection, the impedance becomes open and the pulse is positive and totally reflected. On the other hand, if the abnormality is short-circuited, the impedance will be short-circuited and the pulse will be negative and totally reflected. If the abnormal point is a half-break, the impedance takes an intermediate value between an open circuit and a short circuit, so a reflection with a voltage amplitude smaller than the transmission amplitude occurs.

In step S2, the waveform detection circuit 4 observes the reflection from such an abnormal location as a transmission end voltage after the round trip time of the delay time between the pulse transmission end and the abnormal location. For example, if there is an abnormality at a position 5 m from the transmitting end, and the dielectric constant of the insulator covering the signal line of cable 5 is 2.2, the propagation delay time per unit length is 4.94 ns/ m, the time it takes for the pulse to reach the abnormal location is 24.7 ns. Therefore, at the transmitting end, the reflected waveform is observed after 49.4 ns, which is twice that time. The waveform detection circuit 4 performs AD conversion on the reflected waveform to be observed, and sends the converted data to the data processing section 22 as reflected waveform data.

In step S3, the discrete wavelet transform unit 221 performs discrete wavelet transform on the reflected waveform data in digital format. The level N for decomposing the waveform by the discrete wavelet transform is set to 2 or more. When the level is N, an approximation coefficient of level N and N detailed coefficients of levels 1 to N are obtained.

In step S4, the coefficient adjustment unit 222 adjusts N detailed coefficients at levels 1 to N and one approximation coefficient at level N. Specifically, the detailed coefficients from levels 1 to N-1 and the approximation coefficients at level N are set to 0 at all times. A threshold is set for the detailed coefficients of level N, coefficients below the threshold are set to 0, and coefficients larger than the threshold are left unchanged. In this way, the coefficient adjustment unit 222 adjusts the N detailed coefficients at levels 1 to N and the one approximation coefficient at level N. Note that the threshold value of the detailed coefficient needs to be adjusted in advance by simulation or the like for each measurement target. In the examples shown in FIGS. 8 and 9, which will be described later, the threshold value was adjusted in advance through simulation, and the threshold value was determined to be 0.16.

In step S5, the inverse discrete wavelet transform unit 223 reconstructs the reflected waveform by performing inverse discrete wavelet transform using these modified N detailed coefficients and level N approximation coefficients. By reconstructing the reflected waveform in this manner, noise components can be suppressed or removed from the reflected waveform data.

In step S6, the inverse discrete wavelet transform unit 223 determines the presence or absence of reference data that is reference waveform data. The presence or absence of reference data is determined by referring to the storage unit 226. If there is no reference data in advance, that is, if there is no reference data in the storage unit 226, the inverse discrete wavelet transform unit 223 stores the first measured and reconstructed waveform data in the storage unit 226 as reference data ( Step S7). On the other hand, if the reference data exists in the storage unit 226, the inverse discrete wavelet transform unit 223 outputs reconstructed waveform data.

If the reference data exists in the storage unit 226, in step S8, the data comparison unit 224 calculates the difference between the reference data and the waveform data reconstructed by the inverse discrete wavelet transform unit 223, and calculates the maximum difference and the maximum difference. Calculate the time of the hour. The data comparison unit 224 transmits the calculated maximum difference amount data and the current time data to the abnormality determination unit 225.

In step S9, the abnormality determination unit 225 compares the maximum difference with a predetermined voltage threshold to determine whether there is an abnormality. That is, the abnormality determining unit 225 determines that there is an abnormality if the maximum difference is equal to or greater than the threshold (step S11), and determines that there is no abnormality if it is less than the threshold (step S10). Further, when it is determined that there is an abnormality, the abnormality determining unit 225 calculates the distance d from the transmitting end to the abnormal location from d=ct/(2√ε) (step S12). c is the speed of light, t is the pulse propagation delay time, and ε is the dielectric constant of the insulator of the cable 5.

By operating as described above, the conductor abnormality detection device 1 having the configuration described above can detect cable abnormalities such as disconnections, short circuits, and half-disconnections with high accuracy even in a noisy environment. Can be done.

The pulse reflected from the abnormal location is reflected positively and negatively at both ends of the abnormal location. FIG. 7 shows a schematic diagram illustrating how a pulse transmitted from a transmitter is reflected at an abnormal location. In FIG. 7, Z0 represents the characteristic impedance of the cable, Z1 represents the impedance at the abnormal location, and Z0<Z1. In this system, the pulse is positively reflected in front of the abnormality and negatively reflected in the back of the transmitter. Therefore, a reflected waveform in which positive and negative reflections are superimposed is observed at the transmitting end. This reflection shape has a shape similar to a Haar wavelet. For this reason, it is thought that by using the Haar wavelet as the mother wavelet used in the discrete wavelet transform, it becomes easier to detect abnormal locations.

Furthermore, the detail coefficients at levels 1 to N obtained by discrete wavelet transform include higher frequency components as the level is lower. Further, when assuming the observed reflected waveform and random noise, the random noise is included in a higher frequency band than the band of the reflected waveform. For the above reasons, only the value of the detail coefficient of level N is left in the adjustment of the detail coefficient.

Figures 8 and 9 show simulation results. A case is considered in which, in a cable with a characteristic impedance of 50Ω, a half-break (assuming the impedance is 70Ω) occurs at a position where the propagation delay time is 50ns. In this case, random noise is superimposed on the cable 5. FIG. 8 shows a reflected waveform observed by the waveform detection circuit 4, and although reflection should be observed at a position of 50 ns×2=100 ns, it is confirmed that it is buried in noise. FIG. 9 shows the reflected waveform after processing using the proposed method. At the time of 100 ns (actually half the time, 50 ns) when the half-disconnection occurs, a reflected waveform from the half-disconnection is confirmed. In this way, it was confirmed that the proposed method can detect abnormalities in the cable 5 with high accuracy even in a noisy environment.

<Additional notes>
Some aspects of the various embodiments described above are summarized as follows.

(Additional note 1)
The conductor abnormality detection device (1) of Supplementary Note 1 includes a pulse generation circuit (3) for applying an electric pulse to a conductor, and detects reflection of the applied electric pulse from the conductor, and generates reflection waveform data of the detected reflection. A conductor abnormality detection device comprising: a waveform detection circuit (4) for outputting the waveform data; and a control circuit (2) for calculating the waveform data output from the waveform detection circuit and determining abnormality of the conductor. The control circuit includes a pulse control section (21) that controls the transmission timing of the electric pulse, and a discrete wavelet transform section (221) that performs discrete wavelet transform on the reflected waveform data to calculate detailed coefficients of the reflected waveform data. ), a coefficient adjustment unit (222) that adjusts the calculated detailed coefficients, and an inverse discrete wavelet transform unit (223) that reconstructs the reflected waveform by performing inverse discrete wavelet transform using the adjusted detailed coefficients. ), a data comparison unit (224) that calculates a difference between the reconstructed waveform and a reference waveform, and an abnormality determination unit (225) that determines an abnormality in the conductor based on the calculated difference. Be prepared.

According to the conductor abnormality detection device (1) of Appendix 1, the coefficient adjustment unit (222) adjusts the detailed coefficients, and the inverse discrete wavelet transform unit (223) performs inverse discrete wavelet transform using the adjusted detailed coefficients. Since the reflected waveform is reconstructed by this, it is possible to reconstruct the reflected waveform from which noise has been removed. Therefore, an abnormal location can be detected with high accuracy from the reflected wave of the electric pulse.

(Additional note 2)
The conductor abnormality detection device of Appendix 2 is the conductor abnormality detection device described in Appendix 1, in which the discrete wavelet transform unit performs discrete wavelet transform on the reflected waveform data to a level N (N≧1), and performs the coefficient adjustment. The inverse discrete wavelet transform section sets the detailed coefficients of levels 1 to N-1 and the approximation coefficients of level N to 0, and the inverse discrete wavelet transform section sets the detailed coefficients of levels 1 to N-1 set to 0 and the level set to 0. The reflected waveform is reconstructed using N approximation coefficients.

The detail coefficients of levels 1 to N obtained by discrete wavelet transform include higher frequency components as the level is lower. Furthermore, assuming the presence of electric pulse reflection and random noise, the random noise is included in higher frequencies than the reflection band. Therefore, according to the conductor abnormality detection device of Appendix 2, the detailed coefficients of levels 1 to N-1 corresponding to higher frequency components are set to 0, so that random noise can be removed.

(Appendix 3)
The conductor abnormality detection device of Supplementary Note 3 is the conductor abnormality detection device described in Supplementary Note 2, in which the coefficient adjustment section adjusts the detailed coefficient of level N when the detailed coefficient of level N is equal to or less than a predetermined threshold value. The detail coefficient of the level N is adjusted by setting the coefficient to 0 and leaving the detail coefficient of the level N as it is when it is larger than the threshold value, and the inverse discrete wavelet transform unit adjusts the detail coefficient of the level N that was set to 0. The reflected waveform is reconstructed using the detail coefficient of −1, the approximation coefficient of level N set to 0, and the adjusted detail coefficient of level N.

Supplementary Note 3 According to the conductor abnormality detection device, the threshold value determination is performed for the detailed coefficient of level N corresponding to the low frequency component, so it is possible to better observe the reflection of the electric pulse.

(Additional note 4)
The conductor abnormality detection device of Appendix 4 is the conductor abnormality detection device described in any one of Appendixes 1 to 3, in which the discrete wavelet transform section performs the discrete wavelet transform using a Haar wavelet as a mother wavelet.

Since the conductor abnormality detection device of Appendix 4 performs discrete wavelet transform using the Haar wavelet as a mother wavelet, reflected waves having a waveform similar to the Haar wavelet can be better observed. A half-disconnection is an event that causes such a reflected wave. Therefore, the conductor abnormality detection device of Supplementary Note 4 can better detect half-wire breaks.

(Appendix 5)
The conductor abnormality detection method of Supplementary Note 5 is a conductor abnormality detection method performed by a conductor abnormality detection device including a pulse generation circuit (3), a waveform detection circuit (4), and a control circuit (2), wherein the pulse generation circuit , a step (S1) of applying an electric pulse to the conductor; a step (S2) of the waveform detection circuit detecting the reflection of the applied electric pulse from the conductor and outputting reflected waveform data of the detected reflection; A conductor abnormality detection device comprising steps (S3 to S11) in which the control circuit arithmetic processes the waveform data output from the waveform detection circuit and determines abnormality in the conductor, the control circuit comprising: , a step (S1) of controlling the transmission timing of the electric pulse; a step (S3) of calculating detailed coefficients of the reflected waveform data by performing discrete wavelet transform on the reflected waveform data; and adjusting the calculated detailed coefficients. a step (S4), a step (S5) of reconstructing the reflected waveform using the adjusted detail coefficients, a step (S8) of calculating the difference between the reconstructed waveform and the reference waveform, and the calculation thereof. and a step (S11) of determining an abnormality in the conductor based on the determined difference.

According to the conductor abnormality detection method of Appendix 5, the coefficient adjustment unit (222) adjusts the detailed coefficients, and the inverse discrete wavelet transform unit (223) performs the inverse discrete wavelet transform using the adjusted detailed coefficients to detect the reflection. Since the waveform is reconstructed, the reflected waveform from which noise has been removed can be reconstructed. Therefore, an abnormal location can be detected with high accuracy from the reflected wave of the pulse.

Note that it is possible to combine the embodiments, or to modify or omit each embodiment as appropriate.

The conductor abnormality detection device of the present disclosure can be used as a device for detecting abnormalities such as disconnections, short circuits, or half-disconnections in conductors such as communication cables or power lines.

1 Conductor abnormality detection device, 2 Control circuit, 2A Processing circuit, 2B Processor, 2C Memory, 3 Pulse generation circuit, 4 Waveform detection circuit, 5 Cable, 21 Pulse control unit, 22 Data processing unit, 221 Discrete wavelet transform unit, 222 Coefficient adjustment unit, 223 Inverse discrete wavelet transform unit, 224 Data comparison unit, 225 Abnormality determination unit, 226 Storage unit.

Claims

a pulse generation circuit for applying an electric pulse to a conductor;
a waveform detection circuit for detecting reflection of the applied electric pulse from the conductor and outputting reflection waveform data of the detected reflection;
a control circuit that performs arithmetic processing on the waveform data output from the waveform detection circuit and determines whether there is an abnormality in the conductor;
A conductor abnormality detection device comprising:
The control circuit includes:
a pulse control unit that controls the transmission timing of the electric pulse;
a discrete wavelet transform unit that performs discrete wavelet transform on the reflected waveform data to calculate detailed coefficients of the reflected waveform data;
a coefficient adjustment unit that adjusts the calculated detailed coefficient;
an inverse discrete wavelet transform unit that reconstructs the reflected waveform by performing inverse discrete wavelet transform using the adjusted detail coefficients;
a data comparison unit that calculates the difference between the reconstructed waveform and the reference waveform;
an abnormality determination unit that determines an abnormality in the conductor based on the calculated difference;
A conductor abnormality detection device equipped with.
The discrete wavelet transform unit performs a discrete wavelet transform on the reflected waveform data to a level N (N≧1),
The coefficient adjustment unit sets detailed coefficients of levels 1 to N-1 and approximate coefficients of level N to 0,
The inverse discrete wavelet transform unit reconstructs the reflected waveform using the detailed coefficients of levels 1 to N-1 set to 0 and the approximate coefficients of level N set to 0.
A conductor abnormality detection device according to claim 1.
The coefficient adjustment unit sets the detail coefficient of level N to 0 when the detail coefficient of level N is less than or equal to a predetermined threshold, and leaves the detail coefficient of level N as it is when it is larger than the threshold. adjusting the detail coefficient of the level N;
The inverse discrete wavelet transform unit uses the detail coefficients of levels 1 to N-1 set to 0, the approximation coefficients of level N set to 0, and the adjusted detail coefficients of level N to transform the reflection. reconstruct the waveform,
A conductor abnormality detection device according to claim 2.
The discrete wavelet transform unit performs the discrete wavelet transform using a Haar wavelet as a mother wavelet.
A conductor abnormality detection device according to any one of claims 1 to 3.
A conductor abnormality detection method performed by a conductor abnormality detection device comprising a pulse generation circuit, a waveform detection circuit, and a control circuit, the method comprising:
the pulse generation circuit applying an electric pulse to the conductor;
a step in which the waveform detection circuit detects reflection of the applied electric pulse from the conductor and outputs reflection waveform data of the detected reflection;
a step in which the control circuit performs arithmetic processing on the waveform data output from the waveform detection circuit to determine an abnormality in the conductor;
A conductor abnormality detection device comprising:
The control circuit includes:
controlling the transmission timing of the electric pulse;
calculating detailed coefficients of the reflected waveform data by performing a discrete wavelet transform on the reflected waveform data;
adjusting the calculated detail coefficient;
reconstructing the reflected waveform using the adjusted detail coefficients;
calculating a difference between the reconstructed waveform and a reference waveform;
determining an abnormality in the conductor based on the calculated difference;
A conductor abnormality detection method comprising: