Metal body monitoring device and metal body monitoring method

Abstract

This metal body monitoring device includes a magnetic detection device, a distance detection device, and a processing device. The magnetic detection device has a magnetic sensor unit. The magnetic sensor unit is provided to a moving body that relatively moves on the metal body, the magnetic sensor unit detecting, in a contactless manner, a leakage magnetic field generated in the moving body. The distance detection device is provided to the moving body and detects a relative movement distance of the moving body with respect to the metal body. The processing device converts first data representing a time series of detection signal intensity from the magnetic detection device to second data representing the relationship between the movement distance and the detection signal strength on the basis of the movement distance at each time point detected by the distance detection device. The processing device extracts a spectral response in a wave number space identified from the second data and outputs monitoring information, which is based on the extracted spectral response.

Description

Metallic object monitoring device and metal object monitoring method

[0001] The present invention relates generally to monitoring metallic objects.

[0002] An example of a metal body is a wire rope. Patent Document 1 describes a wire rope deterioration determination device as an example of a wire rope monitoring device. Patent Document 1 states that "the wire rope deterioration determination device includes a detector 30 that detects wire rope deterioration using a leakage magnetic flux method, a data input unit 31 that obtains measurement data from the detector 30, a noise component removal means that removes noise components from the data input unit 31, and a waveform processing means that extracts data that is determined to have a high correlation with factors that cause wire rope deterioration."

[0003] Japanese Patent Application Laid-Open No. 2022-52864

[0004] Patent Document 1 describes a wire rope deterioration determination device that detects deterioration of a wire rope using a leakage magnetic flux method.

[0005] In this device, the waveform of the detection signal can change depending on the speed at which the wire rope passes through the detector. Furthermore, in this device, noise occurs due to the environment or condition of the wire rope monitoring (for example, offset drift due to fluctuations in the detector position, external noise, etc.), and this noise affects the waveform of the detection signal. Therefore, it is difficult to distinguish, from the detection signal, anomalous parts that are different from healthy parts of the wire rope (specifically, whether the anomalous parts are damaged or not).

[0006] Patent Document 1 does not describe how to identify a peculiar part of a wire rope and how to do so.

[0007] Such a problem may occur when the metal object is a metal object other than a wire rope, and may also occur in any metal object monitoring environment in which a non-contact magnetic sensor unit (e.g., a detector) moves relatively on the metal object.

[0008] An object of the present invention is to enable discrimination of specific locations on a metal object being monitored in such a metal object monitoring environment.

[0009] To solve the above problems, one representative metal object monitoring device of the present invention includes a magnetic detection device, a distance detection device, and a processing device connected to the magnetic detection device and the distance detection device. The magnetic detection device includes a magnetic sensor unit that is provided on a mobile object that moves relatively over the metal object and detects the generated leakage magnetic field in a non-contact manner. The distance detection device is provided on the mobile object and detects the distance the mobile object has moved relative to the metal object. The processing device converts first data representing a time series of detection signal strength from the magnetic detection device into second data representing the relationship between the movement distance and the detection signal strength based on the movement distance at each point in time detected by the distance detection device. The processing device extracts a spectral response in wavenumber space identified from the second data and outputs monitoring information based on the extracted spectral response.

[0010] According to the present invention, it is possible to distinguish a specific portion of a metal object in a metal object monitoring environment in which a non-contact magnetic sensor unit moves relatively on the metal object. Problems, configurations, and effects other than those described above will become clear from the description of the following embodiments.

[0011] 7B is a schematic diagram of a metallic object monitoring device according to a first embodiment of the present invention. FIG. 7B is a perspective view of a magnetic detection device according to the first embodiment. FIG. 7C is a bottom view of a magnetic detection device according to the first embodiment. FIG. 7D is a partially cutaway plan view of a magnetic detection device according to the first embodiment. FIG. 7E is a schematic diagram showing an operating state of a magnetic sensor unit according to the first embodiment. FIG. 7F is a schematic diagram showing another operating state of a magnetic sensor unit according to the first embodiment. FIG. 7G is an explanatory diagram of processes 1 to 3 relating to a monitoring information extraction analysis process applying continuous wavelet analysis in a metallic object monitoring device according to a first embodiment of the present invention. FIG. 7H is an explanatory diagram of processes 4 to 6 relating to a monitoring information extraction analysis process applying continuous wavelet analysis in a metallic object monitoring device according to a first embodiment of the present invention. FIG. 7H is an explanatory diagram of process 5 relating to a monitoring information extraction analysis process using a short-time Fourier transform in a metallic object monitoring device according to a first embodiment of the present invention. FIG. 7H is an explanatory diagram of an interpolation process for distance information conversion data in FIG. 7A in a metallic object monitoring device according to a first embodiment of the present invention. 7B in the metal object monitoring device according to the first embodiment of the present invention. FIG. 8 is an explanatory diagram of hierarchical decomposition by performing discrete wavelet processing in process 5 in FIG. 7B in the metal object monitoring device according to the first embodiment. FIG. 9 is a block diagram of the metal object monitoring device according to the first embodiment. FIG. 10 is a block diagram of a detection unit in the processing device. FIG. 11 is a flowchart of a metal object monitoring processing program executed by the evaluation device. FIG. 12 is a bottom view of a magnetic detection device according to a second embodiment. FIG. 13 is a partially cutaway plan view of the magnetic detection device according to the second embodiment. FIG. 14 is a block diagram of the metal object monitoring device according to the second embodiment. FIG. 15 is a diagram showing an example of the position of a singular point present on a metal object and a contour line representation of the signal intensity distribution at that position.

[0012] Hereinafter, several embodiments will be described with reference to the drawings. [First embodiment] <External configuration of the first embodiment>

[0013] FIG. 1 is a schematic diagram of a metal object monitoring device according to a first embodiment of the present invention.

[0014] 1, the metal object monitoring device 1 includes a magnetic detection device 2, a distance detection device 3, a processing device 4, a power supply 88, and a connection cable 60. The metal object monitoring device 1 is mounted on a mobile object 100. The metal object 200 is an object to be inspected (an object to be monitored), and the mobile object 100 includes wheels 300 for traveling on the metal object 200, and the wheels 300 rotate while contacting a traveling surface 200a.

[0015] The magnetic detection device 2 is installed at a position facing the running surface 200a of the metal body 200, and the distance detection device 3, the processing device 4, and the power supply 88 are installed inside the mobile body 100. The power supply 88 supplies drive current to the magnetic detection device 2, the distance detection device 3, and the processing device 4, causing the magnetic detection device 2, the distance detection device 3, and the processing device 4 to operate.

[0016] When the mobile object 100 travels over the metal body 200, the magnetic detection device 2 detects the state of the metal body 200, and a signal representing the detection result is sent to the processing device 4 via the connection cable 60. The axles 400 of the wheels 300 of the mobile object 100 are connected to the distance detection device 3 via the rotation drive mechanism 500, and the operation of the rotation drive mechanism 500 is detected by the distance detection device 3. The processing device 4 then collects each signal from the magnetic detection device 2 and the distance detection device 3 and performs processing such as analysis and display using each signal. In other words, the processing device 4 monitors the state of the metal body 200. If the mobile object 100 is equipped with an AC power supply, the power supply 88 may be a stabilized power supply circuit powered by the AC power supply. If an AC power supply is unavailable, the power supply 88 may be an uninterruptible power supply with a built-in battery.

[0017] The structure of the magnetic detection device 2 will be described with reference to FIG.

[0018] FIG. 2 is a perspective view of the magnetic detection device 2. As shown in FIG. 2, the magnetic detection device 2 has an integrated structure in which an upper housing 26 (an example of a second housing) and a lower housing 20 (an example of a first housing) are fixed to each other, and a flange 25 is provided on the upper side of the upper housing 26. The upper housing 26 and the lower housing 20 are formed in a rectangular parallelepiped shape, and the lower surface of the upper housing 26 is joined to the upper surface of the lower housing 20. The rectangular flange 25 is also joined and fixed to the upper surface of the upper housing 26. A connector 28 is provided on one side of the upper housing 26, and a connector 62 is also provided on one end of a connection cable 60. The connectors 28 and 62 can be mated and connected, and after connection, the magnetic detection device 2 and the processing device 4 can communicate with each other through the connection cable 60. The connectors 28 and 62 have a dustproof and waterproof structure, which prevents foreign matter such as dust and water from entering through gaps when the connectors 28 and 62 are connected to each other, which could cause malfunction of the magnetic detection device 2 or poor connection of the connection cable 60. A suitable connector 28 is a through-type connector that can be inserted into one side of the magnetic detection device 2 to be installed and tightened to prevent it from falling off or loosening due to vibration and that can prevent dust and water from entering, but a connector other than a through-type connector may also be used as long as it has sufficient dustproof and waterproof capabilities in the installation environment of the magnetic detection device 2.

[0019] Through holes 25a are formed in the four corners of the flange. Furthermore, the mobile body 100 (see FIG. 1 ) has screw holes (not shown) at positions facing the through holes 25a at the installation location of the magnetic detection device 2. A bolt (not shown) is inserted into the through hole 25a via a washer (not shown), and the bolt is then tightened into a screw hole provided in the mobile body 100, thereby fixing the magnetic detection device 2 to a predetermined position on the mobile body 100. The lower housing 20 incorporates a magnetic sensor unit 21 for detecting the state of the metal body 200. Furthermore, the upper housing 26 incorporates a preamplifier unit 210 for amplifying the magnetic signal detected by the magnetic sensor unit 21. Details of the magnetic sensor unit 21 and the preamplifier unit 210 will be described later.

[0020] When the magnetic detection device 2 is installed at a predetermined position on the mobile object 100 and used, measures must be taken to protect it from vibrations and shocks caused by the rocking motion of the mobile object 100 as it moves over the metal body 200. Therefore, the internal spaces of the upper housing 26 and the lower housing 20 are filled with an insulating and non-magnetic filler (not shown) and molded into a resin structure. This structure firmly holds the magnetic sensor unit 21 and the preamplifier unit 210 inside the lower housing 20 and the upper housing 26, preventing damage to the magnetic sensor unit 21 and the preamplifier unit 210 and misalignment of the detection position of the magnetic sensor unit 21 due to rocking motion caused by the movement of the mobile object 100. In the example shown in FIG. 2 , the upper housing 26 and the lower housing 20 are rectangular parallelepipeds, but these shapes can also be other shapes, such as a cube, a cylinder, or a prism, depending on conditions such as the installation space for the mobile object 100.

[0021] The configuration of the magnetic sensor unit 21 and the preamplifier unit 210 inside the magnetic detection device 2 will be described with reference to FIG. 3, which is a bottom view of the magnetic detection device 2.

[0022] As shown in Figure 3, the magnetic sensor unit 21 is composed of an oscillator coil 5A, an oscillator coil 5B, and a receiver coil 6, each of which is fabricated by winding coated copper wire. The receiver coil 6 is located between the oscillator coils 5A and 5B, and the arrangement of the coils is parallel to the direction of movement of the mobile object 100 moving on the metal body 200. An AC current of a predetermined oscillation frequency f (predetermined frequency) is supplied to the oscillator coils 5A and 5B from the processing device 4 (see Figure 1) via a connection cable 60. This generates an AC magnetic field from each of the oscillator coils 5A and 5B, which excites the metal body 200. An induced voltage is generated in the receiver coil 6 by the magnetic flux generated from the excited metal body 200.

[0023] FIG. 4 is a partially cutaway plan view of the magnetic detection device 2, showing the layout of the preamplifier section 210 inside the upper housing 26.

[0024] In Figure 4, the center of flange 25 has been cut away to expose the interior of the upper housing. Preamplifier section 210 is made up of printed circuit board 210a on which amplifier filter circuit 22 is mounted. Amplifier filter circuit 22 amplifies and filters the induced voltage generated in receiving coil 6, and transmits the results to processing device 4 via connection cable 60 (see Figure 1). Processing device 4 analyzes the received signal and detects the magnetic signal generated from metal body 200.

[0025] The upper housing 26, which houses the preamplifier unit 210, is made of a metal such as aluminum, which functions as an electromagnetic shield. By using a metal housing for the upper housing 26, it is possible to shield the electromagnetic field generated by the preamplifier unit 210 and suppress electromagnetic waves from entering the preamplifier unit 210 from the outside of the usage environment. Furthermore, if the flange 25 is formed on the upper housing 26 by metal cutting during production, the flange 25 and the upper housing 26 will be identical, thereby reducing the number of components of the magnetic detection device 2 and improving its strength. Furthermore, when using aluminum for the upper housing 26, it is more preferable to use aluminum that has been anodized to form an oxide film on the aluminum surface, as this improves surface hardness and corrosion resistance and provides insulation.

[0026] The lower housing 20 is made of a non-magnetic material. This is because the lower housing 20 houses the oscillator coils 5A and 5B that generate an AC magnetic field and the receiver coil 6 that detects changes in the magnetic field generated by the metal body. Furthermore, because the magnetic detection device 2 is installed outside the mobile object 100 and is used while the mobile object 100 is moving, it is preferable to use a material with excellent toughness and corrosion resistance, such as fiber-reinforced resin, for the lower housing 20. For example, when using glass fiber-reinforced plastic such as GFRP, it is recommended to apply a coating film to the surface of the lower housing using a paint made by combining a liquid unsaturated polyester resin and a hardener with glass fiber or the like. This coating film formation forms a waterproof coating layer, further improving the weather resistance of the lower housing.

[0027] The lower housing 20 is provided with a screw-type insert nut (not shown) on the contact surface side with the upper housing 26, and is fixed by screwing to the upper housing 26. The upper housing 26 and the lower housing 20 are bonded at their contact surfaces with a silicone contact agent that has excellent weather resistance, and gaps are sealed to form an integrated unit.

[0028] The connection cable 60 connecting the magnetic detection device 2 and the processing device 4 includes a power line for the preamplifier unit 210, an output signal line for the receiving coil via the amplifier filter circuit 22, and an input signal line for the excitation signal to the oscillator coil 5A and the oscillator coil 5B. If the connection cable 60 is subjected to noise due to electromagnetic waves traveling through space, this will affect the detection of the magnetic signal from the metal body 200. For this reason, it is preferable to use a multi-pair shielded cable with excellent noise resistance, such as a twisted pair shielded cable, for the connection cable 60. Furthermore, as a countermeasure against crosstalk within the connection cable 60, the connection cable 60 may be divided into two cables. Specifically, one cable may be a multi-pair shielded cable including a power line for the preamplifier unit 210 and an output signal line for the receiving coil from the amplifier filter circuit. The other cable may be another multi-pair shielded cable including an input signal line for the excitation signal to the oscillator coil 5A and the oscillator coil 5B.

[0029] The connection cable 60 is fixed to the underside of the moving body 100 facing the metal body 200, and is connected to the processing device 4 via an inlet provided in the moving body 100. The connection cable 60 is firmly fixed and held in place using fasteners such as cable ties to prevent loosening of the underside of the moving body 100, in order to prevent interference with the movement of the moving body 100 and noise generation due to the connection cable's shaking. Furthermore, when fixing and holding the connection cable 60, it is advisable to design the wiring path to keep the length of the connection cable as short as possible. The shorter this wiring path, the more effectively the risk of noise being introduced into the detection signal from the magnetic detection device 2 via the connection cable 60 can be avoided.

[0030] The distance detection device 3 that detects the distance traveled by the mobile object 100 will now be described. The distance detection device 3 is a rotary encoder, and pulse signals are generated as the rotary encoder's rotary shaft rotates. By counting these pulse signals, the amount of movement associated with the rotation of the rotary shaft can be calculated. Utilizing the function of this rotary encoder, the rotation of the axle 400 of the wheel 300 of the mobile object 100 is transmitted to the rotary encoder's rotary shaft via a rotary drive mechanism 500. This rotary drive mechanism 500 is composed of pulleys and a timing belt attached to the axle 400 and the rotary encoder's rotary shaft. In other words, when the mobile object 100 moves, the axle 400 rotates in response to the movement of the wheel, and this movement is transmitted to the rotary drive mechanism 500, causing the rotary encoder's rotary shaft to rotate simultaneously.

[0031] The pulse signal output from the rotary encoder is input to the processing device 4 via the connection cable 61. The processing device 4 inputs this pulse signal in a state synchronized with the output signal from the magnetic detection device 2. As with the connection cable 60, the connection cable 61 may be a multi-pair shielded cable with excellent noise resistance, such as a twisted pair shielded wire. The above-mentioned rotary encoder is of an incremental type that performs counting measurement by accumulating pulses generated in response to rotations from the measurement start point, but it is also possible to use an absolute type rotary encoder that measures the absolute angular position of the rotary encoder's rotation shaft. Furthermore, rotary encoders come in a variety of types, including an optical type that irradiates a slit disk inside with light and converts rotational position information into a light pulse signal that passes through the slit disk, a magnetic type that outputs a pulse signal from periodic changes in the magnetic field as rotational position information from a rotating disk on which a magnetic pattern is formed, an electrostatic type that is composed of a transmitter, receiver, and rotor fixed inside the encoder and outputs distance information based on changes in electrostatic capacitance caused by the rotation of the rotor, and a contact type that converts an electrical signal generated when electrical contacts inside the encoder come into contact with the rotation of the rotating shaft into the amount of rotation, and any of these may be used. <Principle of detecting a singular point on a metal body>

[0032] 5 is a schematic diagram showing the operating state of this embodiment. When current is supplied from the processing device 4 (see FIG. 1), the oscillator coils 5A and 5B generate synchronized, phase-inverted AC magnetic fields. Specifically, the oscillator coils 5A and 5B are connected in series (or parallel) at the beginning or end of the insulated copper wire windings, and when an AC voltage is applied from the processing device 4, an AC magnetic field is generated.

[0033] When magnetic fluxes ΦA and ΦB generated from oscillator coils 5A and 5B propagate to running surface 200a of metal body 200 (see FIG. 1), a flow of magnetic flux occurs on running surface 200a. Here, the moving direction of mobile body 100, which is the moving direction of the magnetic detection device and distance movement device, is defined as the "x direction." Furthermore, the direction in which receiving coil 6 detects magnetic flux, i.e., the direction of separation between metal body 200 and magnetic detection device 2, is defined as the "y direction."

[0034] 5 shows an example of a metal body 200 in which there is no change in the state of the metal body near the oscillator coil 5A, oscillator coil 5B, and receiver coil 6. Here, a case in which there is no change in the state of the metal body refers to a case in which there is no damage due to defects such as cracks, or a case in which there is no structural change in the connection of the metal bodies (for example, a structure that is not damaged, such as a joint, welding, or bolt installation). The components of the magnetic flux linking to the receiver coil 6 are canceled out because the magnetic fluxes ΦA and ΦB are in opposite directions, and depend on the strength balance of the magnetic fluxes ΦA and ΦB. Therefore, when there is no change in the state of the metal body, the magnetic flux linkage of the receiver coil 6 becomes almost zero, and the induced voltage of the receiver coil 6 also becomes almost zero.

[0035] FIG. 6 is a schematic diagram showing another operation of this embodiment.

[0036] 6 shows an example in which a singular point 202 is present near the oscillator coil 5A, the oscillator coil 5B, and the receiver coil 6. Here, the singular point 202 refers to a point having magnetic properties different from those of a normal metal body 200. Specifically, the singular point 202 can be broadly classified into a "damaged point" and a "structural point." In other words, the singular point 202 is either a damaged point or a structural point.

[0037] The meanings of the "x direction" and "y direction" in Fig. 6 are the same as those in Fig. 5. In the example shown, the flow of magnetic flux generated on the running surface 200a of the metal body 200 by the oscillator coils 5A and 5B is disturbed, causing leakage of magnetic flux from the running surface 200a. Therefore, when the receiving coil 6 passes over the singular point 202, the change in induced voltage of the receiving coil 6 is significantly larger than the change in induced voltage in the case shown in Fig. 5.

[0038] The metal body monitoring device 1 in this embodiment detects the generated leakage magnetic field based on the fact that the flow of magnetic flux generated in the metal body 200, which is the object of inspection, changes in the vicinity of the singular point 202. As an analytical model of this leakage magnetic field, the leakage magnetic field generated in space can be expressed based on a dipole model. Here, as an example, let us assume that, in this model, the singular point 202 is a structure that is a joint with a gap in the metal body 200. In the leakage magnetic field component generated in the y direction of the metal body 200, a bipolar magnetic flux change appears that has extreme values ​​(maximum and minimum values) in the x direction, with the center position of the joint as an inflection point during movement of the mobile body 100.

[0039] In the above example, a structure in which the singular location is a joint with a gap in the metal body 200 was described, but the singular location 202 can also be detected in a similar manner if it is damaged. When a crack, which is damage, exists on the running surface 200a of the metal body 200, the gap at the damaged location has a significantly different magnetic permeability compared to the magnetic permeability of the metal body 200, causing magnetic resistance and disrupting the flow of magnetic flux. This disruption in the flow of magnetic flux causes the magnetic flux flowing on the running surface 200a to bypass the crack, generating leakage magnetic flux from the running surface 200a. Therefore, when the magnetic detection device 2 passes over the damaged location, the strength balance of the magnetic fluxes ΦA and ΦB (see FIG. 6 ) in the metal body 200 is disrupted, similar to the detection of the above-described structure, and the magnetic detection device 2 can detect the presence of the damaged location. <Monitoring of Metal Body>

[0040] Monitoring of the metal object 200 by the metal object monitoring device 1 will be described with reference to FIGS. 7A, 7B, and 7C.

[0041] The detection signal acquired by the magnetic detection device 2 is time-series data 111 representing a time series of detection signal strength (process 1 in FIG. 7A ). Therefore, the response waveform of the detection signal changes depending on the moving speed of the moving object 100, and is moving speed-dependent. Therefore, singular locations where damage or structures exist do not always produce the same signal response, making it difficult to intuitively grasp the response from the signal waveform even when signals from similar singular locations are present. Furthermore, if noise is mixed into the detection signal, the settings of the filter applied to remove noise must be changed depending on the moving speed, making its application complicated. For example, waveform 971 indicates a relatively long response time when the moving speed is relatively slow, and waveform 972 indicates a relatively short response time when the moving speed is relatively fast.

[0042] Therefore, the detection signal from the magnetic detection device 2 is captured by the processing device 4 in synchronization with the output signal from the distance detection device 3, and the processing device 4 converts the time-series data 111 of the detection signal into data 222 indicating changes in the detection signal strength with respect to the moving distance (processing 2 in FIG. 7A ). This converted data 222 is sampled at equal intervals if the moving speed of the moving body 100 is constant, but in reality, the movement of the moving body 100 is accelerated and decelerated, so the moving speed is not always constant, and therefore the sampling intervals are not equal.

[0043] Therefore, the processing device 4 converts the data 222 into data 333 with equal sampling intervals by interpolating the sampling of the converted data 222 obtained in process 2 in Fig. 7A (process 3 in Fig. 7A). The sampling intervals in the data 333 may be a set value, such as equal intervals of 1 mm, and the set value may be a value that matches the resolution required for monitoring the metal object to be inspected.

[0044] An example of sampling conversion accompanying interpolation of detection signals relative to movement distance will be described with reference to FIG.

[0045] In process 2, the processing device 4 assigns an ID to each moving distance from the signals acquired from the magnetic detection device 2 and the distance detection device 3, and stores data including the ID, the moving distance, and the detection signal strength relative to the moving distance. As shown in Fig. 8A, in this stored data, the distance intervals are not equal because they depend on the moving speed of the moving object 100.

[0046] Therefore, as shown in FIG. 8B, in process 3, the processing device 4 calculates the detection signal strength of the distance by interpolation (for example, linear interpolation) in accordance with the desired sampling interval (for example, the sampling interval represented by the set value). After calculation, the processing device 4 stores data on the detection signal strength for the movement distance of equal sampling with an interpolation process ID. FIG. 8B shows an example where sampling is performed at equal intervals of 1 mm. In this case, to calculate the detection signal strength for the distance of 1 mm shown in FIG. 8B from the stored data shown in FIG. 8A, the data is obtained by linear interpolation of the data on the detection signal strength between two points at distances of 0.5 mm and 1.3 mm, which sandwich the distance of 1 mm. This linear interpolation is performed using two points (X ｉ , Y ｉ ) and (X ｉ＋１ , Y ｉ＋１ ) and the approximation of the function Y for any point X is expressed as a linear function of X, connecting two points with a straight line, and Y ａ = Y ｉ + (Y ｉ＋１ -Y ｉ ) * (X ａ -X ｉ ) / (X ｉ＋１ -X ｉ ) where X ｉ ≦X ａ ≦X ｉ＋１ For example, the data when the distance is 1 mm shown in FIG. 8B (X ａ (=1), Y ａ ) is obtained by using the data (X ｉ (=0.5), Y ｉ (=1.1)) and the data at a distance of 1.3 mm (X ｉ＋１ (=1.3), Y ｉ＋１(=1.2)), and from the above formula, Y becomes 1.1625. Similarly, data at other distances (for example, a distance of 2 mm) is continuously calculated and processed within the processing device 4, and data on the detected signal strength at equal sampling intervals of 1 mm of moving distance is stored. Note that, although an example of intervals of 1 mm has been given in the explanation, the distance interval in the interpolation process can be set to any value by the processing device 4.

[0047] Because the data 333 obtained in process 3 in Figure 7A is uniformly sampled data, a spatial filter can be applied to the detection signal (process 4 in Figure 7B). Using this spatial filter makes it possible to extract only the signal components necessary for identifying the anomalous location. For example, signal changes over distances greater than the size of the magnetic detection device 2 performing signal detection or over distances smaller than the size of the receiver coil 6 built into the magnetic detection device 2 can be considered electrical noise caused by the movement of the mobile object 100 or disturbances during measurement. Therefore, the processing device 4 performs spatial filtering using a desired band-pass filter 444 consisting of a high-pass filter and a low-pass filter. This process 4 removes offset fluctuations and other artifacts from the detection signal, resulting in cleansed data 555. The band-pass filter 444 used in process 4 in Figure 7B is shown as an example of extracting detection signal components over a distance range of 20 mm to 1 m from the cutoff spatial frequency. The cutoff spatial frequency can be freely set depending on the metal object being inspected. In the spatial filtering process in process 4, the data 333 represents the change in detection signal strength relative to the moving distance, so the detection signal can be handled without being affected at all by the moving speed of the moving body 100.

[0048] The detection signal data 555 cleansed by process 4 is subjected to extraction and analysis processing of the spectral response of the detection signal in the selected inspection section 666 by the processing device 4 (process 5 in FIG. 7B ). The spectral response of the inspection section 666 selected in process 5 is subjected to continuous wavelet analysis by the processing device 4. Because the anomalous locations of the metal body 200 do not generally occur continuously with periodicity, the detection signal does not occur as a periodic change, but is observed as a sudden change. This continuous wavelet analysis allows for a variable area for extracting the detection signal in the travel distance region, so that a longer analysis area can be processed for the gradual fluctuation components of the detection signal, and a shorter analysis area can be processed for the fine fluctuation components of the detection signal. This allows for effective understanding of the non-stationary or transient fluctuation characteristics of the detection signal occurring in the anomalous location. In the continuous wavelet analysis according to process 5 in FIG. 7B shown as an example for explanation, Ω represents the spatial frequency resolution defined by the wavelet function used, and Δ represents the distance resolution defined by the wavelet function used.

[0049] In the continuous wavelet analysis, the processing unit 4 converts the detected signal (which is assumed to be g(t) for the sake of explanation) that has been data cleansed in the processing unit 4 into a wavelet function (ψ ａ，ｂ A continuous wavelet transform T(a, b) is performed by convolution integral at (t). This continuous wavelet transform T(a, b) is expressed by the following [Equation 1].

[0050] The continuous wavelet function is expressed by the following [Equation 2].

[0051] Note that the following part of [Equation 1] of the continuous wavelet transform, [Equation 3], is the complex conjugate of the continuous wavelet function, and ψ denotes the base of the wavelet function called the mother wavelet.

[0052] From T(a, b), a scale parameter a having information related to the spatial frequency and a shift parameter b having information related to the movement distance can be obtained, thereby determining the magnitude of the signal component at each spatial frequency for each position in the movement distance in the data-cleansed detection signal data 555. Typical continuous wavelet functions include the Mexican hat function, Meyer function, Morlet function, and Symlet function, and any of these functions can be used depending on the response of the metal object to be inspected. For example, when the Mexican hat function is used as the continuous wavelet function, the Mexican hat function has the shape of a second-order derivative of a Gaussian function, and the mother wavelet ψ is expressed by the following [Equation 4].

[0053] In combination, the wavelet function is as shown in the following [Equation 5].

[0054] In addition to the above functions, any waveform shape can be used for the continuous wavelet function, and if the shape of the response waveform to be obtained from the metal body 200 is known in advance, a wavelet function with a shape similar to that waveform can be created and wavelet transformation can be performed by the processing device 4. In continuous wavelet analysis, two parameters, a and b, are defined and processing is performed. a is a coefficient for expanding or contracting the wavelet function; if the value is increased, the wavelet function is stretched in the distance axis direction, and if the value is decreased, it is contracted in the distance axis direction. Furthermore, b is a coefficient for shifting the wavelet function in the distance axis direction. In the continuous wavelet analysis (process 5 in Figure 7B) shown as an example for explanation, the image of the expansion or contraction operation of the wavelet function can be obtained by setting the parameter a to large, medium, or small (a １ , a ２ , a ３ ) is used as an example.

[0055] Based on the information obtained by the wavelet analysis in process 5, the processing device 4 performs a process of displaying a color contour showing a signal intensity bar of the detection signal, with the vertical axis representing spatial frequency and the horizontal axis representing the moving distance of the mobile object 100 (process 6 in Figure 7B). Because the spatial frequency components of the detection signal vary depending on the type of anomalous location, the spectral response (pattern, spatial frequency range in which the signal occurs, etc.) of the anomalous location extracted in the color contour display enables monitoring including anomalous location information. This color contour display allows the visualization of anomalous locations by using the spectral response in wavenumber space of the detection signal data 555 cleansed by the spatial filter (bandpass filter 444), which is also effective in terms of usability. For example, since structures exist over a relatively large area, they exhibit a spectral response in which signal intensity increases in the low spatial frequency range (see reference numeral 89). On the other hand, damage such as cracks exists locally, resulting in a spectral response in the high spatial frequency range (see reference numeral 88). Furthermore, when the metal object being inspected is infrastructure equipment, welds and other joints and structures for maintaining the equipment are present at specific intervals, while abnormal areas in the metal object due to damage are likely to occur irregularly. Therefore, it is possible to identify (discriminate) structures and damage from differences in spectral response in wavenumber space and the regularity of response patterns that occur in the inspection section.

[0056] 7A and 7B may be implemented as separate processes. The detection signal from the magnetic detection device 2 acquired by the metal object monitoring device 1 is subjected to data cleansing by spatial filtering in process 4, but there are cases where observation noise originating from the device or usage environment cannot be efficiently removed. In such cases, an appropriate response to the noise contained in the detection signal after process 4 is required. To address this, before performing the continuous wavelet analysis in process 5 in FIG. 7B, the processing device 4 separates the detection signal in the form of a linear combination of different resolution components using a discrete wavelet transform (FIG. 9).

[0057] In this discrete wavelet transform, the scale parameter a in the continuous wavelet transform is set to 2 ｊ and the shift parameter b is 2 ｊIn this binary division process, the discrete wavelet function is expressed by the following [Equation 6].

[0058] The j shown in the discrete wavelet function is called the level and represents an expansion or contraction by a factor of two. Note that j and k are integers. The detection signal (denoted as g(t) for the sake of explanation) from the magnetic detection device 2, which has undergone data cleansing through spatial filtering processing in process 4, can be expanded into a series using the discrete wavelet function as a basis, and can be separated as the value of j increases. Series expansion refers to the process of separating the signal, and the detection signal is expressed by the following [Equation 7].

[0059] The following [Equation 8] in the series expansion ([Equation 7]) is the discrete wavelet coefficient.

[0060] The discrete wavelet transform S(j, k) in a similar format to the continuous wavelet processing is expressed by the following [Equation 9].

[0061] This series expansion corresponds to approximation processing using a linear combination of wavelet functions, and the approximation accuracy of the detection signal g(t) using this approximation processing depends on the level, with the larger the level value, the coarser the approximation. That is, the approximation function of level j of the signal g(t) is expressed as a series by the following [Equation 10]. A level of 0 indicates the detection signal g(t) with the highest accuracy.

[0062] The detection signal g(t) is a continuous signal, and N values ​​(a １ ...a Ｎ ) In this case, when performing discrete wavelet processing, the number of data N is a power of 2, and N=2 ｎ The approximation of the detection signal g(t) is performed hierarchically, and the flow of approximation is explained in FIG. 9. For the sake of explanation, FIG. 9 shows the number of data N when the detection signal g(t) is at level 0 as 8 (=2 ３ This hierarchical approximation is an example of a hierarchical approximation when eight data (a １ ...a８ ) is replaced by the average value for every two consecutive data. １ ...a ８ ) is four data ((a １ +a ２ ) / 2, (a ３ +a ４ ) / 2, (a ５ +a ６ ) / 2, (a ７ +a ８ ) / 2). This means that the same process is then carried out to replace every two consecutive pieces of data with the average value of the four pieces of data that have been replaced.

[0063] When performing this hierarchical approximation, the detection signal g(t) is hierarchically decomposed by calculating data h(t) obtained by subtracting data before and after the approximation. Based on the example shown in FIG. 9, hierarchical decomposition is explained in FIG. 10. The data at level 0, g ０ (t) is the approximate data g １ (t) to h １ Next, the data at level 1, g １ (t) to h ２ (t) can be decomposed. Similarly, at level 2, g0(t) is ３ (t) and h ３ (t) where the original data g ０ (t) is g ３ (t) and h １ (t) and h ２ (t) and h ３ (t). Therefore, the discrete wavelet coefficients of the detection signal g(t) are calculated under the conditions for each level value within a desired range, and the calculated discrete wavelet coefficients are smoothed using a certain threshold T to remove noise. The threshold processing used for this noise removal can be hard threshold processing or soft threshold processing. Hard threshold processing is performed using wavelet coefficients (see Equation 11 below).

[0064] In hard thresholding, a decision is made as to whether to leave the discrete wavelet coefficients as they are or remove them. In contrast, in soft thresholding, the discrete wavelet coefficients are considered to contain both signal and noise, and the noise portion is removed from all discrete wavelet coefficients to separate the signal. This soft thresholding results in wavelet coefficients (see Equation 12 below).

[0065] Here, all discrete wavelet coefficients less than the threshold T are set to 0, and all discrete wavelet coefficients with magnitudes equal to or greater than the threshold are reduced toward 0 by T. Note that the thresholding process is performed on the detection signal g ｊ A preferred value may be selected according to (t). A universal threshold T is used for threshold processing, and the threshold at level j is as shown in [Equation 13] below.

[0066] Here, n ｊ is the length (number) of discrete wavelet coefficients at level j, and σ ｊ is the target detection signal g ｊ is the standard deviation of the noise contained in (t). ｊ Since the value of is unknown, the median value of the absolute deviation of the discrete wavelet coefficients at the minimum scale is divided by 0.6745 as an estimate of the standard deviation, and a value adjusted to the standard deviation of the Gaussian distribution is used. In addition to the above-mentioned method using a universal threshold, threshold processing can also be performed using the mini-max method, the SURE method, the HYBRID method, the cross-validation method, the Lorenz method, the Bayesian approach, etc., and any of these may also be used. As described above, the discrete wavelet coefficients obtained by the discrete wavelet transform are subjected to threshold processing at each level j according to the threshold, and the detection signal g is obtained by the inverse discrete wavelet transform. ｊ (t) is reconstructed. This reconstructed detection signal g ｊ (t) is subjected to continuous wavelet transformation in process 5 shown in FIG. 7B, and a spectral response in wavenumber space is output from the continuous wavelet transformed data using color contour display shown in process 6.

[0067] Alternatively, in metal object monitoring, process 5 may be process 5 shown in FIG. 7C . This can be applied when the detection signal constantly exhibits periodic fluctuations and a specific location within the fluctuations is to be detected. In this process, instead of the continuous wavelet analysis performed in process 5 in FIG. 7B , the processing device 4 performs short-time Fourier transform analysis of a predetermined section 1111 set, for example, 1 m, in the area where the detection signal of the section to be inspected exists, shifting the section by 1 mm, and then performs color contour display processing of the spectral response similar to process 6 in FIG. 7B from the obtained results. Continuous wavelet analysis is a superior analysis method that achieves both better moving distance and spatial frequency resolution than short-time Fourier transform. However, when monitoring a metal object with a surface shape consisting of twisted wires called strands, such as a wire rope, periodic fluctuations reflecting the strands occur in the detection signal, and short-time Fourier transform may be more suitable than wavelet analysis for analysis involving periodic fluctuations. <Circuit Configuration of the First Embodiment>

[0068] FIG. 11 is a block diagram showing the overall configuration of a metallic object monitoring device 1 according to this embodiment.

[0069] As described above, the metal body monitoring device 1 has the magnetic detection device 2, the distance detection device 3, the processing device 4, and the power supply 88. The magnetic detection device 2 has the magnetic sensor unit 21 and the preamplifier unit 210, and the magnetic sensor unit 21 has the oscillator coil 5A, the oscillator coil 5B, and the receiving coil 6. The preamplifier unit 210 has an amplifier filter circuit 22, and the amplifier filter circuit 22 is connected to the magnetic sensor unit 21.

[0070] The processing device 4 also includes an amplifier 31, a digital-to-analog converter 32, an oscillator 33, a detector 34, an analog-to-digital converter 35, a memory 36, and an evaluation device 7. The oscillator 33 outputs a sinusoidal digital oscillation signal at a predetermined oscillation frequency f. The oscillation frequency f is selected to output a sufficient excitation magnetic field while ensuring sufficient sensitivity of the receiver coil 6, taking into account the impedance of the oscillator coils 5A and 5B. When selecting the oscillation frequency f, it is preferable to fully consider its influence on the metal object monitoring inspection. Specifically, the selected frequency range is preferably selected from 10 kHz to 100 kHz to ensure sufficient separation from 1 / f noise, line noise (50 Hz or 60 Hz) and its harmonics, and sufficient magnetic flux flow even with the influence of the skin effect of the metal object being inspected.

[0071] 11 , a digital-to-analog converter 32 converts the digital oscillation signal output by an oscillator 33 into an analog AC voltage. An amplifier 31 amplifies this AC voltage and applies it to oscillator coils 5A and 5B in the magnetic sensor unit 21. As a result, an AC magnetic field with an inverted phase is generated from the oscillator coils 5A and 5B. The magnetic detection device 2 mounted on the mobile object 100 is spaced apart from the travel surface 200a (see FIG. 1 ) so as not to interfere with the movement of the mobile object 100, and therefore outputs an AC magnetic field from the oscillator coil with a strength sufficient to sufficiently excite the travel surface 200a.

[0072] The amplifier filter circuit 22 in the magnetic detection device 2 amplifies and filters the signal from the receiving coil 6 and transmits the result to the detector 34 of the processing device 4. This "filtering" is a low-pass filter that removes frequency components higher than the upper limit of the oscillation frequency f in the variable oscillation frequency range, taking into account the possibility of allowing flexibility in the setting of the oscillation frequency f, or a high-pass filter that removes frequency components lower than the lower limit of the oscillation frequency f in the variable oscillation frequency range. If the oscillation frequency f to be used is fixed, band-pass filter processing may be used that passes only a bandwidth centered on the oscillation frequency f and that includes a frequency range that satisfies the response speed at the maximum moving speed of the mobile object 100. Furthermore, the detector 34 uses the reference signal SR1 supplied from the oscillator 33 to generate signals X, Y, R, and θ (details of these signals will be described later) based on the signal supplied from the amplifier filter circuit 22, and supplies these signals to the analog-to-digital converter 35. The analog-to-digital converter 35 converts each analog signal received from the detector 34 into a digital signal.

[0073] In this embodiment, a signal from the distance detection device 3 is supplied to the processing device 4. This distance detection device 3 (see FIG. 1 ) detects the rotation angle of the wheels 300 of the mobile object 100 via a rotation drive mechanism 500 connected to an axle 400, and the distance detection device 3 outputs a pulse signal SP in response to rotation with a predetermined accuracy. Then, an analog-to-digital conversion unit 35 converts the pulse signal SP into a digital signal and stores it in a memory unit 36. The digital signal output from the analog-to-digital conversion unit 35 is stored as data in the memory unit 36, and information on the travel distance of the mobile object 100 is supplied to an evaluation device 7. The evaluation device 7 counts the pulse signals SP to calculate the travel distance of the mobile object 100.

[0074] Next, the evaluation device 7 will be described. The evaluation device 7 is a hardware device that is a typical computer and includes a CPU (Central Processing Unit), RAM (Random Access Memory), ROM (Read Only Memory), and HDD (Hard Disk Drive), and the HDD stores an OS (Operating System), application programs, various data, etc. The OS and application programs are loaded into the RAM and executed by the CPU.

[0075] The evaluation device 7 includes a control unit 42, a data processing unit 43, an output processing unit 44, an operation input unit 45, a display unit 46, and a storage unit 47. The evaluation device 7 executes an inspection processing program that identifies a singular location 202 (see FIG. 6 ) of the metal body 200 based on the metal body monitoring data received from the magnetic detection device 2, the detection unit 34, the analog-to-digital conversion unit 35, and the memory unit 36. In this embodiment, the "metal body monitoring data" refers to data at all stages from the receiving coil 6 of the magnetic detection device 2 to the evaluation device 7.

[0076] The control unit 42 is, for example, a CPU (an example of a processor) and controls the reading of test data from the memory unit 36 ​​and arithmetic processing. The data processing unit 43 performs test processing based on the test data (details will be described later). The display unit 46 is an LCD (Liquid Crystal Display), CRT (Cathode Ray Tube) display, etc. that displays test results, etc. The output processing unit 44 displays the results of metal body monitoring, etc. on the display unit 46. In this case, the output processing unit 44 performs processing to display the results in a visually easy-to-understand display format, appropriately using graphs or tables. The operation input unit 45 is information input means such as a keyboard, mouse, or touch panel. The memory unit 47 saves data such as test results processed by the data processing unit 43. In addition, the data stored in the memory unit 36 ​​is also transferred to the memory unit 47. The data processing unit 43 and the output processing unit 44 are realized by loading the programs and data stored in the storage unit 47 into the control unit 42 and executing arithmetic processing.

[0077] FIG. 12 is a block diagram of the detector 34. The received signal SS from the amplifier filter circuit 22 is supplied to phase comparators 74 and 76. The reference signal SR1 supplied from the oscillator 33 (see FIG. 8) is delayed by a delay circuit 72 by a time corresponding to a phase difference of 90° from the oscillation frequency f. The delayed reference signal SR1 is referred to as reference signal SR2. The reference signal SR1 is supplied to a phase comparator 76, and the reference signal SR2 is supplied to the phase comparator 74. The phase comparator 76 extracts a component of the received signal SS that is synchronized with the reference signal SR1. The extracted signal is filtered by an LPF (low-pass filter) 80, which outputs the result of the filtering as a cosine signal X.

[0078] Furthermore, the phase comparator 74 extracts a component of the received signal SS that is synchronized with the reference signal SR2. The extracted signal is filtered by the LPF 78, which outputs the result as a sine signal Y. The calculator 84 calculates the sine signal Y by the formula: √(X ２ +Y ２) and outputs the result as an amplitude signal R. Furthermore, calculator 82 calculates the arc tangent of (Y / X), that is, a tan(Y / X), and outputs the result as a phase difference signal θ.

[0079] The detection unit 34 then supplies the above-mentioned signals X, Y, R, and θ to the memory unit 36 ​​via the analog-to-digital conversion unit 35 (see FIG. 11 ). In the example shown, the detection unit 34 outputs all of the signals X, Y, R, and θ, but the amplitude signal R and the phase difference signal θ may be calculated by a data processing unit 43 (see FIG. 11 ) based on the cosine signal X and the sine signal Y, rather than by the detection unit 34.

[0080] Here, we will explain why the detection unit 34 detects the sine signal Y in addition to the cosine signal X. Information related to metal object monitoring (changes in amplitude in the detection signal) can be obtained using either the cosine signal X or the sine signal Y. On the other hand, if only the cosine signal X is used, adjustment is required to set the phase of the reference signal so that the amplitude of the cosine signal X is maximized during inspection. This phase setting must be adjusted each time an inspection condition changes, for example, when the target metal object changes, when the distance between the metal object and the magnetic detection device changes, or when the excitation frequency of the oscillator coil in the magnetic detection device changes. Therefore, by simultaneously detecting the cosine signal X and the sine signal Y, the phase difference can be calculated, enabling inspections to be performed without phase adjustment. Furthermore, not only changes in amplitude but also changes in phase difference in the detection signal can be obtained simultaneously, thereby obtaining more detailed metal object monitoring information. Furthermore, the value of the amplitude signal R calculated by the calculator 84 (or the evaluation device 7) is, in principle, constant even when the phase difference signal θ fluctuates, which has the advantage of omitting the process of optimizing the phase of the reference signal. <Operation in First Embodiment>

[0081] FIG. 13 is a flowchart of the metal object monitoring data processing executed by the data processing unit 43 of the evaluation device 7.

[0082] This process is executed at every predetermined control cycle. In FIG. 10 , the processes of steps S2 and S14 are executed in parallel. First, the evaluation device 7 acquires metal object monitoring data from the memory unit 47 in step S2, and acquires the distance pulse signal SP from the memory unit 47 in step S14. After completing step S14, the evaluation device 7 converts the distance pulse signal SP into distance data SK in step S16. That is, the evaluation device 7 calculates the distance data SK based on the number of times the distance pulse signal SP is detected by the distance detection device until the movement of the moving object stops. Here, the distance data SK refers to the distance from a predetermined reference position (the movement start position of the moving object), and the distance data SK may be an example of position data.

[0083] After completing step S2, the evaluation device 7 acquires spectral response data in wavenumber space using the distance data SK in step S4. After completing step S4, it is determined whether the spectral response data falls outside a predetermined reference range, i.e., a range of signal strength for which a lower limit value is set. Here, the "spectral response data" used for the determination is, for example, the amplitude signal R shown in FIG. 12. If there is a singular point 202 (see FIG. 6), the amplitude signal R will clearly increase. Therefore, in step S6, the evaluation device 7 calculates a value between the amplitude signal R and a predetermined threshold value R. ｔｈ１ Compare with "R ≠ R ｔｈ１ ", it may be determined as "Yes", that is, that there is a possibility that a singular point exists. ｔｈ１ (which may include the case where the difference is equal to or smaller than a predetermined difference), it may be determined as "No", that is, that there is no singular location.

[0084] If the determination here is "No," the process proceeds to step S18. The details of this process will be described later. On the other hand, if the determination in step S6 is "Yes," the process proceeds to step S8. In step S8, the structure and damage are discriminated based on the spatial frequency region in which an increase in signal intensity occurs, such as the signals X, Y, R, and θ shown in FIG. 12.

[0085] When the processing of steps S2 to S16 described above is completed, the process proceeds to step S18, where a metal object monitoring result is generated. Here, the "metal object monitoring result" may include a correlation between peculiar locations (structures and damage) and distance data SK if step S8 was performed (if step S8 was not performed, the "metal object monitoring result" may include a result indicating that no peculiar locations were found). Next, when the process proceeds to step S20, the data processing unit 43 outputs the metal object monitoring result to the display unit 46 or the like, and the processing of this routine ends. <Effects of the First Embodiment>

[0086] As described above, according to the metallic object monitoring device 1 of this embodiment, the magnetic detection device 2 includes a lower housing 20 and an upper housing 26. The lower housing 20 houses the magnetic sensor unit 21 and is made of a non-magnetic material. The upper housing 26 houses the preamplifier unit 210 and is fixed to the upper surface of the lower housing 20. A connector 28 connected to a connection cable 64 is attached to one side of the upper housing 26. The upper housing 26 is made of metal. This allows the state of the metallic object 200 to be detected stably.

[0087] The magnetic detection device 2 excites the running surface 200a of the metal body 200 with an AC magnetic field, and detects a singular point 202 on the running surface based on the disturbance of the flow of magnetic flux that occurs on the running surface, thereby enabling accurate detection of the state of the metal body.

[0088] The metal object monitoring device 1 further has an output processing unit 44. The output processing unit 44 displays the type and location of the detected peculiar point 202 as an image on the display unit 46. This allows the user to visually grasp the condition of the metal object 200.

[0089] Furthermore, the evaluation device 7 identifies the peculiar location 202 based on the discrimination result for the peculiar location 202, and can manage the structure and damage as information on the peculiar location that existed in the metal body 200. Furthermore, the processing device 4 detects the position of the peculiar location based on the distance pulse signal SP from the distance detection device 3, and displays the result on the display unit 46. [Second embodiment]

[0090] Next, a second embodiment of the present invention will be described. In the following description, parts corresponding to those in FIGS. 1 to 12 are given the same reference numerals, and their description may be omitted. In addition, the following description will mainly focus on differences from the first embodiment, and descriptions of commonalities with the first embodiment will be omitted or simplified. <Configuration of the second embodiment>

[0091] The configuration of this embodiment is based on the configuration of FIG. 3 described in the first embodiment, and uses a plurality of magnetic sensor units as shown in FIG. 14. FIG. 14 is a bottom view of a magnetic detection device 2 incorporating a plurality of magnetic sensor units. As shown in FIG. 14, the magnetic sensor unit group 77 has N magnetic sensor units 21-1 to 21-N (N is an integer of 2 or more, as described above, in this embodiment) arranged in a row along the width direction of the lower housing 20, i.e., the width direction of the metal body 200 (see FIG. 1).

[0092] The magnetic sensor unit 21-k (k is any integer between 1 and N) includes an oscillator coil 5A-k, an oscillator coil 5B-k, and a receiver coil 6-k. Each of these coils is formed by winding a coated copper wire.

[0093] The oscillator coil 5A-k, the receiver coil 6-k, and the oscillator coil 5B-k are arranged along the width direction (the direction perpendicular to the movement direction) of the metal body 200 (see FIG. 1), and the receiver coil 6-k is arranged so as to be equidistant between the oscillator coil 5A-k and the oscillator coil 5B-k. Alternating currents with different frequency values ​​are supplied to the oscillator coils 5A-k and 5B-k from the processing device 4 (see FIG. 1) via a connection cable 60. As a result, alternating current magnetic fields with different frequency values ​​are generated from the oscillator coils 5A-k and 5B-k, which excite the metal body 200, and the magnetic flux generated from the excited metal body 200 generates an induced voltage in each receiver coil 6-k.

[0094] Fig. 15 is a partially cutaway plan view of the magnetic detection device 2 in the second embodiment. As with Fig. 3, Fig. 14 is an explanatory view in which the center of the flange 25 is cut away to expose the inside of the upper housing 26. The configuration of the housing structure and materials used for the magnetic sensor unit are the same as those in the first embodiment.

[0095] 15, the preamplifier unit 210 includes a printed circuit board 210a on which the amplification filter unit group 79 is mounted. The amplification filter circuit group 79 includes the same number (N) of amplification filter circuits 22-1 to 22-N as the magnetic sensor unit 21. The magnetic sensor unit 21 and the amplification filter circuit 22 may have a one-to-one correspondence.

[0096] The amplification filter unit 22-k (k is any integer between 1 and N) amplifies and filters the induced voltage generated in the receiving coil 6-k, and transmits the result to the processing device 4 via the connection cable 60 (see FIG. 1). The processing device 4 analyzes the received signal and detects the magnetic signal generated from the metal body 200. <Circuit Configuration of Second Embodiment>

[0097] FIG. 16 is a block diagram showing the overall configuration of a metallic object monitoring device 1 according to this embodiment.

[0098] In the metal object monitoring device 1 of this embodiment, the magnetic detection device 2 has magnetic sensor units 21-1 to 21-N and a preamplifier unit 210, and the magnetic sensor unit 21-k has oscillator coils 5A-k and 5B-k and a receiver coil 6-k. The preamplifier unit 210 has amplifier filter units 22-1 to 22-N, and the amplifier filter unit 22-k is connected to the magnetic sensor unit 21-k.

[0099] The processing device 4 also includes amplifiers 31-1 to 31-N, N digital-to-analog converters 32, an oscillator 33, detectors 34-1 to 34-N, an analog-to-digital converter 35, a memory unit 36, and an evaluation device 7 (in this embodiment, N is an integer greater than or equal to 2, but may also be 1). In FIG. 16, the digital-to-analog converter 32 converts the digital oscillation signal output by the oscillator 33 into an analog AC voltage. An amplifier 31-k (k is an integer between 1 and N) amplifies this AC voltage and applies it to the oscillator coils 5A-k and 5B-k in the magnetic sensor unit 21-k via a resonant filter 60-k. This generates phase-inverted AC magnetic fields from the oscillator coils 5A-k and 5B-k.

[0100] In this embodiment, the spectral response in wavenumber space derived by the processing described in Figures 7A, 7B, 7C, 8A, 8B, 9, and 10 using the signals from the magnetic detection device 2 and the distance detection device is obtained for the entire area of ​​the metal body 200 occupied by the multiple magnetic sensor units 21 used. Therefore, when extracting anomalous locations, the response change in signal intensity at a specific spatial frequency in the measurement area of ​​the metal body 200 can be determined, allowing for more detailed metal body monitoring, such as understanding the distribution of damage occurrence. In this embodiment, the signal components at a specific spatial frequency obtained by the multiple magnetic sensor units 21 used can be used to create a contour representation of the location of anomalous locations present on the metal body and the signal intensity distribution at that location, as shown in Figure 17. In Figure 17, the vertical axis represents the position information of the magnetic sensor units 21 used, the horizontal axis represents the travel distance of the mobile object 100, and a color contour display is shown with signal intensity bars for the detection signal components at a specific spatial frequency. Among the peculiar locations, structures are artificial, and therefore exist throughout the area measured by each magnetic sensor unit at their location. Therefore, as shown by reference numeral 1000 in FIG. 17 , the signal intensity is uniform at each measurement position of the magnetic sensor unit 21 at the location of the structure. On the other hand, unlike structures, damage occurs naturally, and is not uniform like a structure. In other words, the signal intensity obtained at each measurement position of the magnetic sensor unit 21 reflects the area where the damage occurred, and exhibits a non-uniform distribution showing a bias, as shown by reference numeral 2000 in FIG. 17 . Note that any value can be set and input for the specific spatial frequency, and the location of the peculiar location present on the metal body and a contour representation of the signal intensity distribution at that location are output and displayed on the display unit 46 by the evaluation device 7. Such an output and display of a contour representation may be an example of a display of monitoring information. [Modification]

[0101] The present invention is not limited to the above-described embodiments and various modifications are possible. The above-described embodiments are provided as examples to facilitate understanding of the present invention and are not necessarily limited to those including all of the described components. Furthermore, it is possible to replace part of the configuration of one embodiment with the configuration of another embodiment, or to add the configuration of another embodiment to the configuration of one embodiment. Furthermore, it is possible to delete part of the configuration of each embodiment, or to add and / or replace other configurations. Furthermore, the control lines and information lines shown in the figures are those considered necessary for explanation, and do not necessarily represent all control lines and information lines necessary for the product. In reality, it is acceptable to assume that almost all components are interconnected. Possible modifications to the above embodiments include, for example, the following. (1) Because the hardware of the evaluation device 7 in each of the above embodiments can be implemented by a general-purpose computer, the programs for executing the signal processing shown in FIGS. 7A, 7B, 7C, 8A, 8B, 9, and 10 and the processing according to the flowchart shown in FIG. 13 may be installed on a computer from a program source such as a portable storage medium or a server. (2) In the above embodiments, the functions shown in Figures 11, 12, and 16 and the processes described based on Figures 7A, 7B, 7C, 8A, 8B, 9, 10, and 13 were described as functions and processes realized by a processor executing a program. However, some or all of these functions and processes may be replaced with hardware functions and processes using an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array). (3) In each of the above embodiments, the metal object monitoring device 1 may be installed on any mobile object, including manual objects such as pushcarts and self-propelled objects such as those powered by a motor or diesel engine. (4) In addition to the distance pulse signal SP using the encoder described in the embodiments, the distance detection device 3 can also be used in a self-propelled mobile object equipped with a tachometer generator. Alternatively, alternative means such as estimating the travel distance based on acceleration information obtained from an acceleration sensor or measuring the travel distance using laser Doppler may be applied.

[0102] The above description can be summarized as follows, for example. The following summary may include supplementary and modified versions of the above description.

[0103] The metal object monitoring device (1) includes a magnetic detection device (2), a distance detection device (3), and a processing device (4) connected to the magnetic detection device and the distance detection device. The magnetic detection device includes a magnetic sensor unit (21) that is provided on a mobile object (100) that moves relatively over a metal object (200) and that contactlessly detects leakage magnetic fields generated by AC excitation of the metal object. The distance detection device is provided on the mobile object and detects the relative movement distance of the mobile object with respect to the metal object. The processing device converts first data (111) representing a time series of detection signal strength from the magnetic detection device into second data (222) representing the relationship between movement distance and detection signal strength based on the movement distance at each time point detected by the distance detection device. The processing device extracts a spectral response in wavenumber space identified from the second data and outputs monitoring information based on the extracted spectral response. A singular location on the metal object can be identified from the monitoring information. Furthermore, since the offset drift caused by the oscillation of the moving body, which is a noise factor, has a different distance range, it is easy to remove the offset drift regardless of the speed. Note that the monitoring information may include information indicating the results of distinguishing the peculiar part of the metal body (for example, information indicating the moving distance and the identity of the peculiar part). Furthermore, the peculiar part may be broadly classified into a damaged part and a structural part, and the structural part may be identified (estimated) as to which type of part it is from among multiple types of parts classified as structural parts.

[0104] The monitoring information may be output by displaying the monitoring information (for example, on a display unit). The monitoring information may be information representing the correspondence between spatial frequency and the distance traveled by the moving object. Since the spatial frequency components of the detection signal differ depending on the type of peculiar location, it is possible to distinguish the peculiar location from such monitoring information. The correspondence between spatial frequency and the distance traveled by the moving object may be a color contour in an orthogonal coordinate system with a first axis corresponding to the spatial frequency and a second axis corresponding to the distance traveled by the moving object, and a color indicator according to the intensity of the detection signal may be arranged in the orthogonal coordinate system. This is expected to provide high visibility.

[0105] The processing device may perform spatial filtering on the second data according to a size required for monitoring the metal object, and extract a spectral response in wavenumber space for each section (666) corresponding to the monitoring of the metal object from the spatially filtered data (555). The spectral response in wavenumber space obtained after data cleansing using the spatial filter can provide visualization of the extraction and separation of anomalous areas (distinguishing between structural areas and damaged areas).

[0106] The processing device may perform interpolation of the movement distance samples so that the movement distance samples in the second data are evenly spaced. The spatial filtering may be performed on the interpolated second data. Since the movement distance samples are thus interpolated so that they are evenly spaced, the spatial filtering becomes possible.

[0107] For each section of the second data, the processing device may perform continuous wavelet analysis to extract a spectral response in wavenumber space. This makes it possible to identify, for each section, which spatial frequency components have changed at which moving distances. Note that for each section of the second data, the processing device may perform signal separation using discrete wavelet analysis to extract a spectral response in wavenumber space, and then use continuous wavelet analysis on the separated signals.

[0108] For each section of the second data, the processing device may perform a short-time Fourier transform to extract a spectral response in wavenumber space, where the metal object may have shapes that are periodically arranged along the direction of relative movement of the moving object, thereby allowing periodic changes in the structure of the metal object to be detected.

[0109] The magnetic detection device may have a plurality of magnetic sensor units arranged in a direction perpendicular to the direction of relative movement of the moving body.

[0110] REFERENCE SIGNS LIST 1 Metal monitoring device 2 Magnetic detection device 3 Distance detection device 4 Processing device 5, 5A-1 to 5A-N, 5B-1 to 5B-N Oscillating coil 6, 6-1 to 6-N Receiving coil 21 Magnetic sensor unit 100 Mobile body 200 Metal body 202 Singular location (structural location and damaged location present in the metal body)

Claims

1. A metal object monitoring device comprising: a magnetic detection device provided on a moving object that moves relatively over a metal object, the magnetic detection device having a magnetic sensor unit that detects in a non-contact manner a leakage magnetic field generated from the metal object; a distance detection device provided on the moving object and detecting the relative movement distance of the moving object with respect to the metal object; and a processing device connected to the magnetic detection device and the distance detection device, wherein the processing device converts first data representing a time series of detection signal strength from the magnetic detection device into second data representing the relationship between movement distance and detection signal strength, based on the movement distance at each point in time detected by the distance detection device, extracts a spectral response in wave number space specified from the second data, and outputs monitoring information which is information based on the extracted spectral response.

2. A metal object monitoring device as described in claim 1, wherein the output of the monitoring information is a display of the monitoring information, and the monitoring information is information expressing a correspondence relationship between a spatial frequency and a moving distance of the moving object.

3. A metal object monitoring device as described in claim 2, wherein the correspondence is a color contour of an orthogonal coordinate system having a first axis corresponding to spatial frequency and a second axis corresponding to the moving distance of the moving object, and a color indicator according to the detection signal strength is arranged in the orthogonal coordinate system.

4. The metal object monitoring device according to claim 1, wherein said processing device performs spatial filtering on said second data in accordance with a size required for monitoring said metal object, and extracts a spectral response in wavenumber space from the spatially filtered data for each section in accordance with the size of the metal object to be monitored.

5. The metal object monitoring device according to claim 4, wherein said processing device performs sample interpolation processing so that samples of the travel distance in said second data are equally spaced, and said spatial filtering processing is performed on the second data that has been subjected to said interpolation processing.

6. The metallic object monitoring device according to claim 4, wherein for each of said sections, said processing device performs continuous wavelet analysis to extract a spectral response in wavenumber space.

7. The metallic object monitoring device according to claim 4, wherein for each of said sections, said processing device performs signal separation with discrete wavelet analysis to extract the spectral response in wavenumber space, and uses continuous wavelet analysis on the separated signals.

8. The metallic object monitoring device according to claim 4, wherein for each section, the processing device performs a short-time Fourier transform to extract the spectral response in wavenumber space.

9. The metallic object monitoring device according to claim 8, wherein the metallic objects have shapes that are periodically arranged along the relative movement direction of the moving object.

10. The metal object monitoring device according to claim 1, wherein the magnetic detection device has a plurality of magnetic sensor units arranged in a direction perpendicular to the relative movement direction of the moving object.

11. A metal object monitoring method comprising the steps of: converting first data representing a time series of detection signal strength from a magnetic detection device into second data representing the relationship between movement distance and detection signal strength based on the movement distance at each point in time detected by a distance detection device; extracting a spectral response in a wave number space specified from the second data; and outputting monitoring information which is information based on the extracted spectral response, wherein the magnetic detection device is a device provided on a moving body that moves relatively over a metal object and has a magnetic sensor unit that detects leakage magnetic field generated from the metal object in a non-contact manner, and the distance detection device is a device provided on the moving body and detects the relative movement distance of the moving body with respect to the metal object.

12. A computer program that causes a computer to execute the following steps: converting first data representing a time series of detection signal strength from a magnetic detection device into second data representing the relationship between movement distance and detection signal strength based on the movement distance at each point in time detected by a distance detection device; extracting a spectral response in a wave number space specified from the second data; and outputting monitoring information that is information based on the extracted spectral response; wherein the magnetic detection device is a device provided on a moving body that moves relatively over a metal body and has a magnetic sensor unit that detects leakage magnetic field generated from the metal body in a non-contact manner; and the distance detection device is a device provided on the moving body and detects the relative movement distance of the moving body with respect to the metal body.

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