Monitoring system, monitoring method, and program

Abstract

The monitoring system (10) comprises a signal acquisition unit (12) that acquires vibration data at each of a plurality of sensing points in an optical fiber cable laid along a transportation infrastructure including a plurality of sections with different structures; a raw data set processing unit (14) that acquires time-distance vibration data of the road based on the vibration data; a passband processing unit (16) that acquires filtering parameters for the time-distance vibration data of each section of the road based on the structural characteristics or dimensional characteristics of each section of the road; a waterfall processing unit (18) that uses the filtering parameters to remove noise from the time-distance vibration data of each section of the road to acquire waterfall data; and a trajectory detection unit (20) that identifies the trajectory of a moving object passing through the road from the waterfall data.

Description

[Technical Field]

[0001] The present disclosure relates to a monitoring system, a monitoring method, and a non-transitory computer-readable medium. [Background technology]

[0002] Distributed acoustic sensing technology is a technology that acquires vibration signals (acoustic signals) around optical fiber cables. In general DAS technology, pulsed light is transmitted through optical fiber cables installed along road infrastructure. Distributed acoustic sensing devices acquire vibration signals from infrastructure around the road by analyzing the Rayleigh backscattered light of the pulsed light.

[0003] Vibration patterns generated by vehicular traffic are measured by fiber optic cables laid on the road. Traffic monitoring applications observe these vibration patterns in real time and continuously monitor traffic characteristics such as vehicle speed and vehicle traffic counts to maintain the smooth flow of traffic.

[0004] Trajectory features are estimated from a 3D waterfall dataset obtained from post-processing of the measured vibration signals, which is a commonly used method to represent vibration signals along a sensing fiber cable.

[0005] The 3D waterfall dataset is extracted by post-processing the multi-point vibration signals with a combination of low and high frequency bands and filtering parameters. A combination filter is applied to filter out the noise information in these frequency bands. The 3D waterfall dataset is processed to obtain trajectory features used for traffic monitoring applications and traffic characteristic calculations.

[0006] US Patent No. 6,269,949 discloses applying spectral filtering to vibration signals, and US Patent No. 6,269,949 discloses identifying bridge sections from waterfall datasets. [Prior art documents] [Patent documents]

[0007] [Patent Document 1] Special Publication No. 2019-529952 [Patent Document 2] Patent Publication No. 2021-121917 Summary of the Invention [Problem to be solved by the invention]

[0008] Trajectory features obtained from the 3D waterfall dataset are filtered using a common set of filtering parameters that are applied to all sensing points corresponding to their locations on the road. However, sensing points vibrate according to dynamic and structural frequency responses that depend on the vibration characteristics of each structure and the excitation. Therefore, noise may be introduced into the waterfall dataset.

[0009] An exemplary object of the present disclosure is to provide a monitoring system, a monitoring method, and a non-transitory computer-readable medium that can reduce noise in a waterfall dataset. [Means for solving the problem]

[0010] According to one aspect of the present disclosure, a signal acquisition means for acquiring vibration data at each of a plurality of sensing points in an optical fiber cable laid along a transportation infrastructure including a plurality of sections having different structures; a raw data set processing means for obtaining time-distance vibration data of the transportation infrastructure based on the vibration data; a passband processing means for acquiring filtering parameters for the time-distance vibration data for each section of the transportation infrastructure based on structural characteristics of the section; a waterfall processing means for removing noise from the time-distance vibration data for each section of the transportation infrastructure using the filtering parameters to obtain waterfall data; a trajectory detection means for identifying a trajectory of a moving object passing through the transportation infrastructure from the waterfall data; A monitoring system is provided comprising:

[0011] According to one aspect of the present disclosure, In an optical fiber cable laid along a transportation infrastructure including a plurality of sections having different structures, vibration data is acquired at each of a plurality of sensing points; acquiring time-distance vibration data of the transportation infrastructure based on the vibration data; obtaining filtering parameters for the time-distance vibration data for each section of the transportation infrastructure based on structural characteristics of the section; using the filtering parameters to remove noise from the time-distance vibration data for each section of the transportation infrastructure to obtain waterfall data; Identifying the trajectory of a moving object passing through the transportation infrastructure from the waterfall data A monitoring method is provided.

[0012] According to one aspect of the present disclosure, In an optical fiber cable laid along a transportation infrastructure including a plurality of sections having different structures, vibration data is acquired at each of a plurality of sensing points; acquiring time-distance vibration data of the transportation infrastructure based on the vibration data; obtaining filtering parameters for the time-distance vibration data for each section of the transportation infrastructure based on structural characteristics of the section; using the filtering parameters to remove noise from the time-distance vibration data for each section of the transportation infrastructure to obtain waterfall data; Identifying the trajectory of a moving object passing through the transportation infrastructure from the waterfall data A non-transitory computer-readable medium is provided that stores a program for causing a computer to execute the method. [Effects of the Invention]

[0013] According to the present disclosure, a monitoring system, a monitoring method, and a non-transitory computer-readable medium can be provided that can reduce noise in a waterfall dataset. [Brief explanation of the drawings]

[0014] [Figure 1] FIG. 1 is a diagram illustrating an example of a 3D waterfall dataset. [Figure 2] FIG. 1 is a diagram for explaining a related art. [Figure 3] FIG. 1 is a diagram for explaining a monitoring system according to a first embodiment of the present disclosure. [Figure 4A] FIG. 10 is a diagram for explaining a second embodiment of the present disclosure. [Figure 4B] FIG. 10 is a diagram for explaining a second embodiment of the present disclosure. [Figure 4C] FIG. 10 is a diagram for explaining a second embodiment of the present disclosure. [Figure 5] FIG. 10 is a block diagram showing a configuration example of a monitoring system according to a second embodiment of the present disclosure. [Figure 6] FIG. 10 is a diagram for explaining a monitoring system according to a second embodiment of the present disclosure. [Figure 7] FIG. 10 is a diagram for explaining a monitoring system according to a second embodiment of the present disclosure. [Figure 8] 10 is a table for explaining filtering parameters according to the second embodiment of the present disclosure. [Figure 9] 10 is a flowchart illustrating the operation of the monitoring system according to the second embodiment of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0015] (Considerations leading to the embodiment) First, we will explain the investigations conducted by the inventors of this application. Representing the vibration signal along a sensing fiber cable as shown in Figure 1 is a commonly used method. The X-axis corresponds to the distance from the sensing device (box) location, the Y-axis corresponds to time, and the Z-axis corresponds to the amplitude of vibration at that distance from the sensing device location. The amplitude is normalized. The waterfall dataset contains vibration data for the bridge section enclosed by the dashed line.

[0016] The trajectory features obtained from the 3D waterfall dataset are filtered in a filtering process using a common set of filtering parameters that are applied to all sensing points corresponding to positions on a road (e.g., a highway), as shown in FIG. 2. Multi-point vibration signals are input, the vibration signals are filtered, and the accumulated amplitude is output. The amplitude corresponds to the signal strength. The set of filtering parameters is applied to four different sections of the transportation infrastructure (e.g., a tunnel, a normal road, and a bridge). However, the sensing positions vibrate according to dynamic and structural frequency responses that depend on each structure and its vibration characteristics when excited. This may introduce noise into the waterfall dataset. The inventors of the present application have conceived the embodiments of the present disclosure based on the above considerations.

[0017] Embodiments according to the present disclosure will be described below with reference to the drawings. Note that in the following description and drawings, appropriate omissions and simplifications are made for clarity. Furthermore, throughout the drawings, the same elements are represented by the same reference numerals (or symbols), and redundant descriptions thereof will be omitted as necessary. Furthermore, in this disclosure, unless otherwise specified, "at least one of A or B (A / B)" may mean either A or B, or both A and B. Similarly, when "at least one" is used in reference to three or more elements, it may mean any one of these elements, or any multiple elements (including all elements). Furthermore, in the description of this disclosure, it should be noted that elements described using singular forms such as "a," "an," "the," and "one" may refer to multiple elements unless otherwise specified.

[0018] (Embodiment 1) First, a monitoring system 10 according to the first embodiment of the present disclosure will be described with reference to FIG.

[0019] Referring to FIG. 3, the monitoring system 10 includes a signal acquisition unit 12, a raw data set processing unit 14, a passband processing unit 16, a waterfall processing unit 18, and a trajectory detection unit 20.

[0020] The signal acquisition unit 12 acquires vibration data at each of a plurality of sensing points on the optical fiber cable. The optical fiber cable is installed along a transportation infrastructure including a plurality of sections with different structures. The transportation infrastructure can be called a road. The plurality of sections can include a tunnel section, a bridge section, and a normal road section. The normal road section is supported by the ground.

[0021] The raw dataset processor 14 acquires the road time-distance vibration data and applies pre-processing steps such as signal downsampling rate and unit conversion. For example, optical phase radian units can be converted to microstrain units. The unit conversion can depend on the input settings of the measurement system.

[0022] The passband processing unit 16 acquires filtering parameters for the time-distance vibration data of each section of the transportation infrastructure based on the structural characteristics of that section. The filtering parameters are also called structural passband parameters.

[0023] The waterfall processing unit 18 uses filtering parameters to remove noise from the time-distance vibration data of each section of the transportation infrastructure, and obtains waterfall data.

[0024] The trajectory detection unit 20 identifies the trajectory of a moving object passing through the transportation infrastructure from the waterfall data. Examples of the moving object include a vehicle, a train, a bicycle, or a pedestrian.

[0025] The monitoring system 10 removes noise from the time-distance vibration data based on structural characteristics, thereby reducing noise in the waterfall data set.

[0026] The monitoring system 10 includes a processor, a memory, and a storage device (not shown). The storage device stores a computer program that implements the processing of the monitoring method according to this embodiment. The processor then loads the computer program from the storage device into the memory and executes the computer program. This allows the processor to implement the functions of a signal acquisition unit 12, a raw data set processing unit 14, a passband processing unit 16, a waterfall processing unit 18, and a trajectory detection unit 20.

[0027] Alternatively, the signal acquisition unit 12, the raw data set processing unit 14, the passband processing unit 16, the waterfall processing unit 18, and the trajectory detection unit 20 may each be realized by dedicated hardware. Furthermore, some or all of the components of each device may be realized by general-purpose or dedicated circuits, processors, etc., or a combination thereof. These may be configured by a single chip, or by multiple chips connected via a bus. Some or all of the components of each device may be realized by a combination of the above-mentioned circuits, etc., and a program. Furthermore, a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), an FPGA (Field-Programmable Gate Array), etc. may be used as the processor.

[0028] When some or all of the components of the monitoring system 10 are realized by multiple information processing devices, circuits, etc., the multiple information processing devices, circuits, etc. may be centrally or decentralized. For example, the information processing devices, circuits, etc. may be realized as a client-server system, a cloud computing system, or the like, in a form in which each device is connected via a communication network. Furthermore, the functions of the monitoring system 10 may be provided in a SaaS (Software as a Service) format.

[0029] (Embodiment 2) Hereinafter, a second embodiment of the present disclosure will be described with reference to the drawings. This second embodiment describes one specific example of the first embodiment, but the specific example of the first embodiment is not limited to this embodiment.

[0030] 4A to 4C are diagrams for explaining an outline of the second embodiment.

[0031] Figure 4A shows an example of a raw vibration signal dataset (multi-point raw dataset) measured at all points in a series of optical fiber cables attached to a road. Three vibration signals are shown as examples.

[0032] Figure 4B shows the vibration signal obtained by applying a structural passband filter to all channels (sensing points) of the fiber optic cable. Three vibration signal channels are shown as an example. The structural passband filter can be derived from a known structural characteristic of a given structure, such as a resonant frequency (natural frequency) or a low frequency corresponding to the surface displacement of the structure. The signal clearly indicates the presence of a vehicle due to a change in amplitude from the baseline amplitude.

[0033] Figure 4C shows the extracted 3D waterfall dataset. The first axis, the distance axis, is the number of channels (sensing points) on the fiber optic cable. The second axis, the time axis, is the measurement time. The third axis is the processed amplitude of the vibration signal after applying a structural passband filter. For illustrative purposes, the number of channels in Figure 4C is greater than three, which are not shown in Figures 4A and 4B. The pixels in the 3D waterfall dataset are converted to white and black to indicate the presence or absence of a vehicle, respectively. The white pixels of an individual vehicle are known as the vehicle's trajectory in the time-distance plane. Compared to Figure 1, the vehicle's trajectory, which is a series of white pixels, is maintained even in the bridge section.

[0034] 5, the monitoring device 100 includes a signal acquisition unit 102, a raw data set processing unit 104, a structural passband processing unit 106, a 3D waterfall processing unit 108, and a trajectory detection unit 110. The monitoring device 100 is a specific example of the monitoring system 10. The monitoring device 100 is also referred to as a distributed acoustic sensing 3D waterfall extraction device.

[0035] The monitoring device 100 is connected to a distributed acoustic sensor (DAS), which detects vibration signals at multiple sensing points on the optical fiber cable and transmits the signals to the monitoring device 100.

[0036] The signal acquiring unit 102 is a specific example of the signal acquiring unit 12. The signal acquiring unit 102 acquires vibration signals (acoustic or vibration data) from a DAS, i.e., an interrogator. The DAS can detect road vibration signals induced by a vehicle when the vehicle is traveling in any lane.

[0037] The raw dataset processing unit 104 is a specific example of the raw dataset processing unit 14. The raw dataset processing unit 104 acquires vibration / acoustic signals for each of the consecutive sensing points that form the time-distance chart (the raw dataset in FIG. 4A). The raw dataset can be used to acquire structural information such as normal road sections, bridge sections, and tunnel sections from pre-specified positions on the fiber cable. A snapshot example of a 3D waterfall dataset generated from the related art is shown in FIG. 1, where the vibration intensity corresponding to the type of vehicle passing through the road is visualized as the vehicle moves away from the sensing device. The dashed box represents a bridge section on the road, and the vibration intensity is obscured due to the high vibration of the bridge section.

[0038] The structural passband processing unit 106 is a specific example of the passband processing unit 16. The structural passband processing unit 106 acquires structural passband parameters (filtering parameters) for each section on the road, as shown in Fig. 6. The raw data set is used as an input for acquiring the structural passband parameters.

[0039] Figure 7 provides background on the selection of structural passband parameters. A vehicle 21 (e.g., a truck) with a speed of V is passing over a bridge section 22 of length L. As the vehicle 21 passes over the bridge section 22, the bridge structure tends to displace from its resting state, known as bridge displacement (deflection). Equation (1) represents the total time taken to traverse the bridge section 22, where T is time (seconds), L is the bridge length (meters), and V is the vehicle speed (meters / second). To obtain bridge displacement information from the measured raw dataset vibration signal, the low-frequency band of the signal contains this information. Equation (2) is the inverse of equation (1) from time T (seconds) to frequency F (Hertz). Frequencies from 0 Hertz to the cutoff frequency F comprise all of the displacement information.

[0040] Figure 8 shows a table of cutoff frequencies (Hz) calculated from equation (2) assuming a possible vehicle speed of V kilometers per hour and a bridge length of L meters.

[0041] Referring to FIG. 5, the 3D waterfall processor 108 is a specific example of the waterfall processor 18. The 3D waterfall processor 108 obtains cutoff frequency parameters and applies a low-pass filter to each channel of the corresponding structural interval. A signal smoothing method may be applied to the time axis or the distance axis to remove signal noise. A signal smoothing method such as a moving average may be used. The smoothed signal is converted to an absolute value and accumulated over the time axis to obtain the waterfall intensity amplitude. The 3D waterfall amplitude may be normalized to emphasize the vibrations in each interval.

[0042] The trajectory detector 110 is a specific example of the trajectory detector 20. The trajectory detector 110 detects higher amplitude trajectories of passing vehicles observed in the 3D waterfall dataset. The trajectory detection method can use an artificial intelligence model (e.g., a deep neural network model) to detect trajectory patterns for each structural section. The estimated trajectories for each section are useful for traffic and structural health monitoring, which is enabled by using structural passband filters (parameters).

[0043] FIG. 9 is a flow chart illustrating an example of the operation of the monitoring device 100 to extract 3D intensity amplitudes from raw vibrations measured from a fiber optic cable.

[0044] The monitoring device 100 receives an oscillation signal (an acoustic signal or a vibration signal from the DAS) from the signal acquisition unit 102.

[0045] The raw data set processing unit 104 performs the X RAW (Raw data set) is processed (S100). RAW may be a time-distance graph.

[0046] The pre-processing step (S101) removes amplitude offsets, which are DC (direct current) components (bias components) that may be due to phase drift of the DAS interrogator, from the measurement signal and normalizes the signal amplitude. The pre-processing step may include IIR (infinite impulse response) filtering.

[0047] The structural passband processing unit 106 acquires the structural passband parameters of each section of the road (S102), as shown in Figure 6. The raw data set is used as input to acquire the structural passband parameters. The cutoff frequencies are acquired from pre-specified structural sections.

[0048] Step 103 applies a signal smoothing method to the time and distance axes, which may use, for example, a moving average method.

[0049] Step 104 accumulates the vibration signal amplitude (intensity) from the absolute value of the smoothed signal over time. The accumulated intensity amplitude may be normalized to emphasize trajectory information. This results in a 3D waterfall data set.

[0050] Step 105 detects the trajectory of each sensing point of the optical fiber cable. The estimated trajectory can indicate the presence of a vehicle on the road. The trajectory is estimated and the presence of a vehicle is output.

[0051] The monitoring device 100 removes noise based on structural characteristics, thereby reducing noise from time-distance vibration data.

[0052] The program includes instructions (or software code) that, when loaded into a computer, cause the computer to perform one or more functions described in the embodiments. The program may be stored in a non-transitory computer-readable medium or a tangible storage medium. By way of example and not limitation, computer-readable media or tangible storage media include random-access memory (RAM), read-only memory (ROM), flash memory, solid-state drive (SSD) or other memory technology, CD-ROM, digital versatile disc (DVD), Blu-ray disc or other optical disk storage, magnetic cassette, magnetic tape, magnetic disk storage or other magnetic storage device. The program may also be transmitted on a transitory computer-readable medium or communication medium. By way of example and not limitation, transitory computer-readable media or communication media include electrical, optical, acoustic, or other forms of propagated signals.

[0053] Within the scope of the claims of the present disclosure, various combinations and selections of various disclosed elements (including each element of each example, each element of each drawing, etc.) are possible. In other words, the present disclosure naturally includes various variations and modifications that can be made by a person skilled in the art according to the overall disclosure including the claims and technical concepts. [Explanation of symbols]

[0054] 10. Surveillance System 12 Signal acquisition unit 14 Raw Dataset Processing Section 16 Passband processing section 18 Waterfall Processing Section 20 Trajectory detection unit 100 Monitoring equipment 102 Signal acquisition unit 104 Raw Dataset Processing Unit 106 Structural passband processing section 108 3D waterfall processing section 110 Trajectory detection unit 21 vehicles 22 Bridge Section

Claims

1. a signal acquisition means for acquiring vibration data at each of a plurality of sensing points in an optical fiber cable laid along a transportation infrastructure including a plurality of sections having different structures; a raw data set processing means for obtaining time-distance vibration data of the transportation infrastructure based on the vibration data; a passband processing means for obtaining filtering parameters for the time-distance vibration data of each section of the transportation infrastructure based on the structural characteristics of the section; a waterfall processing means for removing noise from the time-distance vibration data of each section of the transportation infrastructure using the filtering parameters to obtain waterfall data; a trajectory detection means for identifying a trajectory of a moving object passing through the transportation infrastructure from the waterfall data; A monitoring system comprising:

2. The raw data set processing means removes bias components from the vibration data before acquiring the time-distance vibration data. The monitoring system of claim 1 .

3. The passband processing means obtains a cutoff frequency as the filtering parameter based on the structural characteristics.

3. A monitoring system according to claim 1 or 2.

4. The waterfall processing means smoothes the time-distance vibration data on the time axis and the distance axis. A monitoring system according to any one of claims 1 to 3.

5. The trajectory detection means detects the trajectory using a deep neural network model. A monitoring system according to any one of claims 1 to 4.

6. The structural characteristics include at least one of a natural frequency of the section or a length of the section. A monitoring system according to any one of claims 1 to 5.

7. In an optical fiber cable laid along a transportation infrastructure including a plurality of sections with different structures, vibration data is acquired at each of a plurality of sensing points; acquiring time-distance vibration data of the transportation infrastructure based on the vibration data; a passband processing means for obtaining filtering parameters for the time-distance vibration data of each section of the transportation infrastructure based on the structural characteristics of the section; using the filtering parameters to remove noise from the time-distance vibration data of each section of the transportation infrastructure to obtain waterfall data; Identifying the trajectory of a moving object passing through the transportation infrastructure from the waterfall data Monitoring method.

8. In an optical fiber cable laid along a transportation infrastructure including a plurality of sections with different structures, vibration data is acquired at each of a plurality of sensing points; acquiring time-distance vibration data of the transportation infrastructure based on the vibration data; a passband processing means for obtaining filtering parameters for the time-distance vibration data of each section of the transportation infrastructure based on the structural characteristics of the section; using the filtering parameters to remove noise from the time-distance vibration data of each section of the transportation infrastructure to obtain waterfall data; Identifying the trajectory of a moving object passing through the transportation infrastructure from the waterfall data A non-transitory readable medium that stores a program that causes a computer to execute a program.

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