JP7793961B2 - Computer program, deterioration level determination device, and deterioration level determination method - Google Patents

Description

The present invention relates to a computer program, a degradation level determination device, and a degradation level determination method.

When wind blows against overhead electric wires or overhead ground wires, Karman vortices, which are alternating vortex currents, are generated on the downwind side of the wires or ground wires, causing vibrations with amplitudes of one to several tens of millimeters. These vibrations repeatedly stress the wires and ground wires at vibration reflection points such as suspension support points and tension clamps, which serve as fixed points for the overhead electric wires and overhead ground wires, causing metal fatigue degradation and breakage of the wires and overhead ground wires over time. To prevent this, auxiliary devices such as double torsional dampers are installed near the support points of the overhead electric wires and overhead ground wires.

Patent Document 1 discloses a double torsional damper consisting of a restraining wire, a weight secured to both ends of the restraining wire, and a wire-holding clamp secured to the center of the restraining wire, with the restraining wire having a mechanism to prevent the weight from falling. It also devise a method of acquiring images of the double torsional damper to prevent the weight from falling, inputting the acquired images into a learning model generated by machine learning, and determining the deterioration level of the double torsional damper.

JP 2012-5203 A

Such a learning model uses a function (e.g., a softmax function) that converts the output values of each deterioration level so that the sum is 1.0 (100%). When the final deterioration level is determined, the deterioration level belonging to the class with the largest function output value is diagnosed as the deterioration level of the double torsional damper. For this reason, even if the output value of a deterioration level diagnosed as abnormal is not negligible, if the output value of a deterioration level diagnosed as normal is larger, it may ultimately be diagnosed as normal, and something that is actually abnormal may be diagnosed as normal.

The present invention was made in consideration of these circumstances, and aims to provide a computer program, a deterioration level determination device, and a deterioration level determination method that can improve the accuracy of deterioration diagnosis of ground wires or electrical wire accessory equipment.

The present application includes multiple means for solving the above-mentioned problems. As an example, a computer program causes a computer to execute processing to acquire an image of an accessory device attached to a ground wire or electric cable, input the acquired image into a learning model that outputs the accuracy of the deterioration level of the accessory device when the image of the accessory device attached to the ground wire or electric cable is input, acquire the accuracy of the deterioration level of the accessory device captured in the image, and determine whether the deterioration level of the accessory device is a specific deterioration level based on the acquired accuracy and a predetermined threshold value.

This invention can improve the accuracy of diagnosing deterioration of ground wires or electrical wire accessories.

FIG. 1 illustrates an example of the configuration of a deterioration level determination system. FIG. 10 is a diagram illustrating an example of an image of a damper. FIG. 10 is a diagram showing an example of a deterioration level and a determination criterion of a damper. FIG. 10 is a diagram illustrating an example of deterioration determination using a learning model. FIG. 10 is a diagram illustrating an example of a deterioration diagnosis of a damper in a comparative example. 10A and 10B are diagrams illustrating an example of deterioration determination by a deterioration level determining unit. FIG. 10 is a diagram illustrating a first example of deterioration determination by the deterioration level determination device. FIG. 10 is a diagram illustrating a second example of deterioration determination by the deterioration level determination device. FIG. 10 is a diagram illustrating a third example of deterioration determination by the deterioration level determination device. FIG. 10 is a diagram showing a first example of an evaluation result of deterioration determination by the deterioration level determination device. FIG. 10 is a diagram showing a second example of the evaluation result of the deterioration determination by the deterioration level determination device. FIG. 10 is a diagram showing an example of a diagnosis result screen. FIG. 10 is a diagram illustrating an example of a processing procedure of a deterioration level determination device.

Embodiments of the present invention will now be described with reference to the drawings. Figure 1 shows an example of the configuration of a deterioration level determination system. The deterioration level determination system includes a deterioration level determination device 50. A data server 10 and a terminal device 20 are connected to the deterioration level determination device 50 via a communication network 1. The data server 10 collects and records images (image data) of attached equipment of ground wires or electric wires. The attached equipment is, for example, a damper (torsion damper, double torsion damper). Hereinafter, the attached equipment will also be referred to as a damper. Images of the damper are captured, for example, during inspection, and inspection and image capture are performed using a drone, human hands, a self-propelled robot, or a helicopter.

Figure 2 shows an example of an image of a damper. Figure 2 is a schematic diagram of a captured image. The damper has a restraining wire 101, a plumb bob 102, and a wire-holding clamp 103. The plumb bob 102 is fixed to both ends of the restraining wire 101. The wire-holding clamp 103 holds an overhead electric wire (overhead ground wire) 104. If the restraining wire 101 deteriorates due to rust or other reasons, there is a risk that the restraining wire 101 will break and the plumb bob 102 will fall. Therefore, when diagnosing damper deterioration, it is particularly important to determine the deterioration level of the restraining wire 101. The captured image used by the deterioration level determination device 50 to determine the deterioration level may be an image of a predetermined size that shows the entire damper from an image of the entire damper, or an image that shows the entire restraining wire may be cut out from an image of the entire damper.

The data server 10 records captured images such as those shown in Figure 2, in association with information such as the damper model, manufacturing date, installation location, and photo date. Furthermore, for dampers that have undergone a deterioration diagnosis, the data server 10 also records the diagnosis date and the assessment results in association with the images.

The terminal device 20 can be configured, for example, as a personal computer, tablet device, etc., and is used by the person performing the deterioration diagnosis of the damper. The person in charge can use the terminal device 20 to import captured images of the object to be diagnosed with deterioration from the data server 10 and upload the imported captured images to the deterioration level determination device 50, thereby obtaining and displaying the diagnosis results from the deterioration level determination device 50. Note that the captured images may also be sent directly from the data server 10 to the deterioration level determination device 50 by running a predetermined program, etc., without going through the terminal device 20.

The deterioration level determination device 50 includes a control unit 51 that controls the entire device, a communication unit 52, a memory 53, a deterioration level determination unit 54, an output unit 55, and a storage unit 56. The storage unit 56 stores a computer program 57 and a learning model 58.

The control unit 51 can be configured with a CPU (Central Processing Unit), MPU (Micro-Processing Unit), GPU (Graphics Processing Unit), etc. The control unit 51 can execute processing defined by the computer program 57. In other words, the processing by the control unit 51 is also processing by the computer program 57.

The communication unit 52, for example, includes a communication module and has the ability to communicate with the data server 10 and the terminal device 20 via the communication network 1. The communication unit 52 can acquire (receive) images (captured images) of the degradation diagnosis target from the terminal device 20 or the data server 10.

Memory 53 can be composed of semiconductor memory such as SRAM (Static Random Access Memory), DRAM (Dynamic Random Access Memory), or flash memory. A computer program 57 can be loaded into memory 53, and the control unit 51 can execute the computer program 57.

The memory unit 56 may be configured, for example, as a hard disk or semiconductor memory, and may store necessary information in addition to the computer program 57 and learning model 58.

The output unit 55 outputs the results of the damper deterioration diagnosis performed by the deterioration level determination device 50 to the terminal device 20.

The learning model 58 can be configured, for example, as a convolutional neural network (CNN), with a softmax function in the final layer, and is generated so that when an image of a damper is input, it outputs the probability (accuracy) of the damper's deterioration level. The learning model 58 may also be AlexNet, ZFNet, GoogLeNet, VGGNet, ResNet, SENet, etc.

Figure 3 shows an example of damper deterioration levels and assessment criteria. As shown in Figure 3, damper deterioration levels can be classified into five levels: Rank I, Rank II, Rank III, Rank IV, and Rank V, with the higher the rank, the greater the degree of deterioration. Ranks IV and V indicate that the damper has been diagnosed as abnormal, while Ranks I to III indicate that the damper has been diagnosed as normal (minor). Note that the classification of ranks is not limited to the example shown in Figure 3.

For example, Rank I indicates that there is no red rust on the deterrent wire, and the color characteristic of zinc plating remains. Rank II indicates that there is no red rust on the deterrent wire, but the surface is discolored to a brown or black color. Rank III indicates that there is a small amount of red rust on the deterrent wire, or that red rust has occurred on only part of the deterrent wire. Rank IV indicates that red rust has occurred on almost the entire front surface of the deterrent wire, or that red rust has occurred on the entire front surface of the deterrent wire and has not yet progressed to severe pitting. Rank V indicates that there is severe red rust on the deterrent wire accompanied by pitting corrosion.

Figure 4 shows an example of deterioration determination using the learning model 58. The control unit 51 functions as an image acquisition unit and a probability acquisition unit, and when it acquires an image (captured image) of the damper, it inputs the acquired image into the learning model 58 and acquires output data from the learning model 58. The output data is the probability of each deterioration level (ranks I to V). For convenience, in the example of Figure 4, the probabilities of ranks I to V are represented by P1, P2, P3, P4, and P5, respectively.

Here, we will explain as a comparative example a case in which damper deterioration diagnosis is performed solely using deterioration determination based on learning model 58.

Figure 5 shows an example of a damper deterioration diagnosis for a comparative example. As shown in Figure 5, the output of the learning model 58 is, for example, 0% (%), 5% (%), 50% (%), 40% (%), and 5% (%) for the probabilities of ranks I to V, respectively. In this case, the maximum probability is 50% for rank III, so the damper deterioration diagnosis results in a deterioration level III. However, although the probability of rank IV is 40%, this is not the maximum probability, and therefore is not taken into consideration at all in the deterioration diagnosis. Considering that the probability of rank IV is 40%, it is considered risky to diagnose the damper as having a deterioration level III. Therefore, in this embodiment, damper deterioration diagnosis is performed not only using the learning model 58 to determine deterioration, but also using the deterioration level determination unit 54. Deterioration determination by the deterioration level determination unit 54 is described below.

The deterioration level determination unit 54 functions as a determination unit and determines whether the damper deterioration level is a specific deterioration level based on the probability (accuracy) of the deterioration level output by the learning model 58 and a predetermined threshold. A specific deterioration level is a deterioration level at which the damper is diagnosed as abnormal, and in this embodiment, these are rank IV and rank V. Specifically, the deterioration level determination unit 54 performs a deterioration diagnosis of the damper in the following order of priority:

Figure 6 shows an example of deterioration determination by the deterioration level determination unit 54. As priority 1, the deterioration level determination unit 54 determines whether the probability of rank V is equal to or greater than a first threshold, and if the probability of rank V is equal to or greater than the first threshold, determines the deterioration level of the damper to be rank V. The first threshold can be set as appropriate, for example, to 10%, 5%, etc. If the probability of rank V is not equal to or greater than the first threshold, the deterioration level determination unit 54 moves on to determining priority 2.

As priority 2, the deterioration level determination unit 54 determines whether the probability of rank IV is equal to or greater than a second threshold, and if the probability of rank IV is equal to or greater than the second threshold, determines the deterioration level of the damper to be rank IV. The second threshold can be set as appropriate, for example, to 15%, 10%, etc. The second threshold can be set to a value greater than the first threshold, but is not limited to this. If the probability of rank IV is not equal to or greater than the second threshold, the deterioration level determination unit 54 moves on to determining priority 3. In this way, for ranks V and IV, where the deterioration diagnosis is determined to be abnormal, the deterioration level of the damper is determined using the probability of the deterioration level output by the learning model 58 and a predetermined threshold.

As described above, the predetermined threshold value can be set according to a specific deterioration level (Rank V, Rank IV). The deterioration level determination unit 54 determines the deterioration level of the damper according to the priority of the specific deterioration level. The priority corresponds to the degree of abnormality of the specific deterioration level (Rank V is Priority 1, and Rank IV is Priority 2).

As priority 3, the deterioration level determination unit 54 determines which rank among ranks I to III has the highest probability, and determines the rank with the highest probability as the deterioration level of the damper. In other words, if the deterioration level of the damper is not a specific deterioration level, the deterioration level determination unit 54 determines the deterioration level of the damper based on the probability output by the learning model 58. A specific example is described below.

Figure 7 shows a first example of deterioration determination by the deterioration level determination device 50. As shown in Figure 7, the output of the learning model 58 is, for example, 15% (%), 50% (%), 10% (%), 10% (%), and 15% (%) for ranks I to V, respectively. In this case, the highest probability is 50% for rank II. First, a deterioration determination is made for rank V. Since the probability of rank V is 15%, which is greater than or equal to the first threshold value (e.g., 10%), the deterioration level of the damper is determined to be rank V.

Figure 8 shows a second example of deterioration determination by the deterioration level determination device 50. As shown in Figure 8, the output of the learning model 58 is, for example, 5% (%), 20% (%), 40% (%), 30% (%), and 5% (%) for ranks I to V. In this case, the highest probability is 40% for rank III. Deterioration determination is first performed for rank V. Since the probability of rank V is 5% and is not greater than or equal to the first threshold (e.g., 10%), deterioration determination is performed for rank IV in accordance with priority. Since the probability of rank IV is 30% and is greater than or equal to the second threshold (e.g., 15%), the deterioration level of the damper is determined to be rank IV.

Figure 9 shows a third example of deterioration determination by the deterioration level determination device 50. As shown in Figure 9, the output of the learning model 58 indicates, for example, the probabilities of ranks I to V, respectively, as 5% (%), 50% (%), 30% (%), 10% (%), and 5% (%). In this case, the maximum probability is rank II, which is 50%. Deterioration determination is first performed for rank V. Since the probability of rank V is 5%, which is not greater than or equal to the first threshold (e.g., 10%), deterioration determination is performed for rank IV in order of priority. Since the probability of rank IV is 10%, which is not greater than or equal to the second threshold (e.g., 15%), deterioration determination is performed for ranks I to III in order of priority. Since the maximum probability among ranks I to III is rank II, which is 50%, the deterioration level of the damper is determined to be rank II.

Next, we will explain the evaluation results of the deterioration level determination device 50.

Figure 10 shows a first example of the evaluation results of deterioration determination by the deterioration level determination device 50. Deterioration determination is evaluated using the indices "failure rate" and "false alarm rate." The failure rate is the rate at which abnormal dampers were not diagnosed as abnormal, and includes, for example, the rate at which rank V was not diagnosed as rank V and the rate at which rank IV was not diagnosed as rank IV. Diagnosing an actually abnormal damper as normal should be minimized in deterioration diagnosis, and the rate at which this oversight is referred to as the failure rate. Systems that perform deterioration diagnosis in actual operation are required to have a low failure rate. The false alarm rate is the rate at which dampers that are not abnormal are diagnosed as abnormal, and includes, for example, the rate at which ranks I to III are diagnosed as rank IV and the rate at which ranks I to III are diagnosed as rank V.

For dampers that have undergone deterioration diagnosis within a specified period, such as one year, six months, or three months, the target is a failure rate of 5% or less and a false alarm rate of 20% or less for both ranks IV and V. According to the deterioration level determination device 50 of this embodiment, for rank IV, the failure rate was 5% and the false alarm rate was 4%, both of which achieved the target. For rank V, the failure rate was 3% and the false alarm rate was 8%, both of which also achieved the target. Furthermore, although not shown, the false alarm rate for ranks I to III was 10% or less.

Figure 11 shows a second example of the evaluation results of deterioration determination by the deterioration level determination device 50. Figure 11 illustrates the "accuracy" and "detection rate" for ranks IV and V. As with the comparative example in Figure 5, the accuracy is the accuracy when a damper deterioration diagnosis is performed using only deterioration determination by the learning model 58. The accuracy is the percentage of those determined to be abnormal that were actually abnormal. The accuracy for rank IV was approximately 75%, and the accuracy for rank V was approximately 85%.

The detection rate is the detection rate when the deterioration level determination unit 54 is also used to perform a deterioration diagnosis of the damper. The detection rate is the percentage of abnormal dampers that are determined to be abnormal, and is calculated as {100 (%) - detection rate (%)} = false alarm rate (%). As shown in Figure 11, by also using the deterioration level determination unit 54, the detection rate for rank IV was 95%, and the detection rate for rank V was 97%. In other words, the false alarm rate for rank IV was 5%, and the false alarm rate for rank V was 3%.

Figure 12 shows an example of a diagnosis result screen. The diagnosis result screen is displayed, for example, on the terminal device 20. The diagnosis result screen displays the device ID of the damper being diagnosed for deterioration, a deterioration level column, a maximum probability column, and the probability for each rank. The deterioration level column displays the results of the damper's deterioration diagnosis. The deterioration level column indicates the final deterioration diagnosis of the damper using the deterioration level determination unit 54 as well (Rank IV in the example shown). The maximum probability indicates the rank with the highest probability among the probabilities for each deterioration level output by the learning model 58 (Rank III in the example shown). While the probability for each rank is illustrated as being Ranks I to V, it is also possible to display a comparison between the probability of Rank IV, which is a specific deterioration level, and the probability of Rank III, which is the maximum probability.

As described above, when the deterioration level of the damper is a specific deterioration level (rank IV in the example shown), the control unit 51 may display a comparison between the maximum probability (maximum value of probability) of the deterioration level output by the learning model 58 and the probability of the specific deterioration level output by the learning model 58.

Figure 13 shows an example of the processing procedure of the deterioration level determination device 50. For convenience, the following description will be given with the control unit 51 as the main processor. The control unit 51 acquires an image of the accessory device (damper) (S11), inputs the acquired image into the learning model 58, and acquires the probability of the damper's deterioration level (S12). The control unit 51 determines whether the probability of rank V is equal to or greater than the first threshold (S13).

If the probability of rank V is greater than or equal to the first threshold (YES in S13), the control unit 51 sets the damper's deterioration level to rank V (S14) and terminates processing. If the probability of rank V is not greater than or equal to the first threshold (NO in S13), the control unit 51 determines whether the probability of rank IV is greater than or equal to the second threshold (S15).

If the probability of rank IV is greater than or equal to the second threshold (YES in S15), the control unit 51 sets the damper deterioration level to rank IV (S16) and terminates processing. If the probability of rank IV is not greater than or equal to the second threshold (NO in S15), the control unit 51 sets the damper deterioration level to the rank with the highest probability among the probabilities of ranks I to III (S17) and terminates processing.

As described above, according to this embodiment, the false alarm rate for Rank IV is 5%, the false alarm rate for Rank V is 3%, and the false alarm rate for Ranks I to III is 10% or less, achieving results that are suitable for practical use.

The computer program of this embodiment causes a computer to acquire an image of an accessory device attached to a ground wire or electric cable, input the acquired image into a learning model that outputs the accuracy of the deterioration level of the accessory device when an image of the accessory device attached to a ground wire or electric cable is input, acquire the accuracy of the deterioration level of the accessory device captured in the image, and determine whether the deterioration level of the accessory device is a specific deterioration level based on the acquired accuracy and a predetermined threshold.

In the computer program of this embodiment, the predetermined threshold can be set according to the specific degradation level.

The computer program of this embodiment causes the computer to execute a process to determine the deterioration level of the accessory device according to the priority of the specific deterioration level.

In the computer program of this embodiment, the priority corresponds to the degree of abnormality of the specific degradation level.

The computer program of this embodiment causes the computer to determine the deterioration level of the accessory device based on the accuracy output by the learning model if the deterioration level of the accessory device is not the specific deterioration level.

The computer program of this embodiment causes the computer to execute a process that, when the deterioration level of the accessory equipment is a specific deterioration level, displays a comparison between the maximum value of the accuracy of the deterioration level output by the learning model and the accuracy of the specific deterioration level output by the learning model.

The deterioration level determination device of this embodiment includes an image acquisition unit that acquires an image of an accessory device attached to a ground wire or electric cable; an accuracy acquisition unit that inputs the acquired image into a learning model that outputs the accuracy of the deterioration level of the accessory device when an image of an accessory device attached to a ground wire or electric cable is input, and acquires the accuracy of the deterioration level of the accessory device captured in the image; and a determination unit that determines whether the deterioration level of the accessory device is a specific deterioration level based on the acquired accuracy and a predetermined threshold.

The degradation level determination method of this embodiment acquires an image of an accessory device attached to a ground wire or electric cable, inputs the acquired image into a learning model that outputs the accuracy of the degradation level of the accessory device when an image of the accessory device attached to the ground wire or electric cable is input, obtains the accuracy of the degradation level of the accessory device captured in the image, and determines whether the degradation level of the accessory device is a specific degradation level based on the acquired accuracy and a predetermined threshold.

REFERENCE SIGNS LIST 1 Communication network 10 Data server 20 Terminal device 50 Degradation level determination device 51 Control unit 52 Communication unit 53 Memory 54 Degradation level determination unit 55 Output unit 56 Storage unit 57 Computer program 58 Learning model

Claims (7)

On the computer,
Acquire an image of the ground wire or the attached equipment of the electric wire;
inputting the acquired image into a learning model that outputs the accuracy of the deterioration level of the accessory when an image of an accessory of a ground wire or an electric wire is input, and acquiring the accuracy of the deterioration level of the accessory captured in the image;
determining whether the deterioration level of the accessory device is a specific deterioration level based on the acquired accuracy and thresholds associated with each of a plurality of deterioration levels of the accessory device, in accordance with a priority order set based on the plurality of deterioration levels;
A computer program that executes a process. The threshold value can be set according to the particular level of degradation.
2. The computer program of claim 1. The priority corresponds to the degree of abnormality of the particular deterioration level.
3. A computer program according to claim 1 or claim 2. On the computer,
If the deterioration level of the accessory device is not the specific deterioration level, the deterioration level of the accessory device is determined based on the probability output by the learning model.
The computer program according to any one of claims 1 to 3, which causes a process to be executed. On the computer,
When the deterioration level of the accessory device is a specific deterioration level, a maximum value of the accuracy of the deterioration level output by the learning model is displayed in comparison with the accuracy of the specific deterioration level output by the learning model.
5. A computer program product according to claim 1, which causes a process to be executed. an image acquisition unit that acquires an image of the ground wire or an attachment device of the electric wire;
an accuracy acquisition unit that inputs an acquired image into a learning model that outputs an accuracy of a deterioration level of an accessory device when an image of an accessory device of a ground wire or an electric wire is input, and acquires an accuracy of a deterioration level of the accessory device captured in the image;
a determination unit that determines whether the deterioration level of the accessory device is a specific deterioration level in accordance with a priority order set based on the plurality of deterioration levels, based on the acquired accuracy and thresholds associated with each of the plurality of deterioration levels of the accessory device.
Deterioration level determination device. Acquire an image of the ground wire or the attached equipment of the electric wire;
inputting the acquired image into a learning model that outputs the accuracy of the deterioration level of the accessory when an image of an accessory of a ground wire or an electric wire is input, and acquiring the accuracy of the deterioration level of the accessory captured in the image;
determining whether the deterioration level of the accessory device is a specific deterioration level based on the acquired accuracy and thresholds associated with each of a plurality of deterioration levels of the accessory device, in accordance with a priority order set based on the plurality of deterioration levels;
Deterioration level determination method.