Cable state monitoring and fault diagnosis method

Abstract

The invention discloses a cable state monitoring and fault diagnosis method, and relates to the technical field of cable fault diagnosis, a data acquisition module is installed on a cable, and the data acquisition module acquires environmental data characteristics, temperature data characteristics and electrical parameter characteristics in the cable operation process at intervals; meanwhile, the collected data is transmitted to a monitoring center by using a data return module; and a data fusion module arranged in the monitoring center can perform data fusion on the acquired various data, process and analyze the fused data, extract fault features, diagnose cable faults according to the fault features, and analyze to obtain specific fault types of the cable. According to the invention, through multi-source data fusion and adaptive signal processing, real-time monitoring, high-precision positioning and service life prediction of the cable state are realized, the limitation of single parameter detection is broken through, and the accuracy of cable state monitoring is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of cable fault diagnosis, and in particular to a cable state monitoring and fault diagnosis method. Background Art

[0002] Wires and cables are wire products used to transmit electrical energy, information, and achieve electromagnetic energy conversion. In a broad sense, wires and cables are also referred to as cables. In a narrower sense, cables refer to insulated cables, which can be defined as a collection of the following components: one or more insulated wire cores, and their respective coatings, protective layers, and outer sheaths. Cables may also have additional uninsulated conductors. During cable use, cable status monitoring is often necessary to ensure safety.

[0003] Traditional cable fault detection relies on a single parameter (such as voltage and current), and mainly focuses on single-type indicators. The few studies that consider multiple types of indicators make it difficult to achieve early warning, so there is room for improvement. Summary of the Invention

[0004] The present invention aims to address the shortcomings of existing technologies by proposing a cable condition monitoring and fault diagnosis method. Its advantages lie in its ability to achieve real-time cable condition monitoring, high-precision positioning, and lifespan prediction through multi-source data fusion and adaptive signal processing, thus overcoming the limitations of single-parameter detection and improving the accuracy of cable condition monitoring.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] A cable status monitoring and fault diagnosis method comprises the following steps:

[0007] Step 1: When using, first install the data acquisition module on the cable. The data acquisition module collects the environmental data characteristics, temperature data characteristics and electrical parameter characteristics of the cable during operation at regular intervals; at the same time, the collected data is transmitted to the monitoring center using the data return module;

[0008] Step 2: The data fusion module set up in the monitoring center can fuse the various types of collected data, process and analyze the data after fusion, extract fault characteristics, diagnose the cable fault based on the fault characteristics, and analyze the specific fault type of the cable;

[0009] Step 3: When a cable fault is detected, the fault alarm module is immediately activated to issue a fault warning. After receiving the fault warning, the maintenance personnel use the fault location module with the help of the GPS time synchronization device to calculate the arrival time difference of the traveling wave and locate the fault point in combination with the cable wave velocity correction formula;

[0010] Step 4: After the maintenance personnel arrive at the designated fault point based on the fault location, they can repair the cable fault. After the fault is eliminated, the fault warning will be automatically lifted and the cable will resume normal operation.

[0011] The present invention is further configured such that the environmental data features include image data features, smoke data features, and humidity data features; and the electrical parameter features include voltage parameter features and current parameter features.

[0012] The present invention is further configured such that the data fusion module extracts the weights corresponding to the environmental data features, temperature data features, and electrical parameter features respectively and fuses them using the features of the previous layer of softmax. After feature fusion, further feature extraction is required. Softmax converts the data input of the fully connected layer into a probability output. The conversion formula is: Where zi represents the i-th vertical input to softmax; T represents the category data of the multi-classification problem.

[0013] The present invention is further configured such that the conversion formula mainly ensures the non-negativity of the probability by adopting an exponential function, and ensures that the sum of the probabilities of each prediction category is 1 through normalization processing, thereby realizing the conversion of the numerical input of the fully connected layer into a probability output, and intuitively presenting the prediction results.

[0014] The present invention is further configured such that the fully connected layer and the softmax constitute a classification network. After the classification network is sorted out, the acquisition of the fusion weight needs to be further elaborated. The loss function is: Where p(i) is the expected output; s(i) is the actual output; with the goal of minimizing the loss function, the weight factor is used as the training parameter, and the weight is calculated and updated according to the size of the loss, which is obtained by the iterative training process.

[0015] The present invention further provides that the data return module uses wireless signal transmission, and the data return module is a LoRa communication antenna. The high gain characteristics of the LoRa communication antenna can effectively enhance signal strength and receiving sensitivity; at the same transmission power, the LoRa communication antenna can receive signals from longer distances and transmit signals to further distances.

[0016] The present invention is further configured such that the fault location module has an environmental compensation mechanism, wherein the environmental compensation mechanism adopts an environmental compensation algorithm, and the environmental compensation algorithm dynamically corrects the traveling wave velocity according to real-time temperature and humidity data, and the calculation formula is: Where: V0 represents the standard wave velocity; T is the ambient temperature; RH is the relative humidity.

[0017] A cable status monitoring and fault diagnosis system, which applies the above-mentioned cable status monitoring and fault diagnosis method. The cable status monitoring and fault diagnosis system includes a data acquisition module, a data return module, a data fusion module, a fault diagnosis module, a fault alarm module and a fault location module. The data acquisition module is communicatively connected to the data return module, the data return module is communicatively connected to the data fusion module, and the data fusion module is communicatively connected to the fault diagnosis module, the fault alarm module and the fault location module respectively.

[0018] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the steps of the above method are implemented when the processor executes the computer program.

[0019] A computer-readable storage medium stores a computer program, which implements the steps of the above method when executed by a processor.

[0020] The beneficial effects of the present invention are as follows: the cable status monitoring and fault diagnosis method collects environmental data characteristics, temperature data characteristics and electrical parameter characteristics during the operation of the cable at regular intervals; the data fusion module can fuse the various types of collected data, process and analyze the data after fusion, extract fault characteristics, diagnose the cable fault according to the fault characteristics, and analyze the specific fault type of the cable. Compared with traditional fault detection based on a single parameter, through multi-source data fusion and adaptive signal processing, real-time monitoring of the cable status, high-precision positioning and life prediction are realized, breaking through the limitations of single parameter detection and improving the accuracy of cable status monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 The present invention provides a schematic diagram of the working process of a cable status monitoring and fault diagnosis method. DETAILED DESCRIPTION

[0022] The technical solution of this patent is further described in detail below in conjunction with specific implementation methods.

[0023] The following describes in detail embodiments of the present invention, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0024] In the description of this patent, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating the orientation or position relationship, are based on the orientation or position relationship shown in the accompanying drawings. They are only for the convenience of describing this patent and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, they should not be understood as limitations on this patent.

[0025] In the description of this patent, it should be noted that, unless otherwise expressly specified or limited, the terms "installed," "connected," "connected," and "set" should be understood in a broad sense. For example, they can refer to fixed connection or set, detachable connection or set, or integral connection or set. Those skilled in the art will understand the specific meanings of the above terms in this patent based on the specific circumstances.

[0026] Reference Figure 1 , a cable status monitoring and fault diagnosis method, comprising the following steps:

[0027] Step 1: When using, first install the data acquisition module on the cable. The data acquisition module collects the environmental data characteristics, temperature data characteristics and electrical parameter characteristics of the cable during operation at regular intervals; at the same time, the collected data is transmitted to the monitoring center using the data return module;

[0028] Step 2: The data fusion module set up in the monitoring center can fuse the various types of collected data, process and analyze the data after fusion, extract fault characteristics, diagnose the cable fault based on the fault characteristics, and analyze the specific fault type of the cable;

[0029] Step 3: When a cable fault is detected, the fault alarm module is immediately activated to issue a fault warning. After receiving the fault warning, the maintenance personnel use the fault location module with the help of the GPS time synchronization device to calculate the arrival time difference of the traveling wave and locate the fault point in combination with the cable wave velocity correction formula;

[0030] Step 4: After the maintenance personnel arrive at the designated fault point based on the fault location, they can repair the cable fault. After the fault is eliminated, the fault warning will be automatically lifted and the cable will resume normal operation.

[0031] In this embodiment, the environmental data features include image data features, smoke data features, and humidity data features; and the electrical parameter features include voltage parameter features and current parameter features.

[0032] Furthermore, the data fusion module extracts the weights corresponding to the environmental data features, temperature data features, and electrical parameter features respectively, and fuses them with the features of the previous layer using softmax. After feature fusion, further feature extraction is required. Softmax converts the data input of the fully connected layer into a probability output. The conversion formula is: In the formula, zi represents the i-th vertical of the softmax input; T represents the category data of the multi-classification problem; the conversion formula mainly ensures the non-negativity of the probability by adopting the exponential function, and ensures that the sum of the probabilities of each predicted category is 1 through normalization processing, realizing the conversion of the numerical input of the fully connected layer into a probabilistic output, and intuitively showing the prediction results; the fully connected layer and softmax constitute the classification network. After the classification network is sorted out, it is necessary to further elaborate on the acquisition of the fusion weight. The loss function is: Where p(i) is the expected output; s(i) is the actual output; with the goal of minimizing the loss function, the weight factor is used as the training parameter, and the weight is calculated and updated according to the size of the loss, which is obtained by the iterative training process.

[0033] The data backhaul module uses wireless signal transmission, and the data backhaul module is a Lora communication antenna. The high gain characteristics of the Lora communication antenna can effectively enhance the signal strength and receiving sensitivity. Under the same transmission power, the Lora communication antenna can receive signals from longer distances and transmit the sent signals to further places.

[0034] It is worth mentioning that the fault location module has an environmental compensation mechanism. The environmental compensation mechanism uses an environmental compensation algorithm. The environmental compensation algorithm dynamically corrects the traveling wave velocity based on real-time temperature and humidity data. The calculation formula is: Where: V0 represents the standard wave velocity; T is the ambient temperature; RH is the relative humidity.

[0035] A cable status monitoring and fault diagnosis system, which applies the above-mentioned cable status monitoring and fault diagnosis method, includes a data acquisition module, a data return module, a data fusion module, a fault diagnosis module, a fault alarm module and a fault location module. The data acquisition module is communicatively connected to the data return module, the data return module is communicatively connected to the data fusion module, and the data fusion module is communicatively connected to the fault diagnosis module, the fault alarm module and the fault location module respectively.

[0036] In order to verify the advantages of the cable status monitoring and fault diagnosis system over the existing technology, the cable fault types are divided into four categories: normal, caution, abnormal and severe. The method disclosed in the present invention is used to conduct comparative simulation analysis with the existing technology. The recorded data are shown in the following table:

[0037] Monitoring Type normal Notice abnormal serious Single parameter accuracy 83.9 78.9 83.6 82.3 Accuracy of fused data 92.3 91.8 93.6 92.8

[0038] It is not difficult to see from the above table that the cable monitoring method using the fused data of the present invention has a monitoring accuracy significantly higher than the accuracy of a single parameter in the prior art, regardless of the four fault types: normal, caution, abnormal and severe.

[0039] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above method are implemented.

[0040] A computer-readable storage medium stores a computer program, which implements the steps of the above method when executed by a processor.

[0041] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A cable status monitoring and fault diagnosis method, characterized in that: The following steps are involved: Step 1: When using, first install the data acquisition module on the cable. The data acquisition module collects the environmental data characteristics, temperature data characteristics and electrical parameter characteristics of the cable during operation at regular intervals; at the same time, the collected data is transmitted to the monitoring center using the data return module; Step 2: The data fusion module set up in the monitoring center can fuse the various types of collected data, process and analyze the data after fusion, extract fault characteristics, diagnose the cable fault based on the fault characteristics, and analyze the specific fault type of the cable; Step 3: When a cable fault is detected, the fault alarm module is immediately activated to issue a fault warning. After receiving the fault warning, the maintenance personnel use the fault location module with the help of the GPS time synchronization device to calculate the arrival time difference of the traveling wave and locate the fault point in combination with the cable wave velocity correction formula; Step 4: After the maintenance personnel arrive at the designated fault point based on the fault location, they can repair the cable fault. After the fault is eliminated, the fault warning will be automatically lifted and the cable will resume normal operation.

2. A cable status monitoring and fault diagnosis method according to claim 1, characterized in that: The environmental data features include image data features, smoke data features, and humidity data features; the electrical parameter features include voltage parameter features and current parameter features.

3. A cable status monitoring and fault diagnosis method according to claim 1, characterized in that: The data fusion module extracts the weights corresponding to the environmental data features, temperature data features, and electrical parameter features respectively and fuses them with the features of the previous layer using softmax. After feature fusion, further feature extraction is required. Softmax converts the data input of the fully connected layer into a probability output. The conversion formula is: Where zi represents the i-th vertical input softmax; T represents the category data for multi-classification problems.

4. A cable status monitoring and fault diagnosis method according to claim 3, characterized in that: The conversion formula mainly ensures the non-negativity of probability by adopting an exponential function, and ensures that the sum of the probabilities of each prediction category is 1 through normalization processing, thereby converting the numerical input of the fully connected layer into a probabilistic output and intuitively presenting the prediction results.

5. A cable status monitoring and fault diagnosis method according to claim 4, characterized in that: The fully connected layer and softmax form a classification network. After the classification network is sorted out, it is necessary to further elaborate on the acquisition of fusion weights. The loss function is: Where p(i) is the expected output; s(i) is the actual output; with the goal of minimizing the loss function, the weight factor is used as the training parameter, and the weight is calculated and updated according to the size of the loss, which is obtained by the iterative training process.

6. A cable status monitoring and fault diagnosis method according to claim 1, characterized in that: The data return module adopts a wireless signal transmission method, and the data return module is a Lora communication antenna.

7. A cable status monitoring and fault diagnosis method according to claim 1, characterized in that: The fault location module has an environmental compensation mechanism, which uses an environmental compensation algorithm. The environmental compensation algorithm dynamically corrects the traveling wave velocity according to real-time temperature and humidity data. The calculation formula is: Where: V0 represents the standard wave velocity; T is the ambient temperature; RH is the relative humidity.

8. A cable status monitoring and fault diagnosis system, characterized in that: The cable status monitoring and fault diagnosis system applies the cable status monitoring and fault diagnosis method described in any one of claims 1 to 7. The cable status monitoring and fault diagnosis system includes a data acquisition module, a data return module, a data fusion module, a fault diagnosis module, a fault alarm module and a fault location module. The data acquisition module is communicatively connected to the data return module, the data return module is communicatively connected to the data fusion module, and the data fusion module is communicatively connected to the fault diagnosis module, the fault alarm module and the fault location module respectively.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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