Since modern systems do not locate exact location of the fault in cable which results in economical and infrastructural loses. For overcoming these shortcomings we will design cheap system for finding exact location of the fault. The project is combination of hardware and softwar
Underground Cable Fault Location Detection using Machine Learning
Since modern systems do not locate exact location of the fault in cable which results in economical and infrastructural loses.
For overcoming these shortcomings we will design cheap system for finding exact location of the fault.
The project is combination of hardware and software.
Since modern systems do not locate exact location of the fault in cable which results in economical and infrastructural loses. The proposed project will find the location of the fault with the help of Machine Learning techniques.
For overcoming these shortcomings we will design cheap system for finding exact location of the fault.
The project is combination of hardware and software.
The faults created are manually by using switches.
Cable has many types. Every cables has different resistance which mainly depends upon the material.
The value of the resistance is depends upon the length of the cable wire.
If any changes occur in the resistance, the value of the voltage will be changed that particular point is called Fault.
Faults has many types & are given below.
Current sensing part of cable represented as set of resistors & switches are used as fault creators to indicate the fault at each location.
This part senses the change in current by sensing the voltage drop due change in length of the resistor.
Next is controlling part which consists of internal ADC in ARDUINO which receives input from the current sensing circuit as AC input, converts this voltage into digital signal and then it feeds the microcontroller with that signal.
The microcontroller also a part of the controlling unit and makes necessary calculations regarding the distance of the fault according to our program.
Value of distance calculated by ARDUINO program is displayed on LCD and send to the server through ESP8266 module.
In server side the value of distance is fed to ML model which generates GPS coordinates.
Then these coordinates are shown in the mobile app on googlemape.
ML model is trained using the data which collected during laying down the cable.
This data set consists of the following features.
1.DISTANCE FROM BASE STATION
2.GPS COORDINATES ASSOCIATED WITH EACH POINT ON THE CABLE.
ML model is trained using linear regression

SOFTWARES USED.
1. Embedded C is used for programing ARDUINO using arduino IDE.
2. Python and it libraries for machine learning like
Scikit-learn
Pandas
Matplotlib
Seaborn
3. JAVA for creating android app using android studio.
4 XML for designing mobile layout.
Followimg are the advantages of underground cables
But one of the major challenges in underground cables is to detect the location of the fault.
The major benifit of the project is to locate the fault and avoid the digging and manually finding the fault.
Following is the list of equipments used in the project
The circuit diagram is as follow
SOFTWARES USED.
1. Embedded C is used for programing ARDUINO using arduino IDE.
2. Python and it libraries for machine learning like
Scikit-learn
Pandas
Matplotlib
Seaborn
3. JAVA for creating android app using android studio.
4 XML for designing mobile layout.
| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| GSM Module SIM900a | Equipment | 8 | 2500 | 20000 |
| Arduino Mega | Equipment | 8 | 1500 | 12000 |
| 4 Channel Relay Module | Equipment | 16 | 400 | 6400 |
| Bread Board | Equipment | 20 | 250 | 5000 |
| TFT LCD | Equipment | 7 | 1000 | 7000 |
| Power Supply unit for circuit operation | Equipment | 4 | 500 | 2000 |
| transformer | Equipment | 5 | 900 | 4500 |
| Regulator | Equipment | 4 | 200 | 800 |
| Total in (Rs) | 57700 |
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