AI-Based Railway Track Fault Prediction and Autonomous Alert System Using Raspberry Pi Pico, GPS and Computer Vision

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AI-Based Railway Track Fault Prediction and Autonomous Alert System Using Raspberry Pi Pico + ESP32 + GPS + Computer Vision + Agentic AI + n8n Automation + Telegram Voice Alerts + Google Sheets + ThingSpeak Cloud
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AI-Based Railway Track Fault Prediction and Autonomous Alert System

Using Raspberry Pi Pico, ESP32, GPS, Computer Vision, AI Agent, n8n, Telegram Voice Alerts, Google Sheets & ThingSpeak

1. Project Overview

This project provides an intelligent railway monitoring system capable of detecting track cracks, obstacles, abnormal vibrations, and rail misalignment using sensors, computer vision, GPS tracking, cloud computing, and artificial intelligence.

The collected data is transmitted through ESP32 to cloud platforms where AI models predict risk levels and automatically generate alerts through Telegram voice notifications.

2. Objectives

  • Detect railway track cracks automatically.
  • Monitor vibration and temperature continuously.
  • Track exact GPS location of faults.
  • Predict maintenance requirements using AI.
  • Send automatic Telegram alerts.
  • Store data in Google Sheets and ThingSpeak.
  • Provide real-time cloud dashboard monitoring.

3. Components Required

Component Quantity Purpose
Raspberry Pi Pico W 1 Data Processing
ESP32 1 WiFi Communication
NEO-6M GPS 1 Location Tracking
MPU6050 1 Vibration Detection
DHT22 1 Temperature Monitoring
HC-SR04 1 Obstacle Detection
ESP32-CAM 1 Computer Vision
18650 Battery 1 Power Supply

4. System Architecture

Railway Track
      |
      V
Sensors + Camera
      |
      V
Raspberry Pi Pico
      |
      V
ESP32 Gateway
      |
      V
ThingSpeak Cloud
Google Sheets
      |
      V
n8n Automation
      |
      V
AI Agent
      |
      V
Telegram Voice Alert
      |
      V
Control Room

5. Working Principle

  1. Initialize all sensors and modules.
  2. Collect vibration data from MPU6050.
  3. Read temperature using DHT22.
  4. Detect obstacles using ultrasonic sensor.
  5. Capture railway track images.
  6. Perform AI image analysis.
  7. Detect cracks and faults.
  8. Read GPS coordinates.
  9. Calculate risk score.
  10. Upload information to cloud.
  11. Store records in Google Sheets.
  12. Generate Telegram notifications.
  13. Send voice alerts to railway officials.

6. Circuit Connections

MPU6050

VCC -> 3.3V
GND -> GND
SDA -> GPIO21
SCL -> GPIO22

GPS NEO-6M

VCC -> 3.3V
GND -> GND
TX -> GPIO16
RX -> GPIO17

DHT22

DATA -> GPIO4
VCC -> 3.3V
GND -> GND

HC-SR04

TRIG -> GPIO5
ECHO -> GPIO18

IR Sensor

OUT -> GPIO15
VCC -> 3.3V
GND -> GND

7. Flowchart


START

↓

Initialize System

↓

Collect Sensor Data

↓

Capture Image

↓

AI Detection

↓

Fault Found?

YES ------------------- NO
 |                       |
 V                       |
Get GPS                  |
 |                       |
 V                       |
Upload Cloud             |
 |                       |
 V                       |
AI Risk Prediction       |
 |                       |
 V                       |
Telegram Alert           |
 |                       |
 V                       |
Voice Notification       |
 |                       |
 V                       |
END <--------------------

8. Computer Vision Module

YOLOv8 Nano model is used for crack detection and obstacle identification. 

pip install ultralytics

yolo detect train \
data=rail.yaml \
model=yolov8n.pt \
epochs=100

Output Model: best.pt

9. ESP32 Source Code

#include <WiFi.h>
#include <HTTPClient.h>

const char* ssid="WiFi_Name";
const char* password="WiFi_Password";

void setup()
{
 Serial.begin(115200);

 WiFi.begin(ssid,password);

 while(WiFi.status()!=WL_CONNECTED)
 {
   delay(1000);
 }
}

void loop()
{
 HTTPClient http;

 http.begin("https://api.thingspeak.com/update");

 http.GET();

 http.end();

 delay(15000);
}

10. ThingSpeak Setup

  • Create ThingSpeak account.
  • Create channel.
  • Add fields:
    • Temperature
    • Vibration
    • Crack Status
    • GPS Latitude
    • GPS Longitude
    • Risk Score
  • Copy API Key.
  • Use API key in ESP32 code.

11. Google Sheets Integration

Timestamp Temperature Vibration Risk Score
10:00 40°C 8.2 92%

12. Telegram Bot Setup

  1. Open Telegram.
  2. Search BotFather.
  3. Create new bot.
  4. Get Bot Token.
  5. Configure Telegram API.
  6. Connect n8n workflow.

13. n8n Workflow


Webhook

↓

ThingSpeak Data

↓

AI Agent

↓

Risk > 70 ?

↓

Telegram Alert

↓

Voice Generator

↓

Google Sheets Update

↓

Dashboard Update

14. AI Risk Prediction Logic

Risk Score = 0.4 × Crack + 0.3 × Vibration + 0.2 × Temperature + 0.1 × Alignment

Risk Score Status
0 - 30 Safe
31 - 60 Warning
61 - 80 High Risk
81 - 100 Critical

15. Telegram Voice Alert


WARNING!

Railway Track Crack Detected

Latitude : 17.3850
Longitude: 78.4867

Risk Score: 92%

Immediate Inspection Required

16. Future Enhancements

  • Edge AI Deployment
  • LoRa Communication
  • 4G/5G Backup Network
  • Digital Twin Dashboard
  • Automatic Signal Control
  • Predictive Maintenance Analytics
  • Railway Control Center Integration

17. Deployment Guide

Prototype Stage

  • Single Track Section
  • One ESP32 Node
  • One Camera Module

Pilot Deployment

  • 1-5 km Railway Section
  • Solar Powered Sensor Nodes
  • Cloud Monitoring

Production Deployment

  • Sensor Nodes Every 500 m
  • Central AI Server
  • 24/7 Monitoring Dashboard

18. Conclusion

The AI-Based Railway Track Fault Prediction and Autonomous Alert System provides intelligent monitoring, real-time fault detection, predictive maintenance, cloud analytics, GPS tracking, and automated Telegram voice alerts. The solution enhances railway safety and minimizes accident risks through continuous monitoring and AI-driven decision-making.

© Railway Fault Prediction Project | IoT + AI + ESP32 + n8n
Save the file as railway_fault_prediction.php, place it in your PHP server folder (e.g., XAMPP htdocs), and open: http://localhost/railway_fault_prediction.php This will display the complete project documentation as a professional PHP web page.

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