AI-Based Industrial Fault Prediction and Monitoring System

AI-Based Industrial Fault Prediction and Monitoring System AI-Powered ESP32 + Agentic IoT + n8n Automation + Telegram Voice Alerts + Google Sheets + ThingSpeak Cloud Dashboard

AI-Based Industrial Fault Prediction and Monitoring System AI-Powered ESP32 + Agentic IoT + n8n Automation + Telegram Voice Alerts + Google Sheets + ThingSpeak Cloud Dashboard 1. Project Overview Project Title AI-Based Industrial Fault Prediction and Monitoring System Using ESP32, AI Agent, n8n Automation, Telegram Voice Alerts, Google Sheets, and ThingSpeak Cloud Objective Develop an Industry 4.0 smart industrial monitoring platform capable of: Monitoring machine temperature Monitoring vibration levels Monitoring current consumption Monitoring humidity Predicting machine faults using AI Sending instant alerts Logging data to cloud Providing dashboard visualization Generating voice notifications Predicting power consumption trends The system continuously monitors machine health and predicts failures before breakdowns occur. 2. System Architecture Industrial Machine │ ▼ Sensors Layer ┌─────────────────┐ │ DHT22 │ │ Vibration SW420 │ │ ACS712 Current │ │ LM35 Temperature│ └─────────────────┘ │ ▼ ESP32 │ ▼ WiFi Network │ ┌──────┼───────────┐ ▼ ▼ ▼ ThingSpeak Google Sheets n8n Automation Server │ ▼ AI Agent Engine │ ┌─────────┴────────┐ ▼ ▼ Telegram Alerts Voice Alerts 3. Features Real-Time Monitoring Temperature Humidity Vibration Current Consumption AI Prediction Fault Prediction Power Usage Forecast Machine Health Analysis Cloud Services ThingSpeak Dashboard Google Sheets Storage Automation n8n Workflow Telegram Bot Voice Notifications 4. Components Required Component Quantity ESP32 Dev Board 1 DHT22 Sensor 1 SW420 Vibration Sensor 1 ACS712 Current Sensor 1 LM35 Temperature Sensor 1 Breadboard 1 Jumper Wires Several 5V Power Supply 1 WiFi Router 1 Computer 1 5. Pin Connections DHT22 DHT22 ESP32 VCC 3.3V GND GND DATA GPIO4 SW420 SW420 ESP32 VCC 3.3V GND GND OUT GPIO27 ACS712 ACS712 ESP32 VCC 5V GND GND OUT GPIO34 LM35 LM35 ESP32 VCC 5V GND GND OUT GPIO35 6. Circuit Schematic Diagram +----------------+ | ESP32 | | | GPIO4 <---- DHT22 DATA GPIO27 <---- SW420 OUT GPIO34 <---- ACS712 OUT GPIO35 <---- LM35 OUT | | WiFi ▼ Cloud Services 7. Flowchart Start │ ▼ Initialize ESP32 │ ▼ Connect WiFi │ ▼ Read Sensors │ ▼ Calculate Parameters │ ▼ AI Prediction │ ▼ Fault Detected? │ ┌───────┴─────────┐ │ │ Yes No │ │ ▼ ▼ Send Alerts Store Data │ │ ▼ ▼ Update Cloud Dashboard │ ▼ Repeat 8. ESP32 Source Code #include #include #include #define DHTPIN 4 #define DHTTYPE DHT22 DHT dht(DHTPIN,DHTTYPE); const char* ssid="YOUR_WIFI"; const char* password="YOUR_PASSWORD"; String apiKey="THINGSPEAK_API_KEY"; void setup() { Serial.begin(115200); dht.begin(); WiFi.begin(ssid,password); while(WiFi.status()!=WL_CONNECTED) { delay(500); } } void loop() { float humidity=dht.readHumidity(); float temperature=dht.readTemperature(); int vibration=digitalRead(27); int currentRaw=analogRead(34); float current=currentRaw*0.026; int lm35=analogRead(35); float machineTemp=(lm35*3.3*100)/4095; String health="Normal"; if(machineTemp>60 || vibration==1) { health="Fault Predicted"; } if(WiFi.status()==WL_CONNECTED) { HTTPClient http; String url= "http://api.thingspeak.com/update?api_key=" +apiKey+ "&field1="+String(temperature)+ "&field2="+String(humidity)+ "&field3="+String(machineTemp)+ "&field4="+String(current)+ "&field5="+String(vibration); http.begin(url); http.GET(); http.end(); } delay(15000); } 9. AI Fault Prediction Logic Input Parameters Temperature Humidity Current Vibration AI Rules Critical Fault Temp > 70°C AND Current > 15A AND Vibration = HIGH Result: Machine Failure Likely Warning Temp > 60°C OR Current > 10A Result: Maintenance Required Normal All parameters within limits Result: Healthy Machine 10. Power Consumption Prediction Formula: P=V×I Example: Voltage = 230V Current = 5A Power = 1150W AI Agent stores historical data and predicts: Next hour power usage Daily energy usage Monthly energy consumption 11. ThingSpeak Dashboard Setup Create Channel Fields: Field 1: Temperature Field 2: Humidity Field 3: Machine Temperature Field 4: Current Field 5: Vibration Field 6: Fault Status Dashboard Widgets Gauge Line Chart Fault Indicator Energy Consumption Graph 12. Google Sheets Integration Create Sheet: Timestamp Temperature Humidity Current Vibration MachineTemp Status Prediction 13. n8n Workflow Workflow Logic Webhook │ ▼ Receive ESP32 Data │ ▼ AI Analysis Node │ ▼ IF Fault? │ ┌─┴───────────┐ ▼ ▼ Telegram Google Sheet Alert Update n8n Workflow JSON Structure { "nodes":[ { "name":"Webhook" }, { "name":"AI Agent" }, { "name":"IF Fault" }, { "name":"Telegram" }, { "name":"Google Sheets" } ] } 14. Telegram Bot Setup Step 1 Open Telegram Search: @BotFather Create bot: /newbot Step 2 Get: BOT TOKEN Step 3 Get Chat ID Send: /start Use chat ID API. 15. Telegram Alert Messages Text Alert ⚠ INDUSTRIAL ALERT Machine Temperature : 75°C Current : 12A Vibration : HIGH Prediction : Bearing Failure Expected Immediate Inspection Required. 16. Voice Notification Automation n8n uses: Text Warning. Machine Number 3. Abnormal vibration detected. Maintenance required. Convert To Speech Using: Telegram Voice Google TTS OpenAI TTS Edge TTS Send Voice Message Telegram Voice Notification 🎤 Voice Alert Sent 17. AI Agent Analytics The AI Agent performs: Root Cause Analysis Example: High Temperature + High Current Cause: Motor Overloading Predictive Maintenance Example: Bearing Wear Motor Failure Cooling Fan Fault Power Supply Issues 18. Cloud Dashboard Features Real-Time Machine Status Live Charts Sensor Monitoring Historical Daily Reports Weekly Reports Monthly Reports AI Analytics Fault Prediction Energy Forecasting Maintenance Suggestions 19. Future Enhancements Machine Learning Random Forest XGBoost LSTM Prediction Edge AI Run TinyML directly on ESP32 Computer Vision Add camera-based fault detection Digital Twin Virtual machine monitoring Multi-Machine Monitoring 100+ industrial machines Mobile App Android and iOS app MQTT Industrial-grade communication AWS/Azure Integration Enterprise deployment 20. Deployment Guide Small Factory 1 ESP32 1 Machine ThingSpeak Dashboard Medium Industry 10 ESP32 Nodes Central n8n Server Google Sheets Database Large Industry 100+ ESP32 Devices MQTT Broker AI Analytics Server Cloud Dashboard ERP Integration Final Outcome This project creates a complete Industry 4.0 AI-Based Industrial Fault Prediction and Monitoring Platform combining: ✅ ESP32 IoT Monitoring ✅ Temperature, Vibration & Current Sensing ✅ AI Agent Fault Prediction Analytics ✅ n8n Workflow Automation ✅ Google Sheets Database Logging ✅ Telegram Text & Voice Alerts ✅ ThingSpeak Cloud Dashboard ✅ Power Consumption Prediction ✅ Predictive Maintenance System ✅ Cloud-Based Industrial Monitoring Solution ✅ Scalable Smart Factory Deployment Architecture ✅ Real-Time Fault Detection and Early Warning System