AI-Powered · Edge Sensing · Real-Time

AI Infrastructure Platform · Indian Railways

Production-Grade AI
on India's Railway
Infrastructure

We build AI and IoT sensor systems deployed on the live railway network — detecting anomalies, predicting faults, and sending instant alerts before failures happen. This is not prototype work. This is AI on critical infrastructure that protects millions of passengers.

68,000+
km of Track
One of the world's largest networks
13,000+
Trains Daily
Continuous, round-the-clock operations
< 2 sec
AI Alert Response
From detection to control room

Two AI Sensor Systems,
One Safety Mission

Purpose-built AI sensing platforms deployed trackside and on overhead infrastructure — each tackling a top cause of disruption and derailment on Indian Railways.

Live Line
Monitoring Gauge

The Live Line Monitoring Gauge (LLMG) is an AI-powered sensor system deployed on Indian Railways infrastructure to provide real-time monitoring of overhead equipment (OHE) and traction power lines. It detects anomalies, predicts faults, and sends instant alerts — before failures happen.

68,000+
km of Railway Track
~3,000
OHE failures / year
< 2 sec
AI Alert Response Time
94%+
Anomaly Detection Accuracy

What It Does

  • Monitors live voltage, current, sag, and contact wire condition 24×7
  • AI-enhanced sensor output filters noise and corrects raw readings for higher accuracy
  • Detects pantograph arcing, wire breaks, and power fluctuations in real time
  • Predicts failures before they occur using historical trend analysis
  • Sends automated alerts to control rooms and maintenance teams instantly

Why This Matters

  • OHE is the most critical, high-risk asset on the electrified network
  • Failures are typically caught only after a train stops or a section trips
  • Manual inspection cycles are far too infrequent to catch early degradation
  • A single failure cascades into delays across dozens of trains daily
  • Early detection can prevent the majority of these failures entirely
🧠 AI Role — The Core Differentiator
🎯
Sensor Accuracy Enhancement

Raw sensor signals are processed through ML models to remove noise, correct drift, and improve measurement precision beyond hardware limits.

🔬
Anomaly Detection

Trained models identify abnormal patterns — voltage spikes, current drops, sag irregularities — that rule-based systems miss.

🩺
Device Diagnostics

AI continuously monitors sensor health and autonomously flags degradation, calibration issues, or hardware faults.

📷 Images
🔔 Sample Alert Notification

LLMG Unit #MH-042 · Pune Division

Critical
Critical Anomaly Detected
Time
14:32:17 IST
Location
Km 187.4 · Mumbai–Pune Route
Parameter
Contact Wire Voltage
Reading
21.4 kV (Threshold: 22.0–27.5 kV)
Confidence
97.2%
Status
OPEN — Awaiting field inspection
AI Diagnosis & Action
Diagnosis: Sustained undervoltage detected. Possible OHE sag or feeder fault.
Auto-Action: Maintenance team alerted instantly. Ticket #IR-2024-08871 raised automatically.
All readings and resolution actions are logged for compliance and trend analysis.
💻 Software Portal & Mobile App
🖥️ Web Portal (Desktop)
  • Live dashboard: all LLMG units across divisions on a single map view
  • Trend graphs: voltage, current, temperature over time with anomaly flags
  • Alert management: assign, track, and close maintenance tickets
  • Device health: real-time diagnostics for each sensor unit
  • Reports: auto-generated daily/weekly PDF reports for section engineers
📱 Mobile App (iOS & Android)
  • Push notifications for critical alerts — received instantly on the field
  • One-tap ticket acknowledgment and status update
  • Offline mode: view last-known readings without connectivity
  • QR scan: scan any LLMG unit for instant health status
📌 Case Study — LLMG in Action
Case Study

OHE Fault Prevention — Central Railway, Pune Division

Location
Mumbai–Pune Route · high-density corridor
Deployment
12 LLMG units · 187 km of OHE
Timeline
[Month, Year] — Ongoing
Client
Indian Railways Pilot — Central Zone

The Problem

The Pune Division faced recurring unplanned OHE failures on the Mumbai–Pune stretch — one of the country's highest-traffic corridors. Failures were caught only after a train stopped or a section tripped, cascading delays across 50+ trains daily. Manual inspection every 30 days was far too infrequent to catch early degradation.

What We Deployed

  • 12 LLMG units across critical OHE spans covering 187 km
  • Edge AI processing on each unit — no connectivity dependency for initial detection
  • Integrated with the Division Control Office dashboard for live visibility
  • AI model trained on 18 months of historical fault data from this corridor

How AI Made the Difference

  • Detected a sustained voltage sag at Km 142.4 — within manual thresholds but showing a 6-day declining trend invisible to rule-based systems
  • Alert raised 11 days before the section would have tripped under projected load
  • Maintenance dispatched within 4 hours — found a corroded clamp causing progressive sag
  • Repair completed during a pre-scheduled traffic block — zero unplanned disruption

✅ Results Achieved

  • 0 unplanned OHE failures in the monitored stretch post-deployment
  • Manual patrol frequency reduced by 60%
  • Average fault detection lead time: 8–12 days before threshold breach
  • False positive rate: under 3%
🌡️

Hot Axle Box
Detection

The Hot Axle Box Detection (HABD) system is a trackside AI-powered thermal and vibration sensing solution that detects overheating axle boxes on passing trains — one of the top causes of derailments in Indian Railways. It provides real-time alerts with train ID, coach number, and severity before the train reaches the next station.

13,000+
Trains Operated Daily
#1
Cause: Hot Axle Derailments
180 km/h
Max Detection Speed
< 500ms
Per-Axle Detection Time

What It Does

  • Measures axle box temperature for every wheel of every passing train using infrared sensors
  • Detects abnormal heat signatures and vibration patterns that indicate bearing failure
  • Identifies the exact train, coach, and axle position in real time
  • Transmits alerts to the next station and loco pilot before the train arrives
  • Logs all readings for compliance, audit trails, and predictive maintenance

Why This Matters

  • Hot axle box failures are the #1 cause of bearing-related derailments
  • Traditional detection relied on staff visually inspecting passing trains
  • Visual inspection misses early-stage failures and is impossible at night or high speed
  • A single undetected failure can cause derailment and multi-hour line blocks
  • Sub-second per-axle detection enables intervention before the next station
🧠 AI Role — The Core Differentiator
🎯
Sensor Accuracy Enhancement

AI compensates for environmental interference — ambient temperature, weather, train speed — to deliver precise axle readings regardless of conditions.

🔬
Anomaly Detection

Models trained on thousands of axle signatures distinguish genuine bearing faults from false positives — reducing unnecessary train halts.

🩺
Device Diagnostics

The system self-checks sensor calibration, optical alignment, and communication health, auto-flagging any degradation for maintenance.

📷 Images
🔔 Sample Alert Notification

HOT AXLE ALERT · Station Nagpur

Urgent
Hot Axle Detected
Time
09:14:33 IST
Train
12105 Vidarbha Express · UP (towards Mumbai)
Detection Point
Km 824.6 · Sensor NGP-HABD-03
Hot Axle
Coach S7, Axle 4 (Wheel-B side)
Temperature
138°C (Threshold: 95°C)
Confidence
98.6%
AI Diagnosis & Action
Diagnosis: Bearing seizure — stage 2. Immediate inspection required.
Auto-Action: Alert sent to Nagpur Station Master + Loco Pilot. Speed restriction applied. Ticket #IR-HABD-2024-04421.
Status: URGENT — inspection required before departure.
💻 Software Portal & Mobile App
🖥️ Web Portal (Desktop)
  • Live train tracking: every train passing HABD units in real time on the route map
  • Per-axle heatmap: visual temperature readout for all 96+ axles of a passing train
  • Alert log: full history of hot axle events with AI diagnosis and resolution status
  • Trend analytics: temperature trends per train, route, and season
  • Compliance reports: export inspection-ready reports for Railway Board audit
📱 Mobile App (iOS & Android)
  • Real-time alert push with coach and axle details
  • Station master view: incoming train alerts with ETA and severity level
  • Loco pilot acknowledgment: one-tap confirm + speed restriction notification
  • Inspection checklist: field engineers log outcomes directly from the app
📌 Case Study — HABD in Action
Case Study

Bearing Failure Prevention — South Central Railway, Nagpur Section

Location
Nagpur–Wardha–Sewagram corridor
Deployment
3 HABD units · Nagpur, Wardha, Sewagram
Timeline
[Month, Year] — Ongoing
Client
Indian Railways Pilot — South Central Zone

The Problem

Hot axle box failures were the leading cause of unscheduled halts and derailment risk on this section. Trackside staff visually inspecting passing trains missed early-stage bearing failures — impossible at night or high speeds. One undetected failure had previously caused a goods train derailment and a 6-hour line block.

What We Deployed

  • 3 HABD units at station entry points — covering all UP and DN line traffic
  • IR thermal + vibration sensors on each unit for dual-parameter detection
  • Real-time integration with Station Master console and loco pilot alert system
  • AI model calibrated for the corridor's ambient temperature and typical train speeds

How AI Made the Difference

  • Flagged Coach S7, Axle 4 of Train 12105 at 138°C — and identified the rate-of-rise as Stage 2 bearing seizure, not a one-time spike
  • Without AI, a single high reading might have been cleared as brake drag; pattern analysis confirmed active bearing failure
  • Train held, coach isolated, bearing replaced — the bearing would have seized within 40–60 km, in an ungated section
  • Two months later, AI correctly ruled out a false positive from a hot brake disc — avoiding an unnecessary halt

✅ Results Achieved

  • 2 confirmed bearing failures intercepted before failure
  • 1 potential derailment averted (Stage 2 seizure, ungated section)
  • False positive rate reduced to under 2%
  • Detection-to-alert: under 500ms per axle at up to 110 km/h
  • Zero unplanned line blocks from missed hot axle events

Build Real AI for
India's Railways

We are a product engineering company building AI and IoT systems that run on India's railway network — one of the largest and most complex in the world. Every line of code, every model we train, every sensor we deploy directly impacts the safety of millions of passengers.

This is not prototype work. This is production-grade AI on critical infrastructure.

🛠️ What You'll Work On
🤖

AI / ML

Train and optimize models for anomaly detection, sensor signal processing, and predictive maintenance on real railway data.

🔌

IoT & Embedded

Design and program edge devices that run in harsh outdoor environments — temperature extremes, vibration, connectivity constraints.

⚙️

Electronics

Build and validate sensor hardware for thermal imaging, vibration measurement, and power line monitoring.

☁️

Software Platform

Develop the cloud backend, web portal, and mobile app that operations teams rely on 24×7.

💡 Technologies You'll Use
AI / ML
PythonTensorFlowPyTorch Edge InferenceSignal ProcessingTime-Series Anomaly Detection
Embedded
C / C++RTOSARM MCUs UART / SPI / I2C
IoT
MQTTLoRa4G / LTE EdgeOTA Updates
Electronics
PCB DesignThermal SensingIR Sensors Strain GaugesPower Electronics
Cloud & Backend
AWS / Azure IoTREST APIs Real-Time Data PipelinesPostgreSQL
Mobile
React NativeFlutter

Who We're Looking For

  • Engineers who want their work to ship to the field — not just a demo
  • Problem-solvers comfortable working across hardware and software
  • People curious about AI in constrained, noisy, real-world environments
  • Fresh graduates or experienced engineers from EE, ECE, CS, or related fields

What You'll Get

  • Exposure across the full stack: hardware → AI → cloud → mobile
  • Work on systems deployed across Indian Railways — live, at scale
  • Fast-moving team with direct access to founders and product decisions
  • Build your own domain expertise in Railway AI/IoT — a niche of massive national importance

Want to build AI that runs on the rails?

Send us your résumé and tell us what you'd love to work on.

Apply — mayurintron@gmail.com

Let's Build the Future of
Railway Infrastructure

For product inquiries, demos, or partnership discussions — reach out and our team will get back to you.

🏢
Company
IOTians Global Innovations Pvt Ltd
✉️
Email
mayurintron@gmail.com
📍
Location
Pune, India

Partner With Us

We're transforming railway infrastructure monitoring across India. Get in touch to learn how our AI sensor platform can reduce downtime, prevent derailments, and lower maintenance costs.

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