A hardware and software system using IoT sensors, cameras, and predictive AI to monitor iron ore conveyor belts, detect joint damage early, and prevent sudden belt ruptures.
Ministry of Steel · NMDC · Hardware
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Decomposed from what the description asks for. Nothing added.
IoT Sensor Network
Collects real time vibration, temperature, speed, tension, and alignment data along the conveyor belt.
Computer Vision Inspector
Analyzes camera and thermal image feeds to detect surface cracks, tears, edge wear, and belt misalignment.
Predictive Analytics Engine
Processes sensor data and vision feeds using machine learning models to predict belt joint failures before breakdown.
Digital Twin Dashboard
Displays real time conveyor belt health status, sensor readings, failure risk alerts, and digital twin simulations for operators.
Both columns are read off the brief's own wording. Nothing here is inferred from the ministry's name.
Evaluators will check if the hardware prototype combines sensor telemetry with camera or thermal image processing to monitor belt health. They will look for working predictive analytics that detect simulated joint wear or misalignment and trigger alerts. Demonstrating data output compatible with SCADA or PLC systems will also be evaluated.
A jury can still ask about these. Decide them deliberately rather than by accident.
Generated from the brief's own wording and the competition's published rules, never from a guess about what this ministry prefers.
Specific SCADA or PLC communication protocols required for integration?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
Exact accuracy target or failure prediction time window required?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
Sample dataset or logs of conveyor belt sensor readings?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
Wireless communication constraints in dusty mining conditions?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
Has any part of this been shown at a previous event, hackathon or college project?
The guidelines are explicit: your solution must not have appeared in any previous event or programme, of any sort. A recycled project is what a team under time pressure reaches for.
The brief asks for iot sensor network. How would you build that?
Decoded from SIH26008 itself: Collects real time vibration, temperature, speed, tension, and alignment data along the conveyor belt. The brief asks for it by name.
The brief asks for computer vision inspector. How would you build that?
Decoded from SIH26008 itself: Analyzes camera and thermal image feeds to detect surface cracks, tears, edge wear, and belt misalignment. The brief asks for it by name.
The brief asks for predictive analytics engine. How would you build that?
Decoded from SIH26008 itself: Processes sensor data and vision feeds using machine learning models to predict belt joint failures before breakdown. The brief asks for it by name.
The brief asks for digital twin dashboard. How would you build that?
Decoded from SIH26008 itself: Displays real time conveyor belt health status, sensor readings, failure risk alerts, and digital twin simulations for operators. The brief asks for it by name.
Where does your data come from: a published source, one you collect, or one you generate?
No dataset is attached to this problem statement, so sourcing it is part of the work and nobody told you that.
Why not use what already exists? Name the closest thing to this that is already running.
A team that has not named the alternative themselves is answering this for the first time in the room.
Which single thing will you demonstrate end to end, start to finish, with nothing skipped?
Ours, not a rule: a narrow thing that fully works survives questioning better than a broad thing that half works. If nobody on the team can name it, that is the finding.
Show me this working: evaluators will check if the hardware prototype combines sensor telemetry with camera or thermal image processing to monitor belt health.
This is the evaluator read for your problem statement, decoded from the brief's own wording.
Show me this working: they will look for working predictive analytics that detect simulated joint wear or misalignment and trigger alerts.
This is the evaluator read for your problem statement, decoded from the brief's own wording.
Each one is quoted from a gap in the brief, not a guess about your team.
Needs physical hardware
Listed as a hardware problem statement, so a working demo needs physical components you have to source yourself.
Needs data you may not get
The problem statement provides no dataset, requiring teams to create synthetic data or obtain private industrial conveyor sensor data.
No measurable target
The description lists general operational goals but does not define any measurable target for failure prediction accuracy or response time.
What the organisers attached, and what the brief assumes you can get.
Same organisation, same year. Reading two of theirs tells you more about what they care about than reading one.