Put AI on trucks, forklifts, and conveyors to predict failures, cut downtime, and optimise maintenance
What you'll learn
⚡ Implement Edge AI that runs directly on logistics assets for real-time, local fault detection
⚡ Select the right sensors - vibration, temperature, current, acoustic - for trucks, forklifts, and conveyors
⚡ Collect, clean, and pre-process sensor data at the edge so models get usable input
⚡ Build anomaly-detection and failure-prediction models, including remaining-useful-life estimates
⚡ Tune the false-alarm versus missed-failure trade-off that every predictive system faces
⚡ Deploy and manage machine-learning models across a fleet of edge devices, handling drift and updates
⚡ Operate predictively offline - localised decisions on assets with poor or no connectivity
⚡ Turn predictions into optimised maintenance schedules, work orders, and human-in-the-loop alerts
⚡ Prove the ROI of predictive maintenance in downtime, cost, and safety terms
⚡ Design and roll out a predictive-maintenance solution for one of your own assets
Requirements
❗ Familiarity with running or maintaining vehicles, equipment, or warehouse assets - no AI background needed
❗ No coding required to follow the concepts; the few technical examples are explained for practitioners
❗ An asset or fleet from your own operation in mind - you'll design a solution for it in the capstone
❗ Curiosity about sensors and data; everything else is taught from the ground up
Description
This course contains the use of artificial intelligence.
AI is used to reframe the words, fixing spelling mistakes and grammatical mistakes and audio conversion.
A forklift that dies mid-shift, a conveyor that stops the whole warehouse, a truck that breaks down two hundred kilometres from anywhere - every one of these was sending warning signs for days before it failed. Predictive maintenance with Edge AI is how you hear those signs and act before the breakdown, instead of after. This course is built for fleet managers, maintenance engineers, logistics operations managers, and the IoT developers and data scientists who support them. It teaches the practical implementation of Edge AI - intelligence that runs directly on the asset, not in a distant cloud - so a forklift or a truck can detect its own faults in real time and decide what to do, even with no connectivity. You will learn the full pipeline: the sensors that watch your assets, collecting and cleaning their data at the edge, the machine-learning models that spot anomalies and predict failures, deploying and managing those models across a fleet of devices, and turning a prediction into an optimised maintenance schedule that actually saves money.
It is hands-on and honest. You will see real case studies on trucks, forklifts, and conveyor systems. You will learn the brutal trade-off every predictive system faces - false alarms versus missed failures - and how to tune it. And you will design a predictive-maintenance solution for one of your own assets in the capstone. Every technique comes with one global, dollar-based example and one India, rupee-based example, so the numbers feel real wherever you operate. I have spent more than twenty years in warehouse, fleet, and manufacturing automation - living with the forklifts, conveyors, and material-handling systems this course is about. Enrol now and stop maintenance from being a thing that happens to you.
Who this course is for
⭐ Fleet managers wanting to cut breakdowns and downtime across vehicles and equipment
⭐ Maintenance engineers moving from scheduled servicing to condition-based, predictive maintenance
⭐ Logistics and warehouse operations managers responsible for uptime and safety
⭐ IoT developers and data scientists building predictive-maintenance solutions for logistics
⭐ Anyone evaluating whether Edge AI predictive maintenance is worth it for their assets
⭐ Professionals moving into the growing field of industrial and logistics AI

https://rapidgator.net/file/0467a6379b549efab3c15069cf440d6b/Predictive_Maintenance_with_Edge_AI_for_Logistics.rar.html