Prevent downtime with AI predictive maintenance in Industrial IoT.
Data Science & Analytics
Module-by-module breakdown of AI for Predictive Maintenance in Industrial IoT, from foundations to a certified capstone project.
Instrumentation
β’ Vibration, acoustic, thermal and current signature sensing, and what each detects
β’ Sensor placement, mounting and the sampling rates faults actually require
β’ Industrial protocols: OPC UA, Modbus and the realities of brownfield plants
Edge
β’ Edge versus cloud processing under bandwidth and latency constraints
β’ Time-series storage, compression and retention for high-rate signals
β’ Time synchronisation across assets, without which correlation is meaningless
Diagnostics
β’ Spectral analysis, envelope detection and bearing fault frequencies
β’ Condition indicators and thresholds grounded in machinery standards
β’ Anomaly detection where labelled failures are scarce or absent
Prognostics
β’ Degradation modelling and RUL estimation with calibrated uncertainty
β’ Run-to-failure data scarcity and using survival analysis with censored records
β’ Translating a probability of failure into a maintenance decision
Operations
β’ CMMS integration so predictions become scheduled work orders
β’ Operator trust, false alarms and the cost of an unnecessary shutdown
β’ Measuring benefit in avoided downtime rather than model accuracy
e-Certificate and e-Marksheet issued on successful completion.