Specialised Technical Course
AI for Industrial Automation and Predictive Maintenance
Build a predictive maintenance use case by connecting equipment data, failure indicators, model validation and the operational review of predictions.
Course Overview
Examines AI applications for industrial automation and predictive maintenance. Participants review equipment data, model outputs and the operational checks needed before acting on predictions.
Who This Course Is For
- Maintenance engineers reviewing equipment performance
- Data specialists developing industrial models
- Automation engineers supporting operational decisions
- Operations teams assessing maintenance recommendations
What Participants Will Learn
Identify equipment data for maintenance analysis
Connect failure indicators with operating context
Define model validation and review requirements
Assess predictions before operational maintenance decisions
Outline a reviewed industrial AI workflow
Key topics
- Equipment data
- Failure indicators
- Model validation
- Operational response
Course Modules
Back to Course OverviewIndustrial AI and Maintenance Context
Industrial AI and Maintenance Context
Module overview
Review the connection between equipment data, failure indicators, validation and operational decisions in an industrial AI use case.
Key topics
Equipment data · Failure indicators · Model validation · Operational response
Learning outcome
Define a maintenance use case and identify the evidence needed to assess predictions.
Learning format
Technical explanation · worked examples · model exercise
Equipment Data and Failure Indicators
Equipment Data and Failure Indicators
Module overview
Assess available equipment information and identify the operating context required to interpret potential failure indicators.
Key topics
Equipment records · Operating conditions · Failure indicators · Data quality checks
Learning outcome
Identify relevant equipment data and uncertainties affecting indicator interpretation.
Learning format
Technical explanation · worked examples · model exercise
Model Outputs and Validation
Model Outputs and Validation
Module overview
Review prediction outputs and define how their usefulness and limitations will be assessed before operational use.
Key topics
Prediction outputs · Validation criteria · Error consequences · Evidence limitations
Learning outcome
Specify validation checks and explain the consequences of unreliable predictions.
Learning format
Technical explanation · worked examples · model exercise
Automation and Maintenance Response
Automation and Maintenance Response
Module overview
Map how reviewed predictions inform maintenance actions and define decision ownership and escalation points.
Key topics
Operational response · Human review · Automation boundaries · Maintenance coordination
Learning outcome
Outline response responsibilities and review points for a maintenance prediction.
Learning format
Technical explanation · worked examples · model exercise
Predictive Maintenance Application Case
Predictive Maintenance Application Case
Module overview
Develop an industrial AI proposal and review its data needs, validation checks and operational response.
Key topics
Industrial dataset · Use case outline · Validation plan · Response review
Learning outcome
Participants can outline a predictive maintenance use case with appropriate validation checks.
Learning format
Lectures, industrial datasets and application workshop
Programme Information
Course duration, location, practical components, training-centre information and applicable certification arrangements depend on the selected programme.
Learning format: Lectures, industrial datasets and application workshop
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