Subject Overview

An advanced study of preventive and predictive maintenance for production systems, integrating reliability engineering, condition monitoring, maintenance planning, analytics, asset economics, digital technologies and managerial governance. The subject balances established maintenance practices with AI-enabled prognostics, Industry 4.0 and sustainable asset management.

Learning Goal

Enable students to design, evaluate and lead risk-based maintenance systems that improve equipment reliability, availability, safety, quality and lifecycle value through preventive practices, condition monitoring, analytics and responsible digital transformation.

Syllabus

1. Maintenance Management Foundations

Learning Objective: Explain maintenance terminology, objectives, failure consequences and the evolution from reactive maintenance to preventive, predictive, proactive and prescriptive approaches; relate maintenance to production, quality, safety and profitability.

Category: foundation

2. Asset Criticality and Maintenance Strategy Selection

Learning Objective: Classify production assets, perform criticality and risk assessments, and select appropriate run-to-failure, preventive, condition-based, predictive or redesign strategies using structured decision criteria.

Category: core

3. Reliability, Availability and Maintainability Engineering

Learning Objective: Apply failure-rate concepts, bathtub curves, reliability distributions, MTBF, MTTF, MTTR, availability, maintainability and system reliability models to maintenance decisions.

Category: core

4. Failure Analysis and Reliability-Centred Maintenance

Learning Objective: Use functional analysis, FMEA/FMECA, fault-tree analysis, root-cause analysis, Pareto analysis and reliability-centred maintenance to identify failure modes and define effective maintenance tasks.

Category: core

5. Preventive Maintenance System Design

Learning Objective: Develop inspection, servicing, lubrication, calibration, replacement and overhaul schedules; determine task intervals using age, usage, risk and regulatory requirements; evaluate preventive-maintenance effectiveness.

Category: application

6. Condition Monitoring and Inspection Technologies

Learning Objective: Evaluate vibration analysis, thermography, oil and wear-debris analysis, ultrasound, motor-current analysis, process-parameter monitoring and non-destructive testing for different equipment and failure modes.

Category: core

7. Predictive Maintenance Process and Data Architecture

Learning Objective: Design the end-to-end predictive-maintenance process covering data acquisition, sensor selection, sampling, connectivity, data quality, feature engineering, condition indicators, thresholds and alert management.

Category: application

8. Diagnostics, Prognostics and Maintenance Analytics

Learning Objective: Apply statistical process control, trend analysis, anomaly detection, degradation modelling, remaining-useful-life estimation and uncertainty analysis to diagnose faults and forecast failures.

Category: advanced

9. AI and Machine Learning for Predictive Maintenance

Learning Objective: Assess supervised, unsupervised and deep-learning methods, digital twins and prescriptive analytics; address model validation, explainability, concept drift, false alarms, human oversight and deployment limitations.

Category: contemporary

10. Maintenance Planning, Scheduling and Execution

Learning Objective: Plan work orders, labour, tools, permits, shutdowns, job plans and backlogs; coordinate maintenance with production using priority rules, weekly scheduling, standard work and execution control.

Category: application

11. Spare Parts, Resources and Contractor Management

Learning Objective: Optimise maintenance inventory using criticality, service levels, lead times and lifecycle demand; manage repairable spares, skills, outsourcing, service contracts and supplier performance.

Category: application

12. Maintenance Economics and Performance Management

Learning Objective: Evaluate lifecycle cost, total cost of ownership, downtime losses, replacement decisions and maintenance investments; design KPI systems using OEE, availability, schedule compliance, planned-work ratio and cost measures.

Category: strategic

13. Digital Maintenance and Enterprise Integration

Learning Objective: Integrate CMMS/EAM, ERP, MES, SCADA, IIoT, mobile systems, cloud and edge platforms; develop data governance, interoperability, cybersecurity and digital-transformation roadmaps for smart maintenance.

Category: contemporary

14. Asset Governance, Leadership and Future Practice

Learning Objective: Align maintenance with ISO 55000 principles, safety and environmental obligations; lead organisational change, audit performance and address ethics, workforce impacts, sustainability, autonomous maintenance and emerging self-optimising asset systems.

Category: strategic