Maintenance Engineering#1166
Maintenance Planning & Scheduling Training Course
Shows how to plan, prioritize and schedule maintenance work effectively, balancing workloads and resources to raise equipment reliability and efficiency.
5 Days·Classroom
AI in Smart Maintenance Training Course
Shows how machine learning, anomaly detection and condition monitoring data support predictive maintenance, earlier fault detection and less unplanned downtime.
MAI · Maintenance EngineeringAll Levels#2705ClassroomEnglish , Arabic
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Free substitution at any time · 30+ days' notice: full refund minus 10%. Cancellation policy
Sectors:Industrial Engineering, Maintenance & OperationsCategories:Maintenance Engineering
Course Overview
The AI in Smart Maintenance Training Course explores how artificial intelligence can improve maintenance planning, equipment reliability, fault detection, and asset performance in industrial environments. It focuses on practical applications of AI and data analytics to reduce unplanned downtime, optimize maintenance resources, and support data-driven decision-making. The course covers predictive maintenance, machine learning, condition monitoring, anomaly detection, failure prediction, and the integration of AI with computerized maintenance management systems (CMMS), industrial sensors, and Internet of Things (IoT) technologies. It also examines how historical maintenance records and real-time operating data can support earlier fault identification and more effective maintenance interventions. Through practical exercises and industrial case studies, participants learn to identify suitable AI applications, interpret analytical outputs, evaluate implementation requirements, and develop strategies for integrating intelligent maintenance solutions into existing maintenance operations.
Learning Objectives
- Explain the role of artificial intelligence in smart maintenance.
- Differentiate between preventive, predictive, and condition-based maintenance.
- Identify suitable AI applications for industrial equipment and assets.
- Understand machine learning and anomaly detection techniques used in maintenance.
- Interpret equipment condition data and maintenance performance indicators.
- Evaluate the use of sensors, IoT platforms, and CMMS data in AI-driven maintenance.
- Assess the benefits, limitations, and implementation challenges of AI solutions.
- Develop an implementation plan for improving maintenance reliability through AI.
Who Should Attend
This course is designed for Maintenance Engineers, Reliability Engineers, Asset Management Professionals, Maintenance Managers, and technical personnel responsible for equipment performance, maintenance planning, and asset reliability.
It is also suitable for Instrumentation and Control Engineers, Mechanical and Electrical Engineers, Condition Monitoring Specialists, Maintenance Planners, and digital transformation professionals working in oil and gas, petrochemicals, power generation, manufacturing, and other asset-intensive industries.
Learning Outcomes
- Explain the main concepts and applications of AI in maintenance operations.
- Identify equipment and processes suitable for predictive maintenance.
- Understand how machine learning models support failure prediction.
- Interpret vibration, temperature, pressure, and other condition-monitoring data.
- Recognize abnormal operating patterns and potential equipment failures.
- Evaluate maintenance data quality and its impact on AI performance.
- Understand the integration of AI solutions with CMMS and industrial systems.
- Assess AI-generated recommendations alongside engineering judgment.
- Identify implementation risks, data limitations, and cybersecurity considerations.
- Prepare an action plan for introducing AI into maintenance operations.
Course Outline
Smart Maintenance Fundamentals and AI Applications
- Evolution of maintenance strategies: reactive, preventive, predictive, and prescriptive maintenance.
- Fundamentals of artificial intelligence, machine learning, and industrial analytics.
- Smart maintenance architecture and data-driven decision-making.
- Applications of AI in rotating equipment, pumps, compressors, motors, and industrial assets.
- Key maintenance performance indicators, including equipment availability, MTBF, and MTTR.
- Practical exercise: Identify potential AI applications within an industrial maintenance environment.
Industrial Data, Sensors, and Condition Monitoring
- Sources of maintenance data: sensors, inspection records, work orders, and equipment histories.
- Condition-monitoring techniques using vibration, temperature, pressure, and lubricant data.
- IoT sensors, industrial connectivity, and data collection systems.
- Data quality, missing values, inconsistent records, and data preparation.
- Establishing equipment baselines and identifying abnormal operating conditions.
- Practical exercise: Review sample condition-monitoring data and identify potential warning signs.
Predictive Maintenance and Machine Learning
- Principles of predictive maintenance and equipment failure prediction.
- Introduction to supervised and unsupervised machine learning.
- Anomaly detection and identification of abnormal equipment behaviour.
- Failure pattern recognition and remaining useful life estimation.
- Using historical failure records to support maintenance decisions.
- Understanding model accuracy, false alarms, and missed failure warnings.
- Practical exercise: Develop a conceptual predictive maintenance workflow for a selected asset.
AI Integration, Maintenance Optimization, and Decision Support
- Integrating AI outputs with CMMS, enterprise asset management, and industrial control systems.
- AI-assisted work order prioritization and maintenance scheduling.
- Optimizing maintenance intervals, spare parts planning, and resource allocation.
- Using dashboards and analytics to support maintenance decisions.
- Evaluating AI recommendations against operating conditions and engineering knowledge.
- Cybersecurity, data governance, and operational risks in connected maintenance systems.
- Practical exercise: Design an AI-supported maintenance decision process for a critical asset.
Implementation Strategy and Smart Maintenance Improvement
- Assessing organizational readiness and selecting suitable pilot projects.
- Defining business objectives, technical requirements, and data needs.
- Developing an AI implementation roadmap and change management approach.
- Measuring results through reliability, availability, downtime, and maintenance cost indicators.
- Monitoring model performance and maintaining continuous improvement.
- Final workshop: Develop a smart maintenance implementation plan covering asset selection, data collection, AI applications, system integration, performance indicators, and expected operational benefits.
Upcoming Dates
Selected date
Lisbon · 7 – 11 Dec 2026 · Classroom
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| City | Dates | Price per Participant | Early Bird | |
|---|---|---|---|---|
DohaScheduled | 2 – 6 Nov 2026 | €4,700 | — | |
DubaiScheduled | 8 – 12 Nov 2026 | €4,700 | — | |
GenevaScheduled | 16 – 20 Nov 2026 | €7,300 | — | |
IstanbulScheduled | 23 – 27 Nov 2026 | €5,300 | €4,770until 2 Nov | |
JeddahScheduled | 29 Nov – 3 Dec 2026 | €4,800 | €4,320until 9 Nov | |
LisbonScheduled | 7 – 11 Dec 2026 | €6,000 | €5,400until 16 Nov | |
Kuala LumpurScheduled | 7 – 11 Dec 2026 | €4,700 | €4,230until 16 Nov | |
LondonScheduled | 14 – 18 Dec 2026 | €5,800 | €5,220until 23 Nov | |
MadinahScheduled | 21 – 25 Dec 2026 | €4,800 | €4,320until 30 Nov | |
MadridScheduled | 28 Dec 2026 – 1 Jan 2027 | €6,000 | €5,400until 7 Dec | |
MakkahScheduled | 4 – 8 Jan 2027 | €4,800 | €4,320until 14 Dec | |
ManamaScheduled | 11 – 15 Jan 2027 | €5,300 | €4,770until 21 Dec | |
MarbellaScheduled | 18 – 22 Jan 2027 | €6,000 | €5,400until 28 Dec | |
MarrakeshScheduled | 18 – 22 Jan 2027 | €4,700 | €4,230until 28 Dec | |
MilanScheduled | 25 – 29 Jan 2027 | €7,000 | €6,300until 4 Jan | |
MunichScheduled | 1 – 5 Feb 2027 | €6,000 | €5,400until 11 Jan | |
MuscatScheduled | 8 – 12 Feb 2027 | €6,300 | €5,670until 18 Jan | |
OnlineScheduled | 14 – 18 Feb 2027 | €2,700 | €2,430until 25 Jan | |
ParisScheduled | 22 – 26 Feb 2027 | €6,700 | €6,030until 1 Feb | |
RomeScheduled | 1 – 5 Mar 2027 | €7,000 | €6,300until 8 Feb | |
Sharm El-SheikhScheduled | 8 – 12 Mar 2027 | €5,300 | €4,770until 15 Feb | |
ViennaScheduled | 8 – 12 Mar 2027 | €6,400 | €5,760until 15 Feb | |
AmmanScheduled | 14 – 18 Mar 2027 | €4,000 | €3,600until 22 Feb | |
AmsterdamScheduled | 22 – 26 Mar 2027 | €6,000 | €5,400until 1 Mar | |
BakuScheduled | 29 Mar – 2 Apr 2027 | €5,300 | €4,770until 8 Mar | |
BangkokScheduled | 5 – 9 Apr 2027 | €6,700 | €6,030until 15 Mar | |
BarcelonaScheduled | 12 – 16 Apr 2027 | €6,000 | €5,400until 22 Mar | |
CairoScheduled | 19 – 23 Apr 2027 | €3,800 | €3,420until 29 Mar | |
CasablancaScheduled | 26 – 30 Apr 2027 | €4,700 | €4,230until 5 Apr | |
DammamScheduled | 26 – 30 Apr 2027 | €4,800 | €4,320until 5 Apr |
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