Predictive Maintenance and Asset Performance Training

Name: Predictive Maintenance and Asset Performance Management Training
Availability: InStock

Master condition monitoring, Industrial IoT, digital twins, and machine learning to optimize equipment reliability and asset lifecycle value.

Predictive Maintenance and Asset Performance Training

At a glance

Duration: 5 days
Format: Classroom
Cities: Munich, Johannesburg, Casablanca, Rome, Trabzon, Kuwait and more
Next session: 5 – 9 October 2026, Munich
Average fee: 7,550 €

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Predictive Maintenance and Asset Performance Management Training

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Overview

Industrial organizations face increasing pressure to prevent unplanned downtime, extend equipment life, and extract measurable value from physical assets. The Predictive Maintenance and Asset Performance Management Training provides structured methods to transition maintenance operations from reactive firefighting to predictive and prescriptive strategies. Participants explore Industrial IoT architecture, sensor deployment, anomaly detection, machine learning models, and digital twins, bridging the operational gap between field instrumentation and enterprise asset management software. Practical frameworks equip engineering teams to analyze asset criticality, establish reliable failure mode models, and calculate return on investment. This course is delivered by Agile Leaders Training Center.

Who Should Attend

Maintenance managers and plant supervisors seeking to transition from preventive schedules to condition-based intervention.
Reliability engineers and condition-monitoring specialists responsible for vibration analysis, thermography, and oil diagnostics.
Asset managers and plant engineers overseeing equipment lifecycle planning and criticality evaluations.
Operations engineers and automation specialists integrating field operational technology with enterprise platforms.
Digital transformation leads and Industrial IoT professionals designing connected asset strategies.

Departments and Industries

This program supports technical teams managing heavy rotating equipment, continuous production lines, and high-capital infrastructure across asset-intensive sectors.

Maintenance and Reliability Engineering Teams in Manufacturing and Industrial Production
Plant Operations and Integrity Divisions in Oil, Gas, and Process Industries
Asset Management Units in Energy, Power Generation, and Utilities
Condition Monitoring and Technical Services Teams in Mining and Heavy Industry
Engineering and Automation Divisions in Logistics, Transportation, and Pharmaceuticals

Learning Objectives

By the end of this course, participants will be able to:

Contrast reactive, preventive, predictive, and prescriptive maintenance operating models.
Evaluate asset criticality and lifecycle degradation to prioritize monitoring investments.
Deploy appropriate wired and wireless sensors alongside Industrial IoT network architectures.
Apply condition-monitoring technologies including vibration, thermography, ultrasonics, and lubricant testing.
Implement machine learning algorithms for anomaly detection and remaining useful life estimation.
Integrate digital twins and CMMS platforms with enterprise asset performance management solutions.
Execute root cause analysis and failure mode and effects analysis on critical production assets.
Formulate an asset performance management roadmap that incorporates cybersecurity and energy efficiency.

Course Agenda

Day 1: Condition Monitoring and Industrial IoT Architecture

Evolution of Maintenance Strategies: Reactive, Preventive, Predictive, and Prescriptive Models
Core Principles of Asset Performance Management and Operational Asset Lifecycle Stages
Asset Criticality Assessment Frameworks and Failure Consequence Scoring
Financial Justification, Return on Investment Calculations, and Business Case Formulation
Industrial IoT Architecture: Smart Sensors, Gateways, Data Acquisition, and Legacy Integration
Condition-Monitoring Methods: Vibration, Thermography, Ultrasound, Lubricant Analysis, and Acoustic Emission

Day 2: Maintenance Analytics, Digital Twins, and Systems Integration

Maintenance Data Governance: Ingestion, Cleaning, Normalization, and Quality Control
Descriptive, Diagnostic, Predictive, and Prescriptive Maintenance Analytics
Machine Learning Algorithms for Failure Pattern Recognition and Fault Detection
AI-Driven Anomaly Detection and Remaining Useful Life Estimation Principles
Digital Twin Foundations: Creating Virtual Representations of Critical Production Assets
Edge and Cloud Architecture, CMMS Integration, Interoperability, and Scalability

Day 3: Reliability Engineering and Risk-Based Prioritization

Structured Root Cause Analysis Methodologies for Mechanical and Electrical Failure
Failure Mode and Effects Analysis for Identifying Failure Mechanisms
Reliability Data Collection: Weibull Analysis and Failure Distribution Modelling
Risk-Based Maintenance Planning and Dynamic Work Prioritization
Resource Allocation, Labor Balancing, and Maintenance Cost-Benefit Analysis
Asset Performance Metrics: Tracking MTBF, MTTR, Availability, and OEE on Dashboards

Day 4: Operational Cybersecurity, Sustainability, and APM Roadmap

Connected Asset Cybersecurity: Protecting Industrial IoT and IT/OT Convergence Layers
Sustainable Maintenance: Energy Consumption Reduction and Decarbonization Targets
Implementation Planning: Work Streams, Pilot Projects, and Agile Deployment Phases
Change Management: Operator Buy-In, Skill Gap Analysis, and Workforce Readiness
Enterprise Scaling: Harmonizing IoT Sensors, AI Models, CMMS, and APM Software
Capstone Project: Developing an Integrated Predictive Maintenance and Asset Performance Management Roadmap

Practical Exercises

Participants apply classroom concepts through structured analytical exercises focused on industrial applications.

Suggested activity: Conduct an asset criticality assessment on rotating machinery to define monitoring priorities.
Suggested activity: Select appropriate condition-monitoring technologies based on specific mechanical failure modes.
Suggested activity: Evaluate sensor data streams to identify operational anomalies and estimate remaining useful life.
Suggested activity: Construct an asset performance management roadmap outlining technical integration and governance milestones.

Frequently Asked Questions

What specific qualifications or prerequisites are needed before enrolling in the course?

Participants should have a foundational understanding of industrial maintenance practices, mechanical equipment operation, engineering fundamentals, or plant supervision. Prior experience working with maintenance databases, condition monitoring tools, or automation systems is helpful but not required.

How long is each day's session, and what is the total course duration?

Each training day runs for five to six hours, encompassing lectures, technical case discussions, and practical exercises. The full program spans four days, delivering 20 to 24 instructional hours.

Differentiating Features

Unlike standalone vibration or thermography courses, this program unites field condition monitoring with enterprise asset performance management, digital twins, and machine learning analytics. Participants examine the entire data pipeline from sensor placement and Industrial IoT networking to predictive algorithms and CMMS work order triggering. The curriculum addresses IT/OT cybersecurity and sustainability alongside reliability engineering, concluding with an operational roadmap that aligns technical execution with organizational maintenance budgets.

credits: 5 credit per day

Course Mode: full-time

Provider: Agile Leaders Training Center

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Location	Dates	Duration	Mode	Price
Toronto	Week 07, 2027 21 – 25 February 2027	5 Days	Onsite	€16,000
Cape town	Week 08, 2027 28 February – 4 March 2027	5 Days	Onsite	€6,000
Dubai	Week 12, 2027 22 – 26 March 2027	5 Days	Onsite	€6,500
Accra	Week 12, 2027 28 March – 1 April 2027	5 Days	Onsite	€6,000
Manama	Week 13, 2027 4 – 8 April 2027	5 Days	Onsite	€6,500
Al Jubail	Week 13, 2027 4 – 8 April 2027	5 Days	Onsite	€7,500
Abu Dhabi	Week 15, 2027 12 – 16 April 2027	5 Days	Onsite	€6,500
Baku	Week 16, 2027 19 – 23 April 2027	5 Days	Onsite	€8,000
Zanzibar	Week 16, 2027 25 – 29 April 2027	5 Days	Onsite	€6,000
Prague	Week 18, 2027 3 – 7 May 2027	5 Days	Onsite	€7,500
Frankfurt	Week 18, 2027 3 – 7 May 2027	5 Days	Onsite	€6,500
Bangkok	Week 18, 2027 9 – 13 May 2027	5 Days	Onsite	€8,000
Lisbon	Week 19, 2027 10 – 14 May 2027	5 Days	Onsite	€6,500
Amman	Week 20, 2027 23 – 27 May 2027	5 Days	Onsite	€6,000
Singapore	Week 22, 2027 31 May – 4 June 2027	5 Days	Onsite	€6,500
Riyadh	Week 23, 2027 13 – 17 June 2027	5 Days	Onsite	€7,500
London	Week 25, 2027 21 – 25 June 2027	5 Days	Onsite	€6,500
Nairobi	Week 27, 2027 11 – 15 July 2027	5 Days	Onsite	€6,000
Montreux	Week 28, 2027 12 – 16 July 2027	5 Days	Onsite	€8,000
Bali	Week 28, 2027 18 – 22 July 2027	5 Days	Onsite	€6,500

Frequently asked questions

What does this course cover?

OverviewIndustrial organizations face increasing pressure to prevent unplanned downtime, extend equipment life, and extract measurable value from physical assets. The Predictive Maintenance and Asset Performance Management Training provides structured methods to transition maintenance operations from reactive firefighting to predictive and prescriptive st…

Are training dates available?

Yes. Available dates and destinations are listed in the course dates section on this page.

How can I register?

Choose an available date on this page and complete the registration form, or send a programme enquiry.

Can I download the course brochure?

Yes. Use the brochure download link provided on this page.

Predictive Maintenance and Asset Performance Training