Predictive and Preventive Maintenance Technologies

Upcoming Schedules

Date	Venue	Duration	Fee (USD)
03 Feb - 12 Feb 2025	Live Online	10 Day	5250
03 Mar - 12 Mar 2025	Live Online	10 Day	5250
21 Apr - 30 Apr 2025	Live Online	10 Day	5250
23 Jun - 02 Jul 2025	Live Online	10 Day	5250
14 Jul - 23 Jul 2025	Live Online	10 Day	5250
25 Aug - 03 Sep 2025	Live Online	10 Day	5250
03 Nov - 12 Nov 2025	Live Online	10 Day	5250
22 Dec - 31 Dec 2025	Live Online	10 Day	5250

Course Overview

In today’s competitive industrial landscape, unplanned equipment failures can result in catastrophic production losses, safety incidents, and costly downtime. According to a 2023 study by Deloitte, unplanned outages cost manufacturers up to $260,000 per hour, while McKinsey & Company reports that predictive maintenance strategies can reduce maintenance costs by 20-30% and downtime by 45%. Predictive and Preventive Maintenance Technologies is a comprehensive course that combines Predictive Maintenance Strategies with Technologies for Predictive Maintenance, empowering maintenance professionals to transition from reactive to proactive asset management.
This course integrates Industry 4.0 technologies with traditional maintenance methodologies to optimize equipment reliability. Key highlights include:
Predictive Analytics: Leverage vibration analysis, thermography, and ultrasonic testing to detect early failure signs.
Preventive Scheduling: Implement CMMS (Computerized Maintenance Management Systems) for optimized maintenance workflows.
Condition Monitoring: Master Infrared (IR) imaging, oil analysis (tribology), and motor circuit testing for real-time asset health assessment.
Reliability-Centered Maintenance (RCM): Align maintenance strategies with FMECA (Failure Modes, Effects, and Criticality Analysis).
ROI Optimization: Calculate cost-benefit ratios for predictive vs. preventive maintenance programs.
Designed for maintenance managers, reliability engineers, and plant supervisors, this course bridges the gap between theoretical maintenance principles and data-driven decision-making, ensuring compliance with ISO 55000 (Asset Management) and SAE JA1011 (RCM Guidelines).

Key Takeaways:

Predictive Analytics: Leverage vibration analysis, thermography, and ultrasonic testing to detect early failure signs.
Preventive Scheduling: Implement CMMS (Computerized Maintenance Management Systems) for optimized maintenance workflows.
Condition Monitoring: Master Infrared (IR) imaging, oil analysis (tribology), and motor circuit testing for real-time asset health assessment.
Reliability-Centered Maintenance (RCM): Align maintenance strategies with FMECA (Failure Modes, Effects, and Criticality Analysis).
ROI Optimization: Calculate cost-benefit ratios for predictive vs. preventive maintenance programs.

Predictive Analytics: Leverage vibration analysis, thermography, and ultrasonic testing to detect early failure signs.
Preventive Scheduling: Implement CMMS (Computerized Maintenance Management Systems) for optimized maintenance workflows.
Condition Monitoring: Master Infrared (IR) imaging, oil analysis (tribology), and motor circuit testing for real-time asset health assessment.
Reliability-Centered Maintenance (RCM): Align maintenance strategies with FMECA (Failure Modes, Effects, and Criticality Analysis).
ROI Optimization: Calculate cost-benefit ratios for predictive vs. preventive maintenance programs.

Course Objectives

By the end of this course, participants will be able to:
Differentiate between reactive, preventive, and predictive maintenance strategies.
Implement condition-based monitoring (CBM) using vibration analysis, thermography, and ultrasonic testing.
Develop a Predictive Maintenance (PdM) program with CMMS integration.
Apply Root Cause Failure Analysis (RCFA) to minimize repeat failures.
Evaluate P-F (Potential-Functional Failure) intervals for timely interventions.
Optimize maintenance budgets using Life Cycle Costing (LCC).
Utilize AI-driven predictive analytics for failure forecasting.

Personal Benefits

Participants will acquire:
Industry 4.0 Competence: Mastery of IoT-enabled predictive tools.
Career Advancement: Skills to lead reliability engineering teams.
Data Literacy: Ability to interpret machine health analytics.

Organisational Benefits

Organizations will gain:
Reduced Downtime: 30-50% fewer unplanned outages (per ARC Advisory Group).
Cost Efficiency: 20-30% lower maintenance expenditures.
Extended Asset Life: Proactive degradation management.
Regulatory Compliance: Alignment with ISO 55000 and OSHA standards.

Who Should Attend

This course is ideal for:
Maintenance Managers & Supervisors
Reliability Engineers
Plant Operations Managers
CMMS Administrators
Asset Management Professionals

Course Outline

Module 1: Foundations of Maintenance Management

Evolution from reactive to predictive strategies Maintenance cost structures (direct vs. indirect) ISO 55000 asset management framework Case study: ROI comparison across industries

Module 2: Maintenance Strategy Development

Corrective vs. preventive vs. predictive paradigms FMECA (Failure Modes, Effects, and Criticality Analysis) Maintenance task selection matrices Resource allocation optimization

Module 3: Computerized Maintenance Management Systems (CMMS)

Asset hierarchy and work order structuring Key performance indicators (MTBF, MTTR) Integration with ERP systems Data-driven decision workflows

Module 4: Vibration Analysis & Machinery Diagnostics

FFT spectrum interpretation Bearing defect frequencies Envelope detection for early failure signs ISO 10816 vibration severity standards

Module 5: Thermographic Inspection Techniques

IR camera operation principles Electrical component hotspot analysis Mechanical friction detection NFPA 70B compliance requirements

Module 6: Ultrasonic & Tribology Monitoring

Airborne ultrasound for leak detection Oil analysis (wear debris, viscosity, TBN) Particle counting techniques ASTM D7720 standard implementation

Module 7: Electrical System Predictive Maintenance

Motor circuit analysis (Megger testing) Partial discharge detection Transformer dissolved gas analysis NETA MTS-2017 standards

Module 8: Reliability-Centered Maintenance (RCM)

SAE JA1011 implementation guidelines Decision logic trees for task selection Age-related vs. random failure patterns Aviation industry case study

Module 9: P-F Interval Management

Potential vs. functional failure identification Optimal inspection frequency calculations Technology selection matrices Pump system case analysis

Module 10: Maintenance Planning & Scheduling

Work preparation best practices Resource leveling techniques Weekly/Monthly scheduling cadences Critical path method applications

Module 11: Predictive Maintenance Program Implementation

6-phase deployment roadmap Change management strategies Stakeholder buy-in techniques Pilot program design

Module 12: Maintenance Financials & ROI Analysis

Life Cycle Costing (LCC) models Cost avoidance quantification Budget justification templates Industry benchmarking (SMRP metrics)

Module 13: Emerging Industry 4.0 Technologies

IoT sensor networks for condition monitoring Machine learning failure prediction models Digital twin applications Blockchain for maintenance records

Module 14: Continuous Improvement & Audit

PdM program maturity assessment Gap analysis methodologies ISO 55002 audit preparation Sustaining program benefits

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