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The desire and need for accurate diagnostic and real predictive prognostic capabilities have been around for as long as human beings have operated complex and expensive machinery. This has been true for both mechanical and electronic systems. There has been a long history of trying to develop and implement various degrees of diagnostic and prognostic capabilities. Recently, stringent advanced diagnostic, prognostics and health management (PHM) capability requirements have begun to be placed on some of the more sophisticated new applications. A major motivation for specifying more advanced diagnostic and prognostic requirements is the realization that they are needed to fully enable and reap the benefits of new and revolutionary logistic support concepts. These logistic support concepts are called by many names and include condition-based maintenance (CBM), performance-based logistics (PBL), and autonomic logistics all of which include PHM capabilities as a key enabler. The area of intelligent maintenance and diagnostic and prognostic–enabled CBM of machinery is a vital one for today’s complex systems in industry, aerospace vehicles, military and merchant ships, the automotive industry, and elsewhere. The industrial and military communities are concerned about critical system and component reliability and availability. The goals are both to maximize equipment up time and to minimize maintenance and operating costs. As manning levels are reduced and equipment becomes more complex, intelligent maintenance schemes must replace the old prescheduled and labor intensive planned maintenance systems to ensure that equipment continues to function. Increased demands on machinery place growing importance on keeping all equipment in service to accommodate mission-critical usage.

Upon successful completion of this course, the delegates will be able to: 

  • Acquire knowledge and apply methods on intelligent fault diagnosis and prognosis for engineering systems
  • Discuss historical perspective, system requirements, design and functional layers of fault diagnostic and prognostic systems.
  • Recognize the system approach to CBM/PHM that includes trade studies, FMECA, system CBM test-plan design, performance assessment, impact on maintenance and operations and control and contingency management.
  • Implement sensors and sensing strategies, signal processing and database management systems
  • Apply fault diagnosis procedures and methods as well as fault prognosis performance metrics
  • Explain logistics as the support of the system in operation.

Day 1

Introduction

  • Historical Perspective
  • Diagnostic and Prognostic System Requirements
  • Designing in Fault Diagnostic and Prognostic Systems
  • Diagnostic and Prognostic Functional Layers

Systems Approach to CBM/PHM

  • Trade Studies
  • Failure Modes and Effects Criticality Analysis (FMECA)
  • System CBM Test-Plan Design
  • Performance Assessment
  • CBM/PHM Impact on Maintenance and Operations: Case Studies
  • CBM/PHM in Control and Contingency Management

Day 2

Sensors and Sensing Strategies

  • Sensors
  • Sensor Placement
  • Wireless Sensor Networks
  • Smart Sensors

Signal Processing and Database Management Systems

  • Signal Processing in CBM/PHM
  • Signal Preprocessing
  • Signal Processing
  • Vibration Monitoring and Data Analysis
  • Real-Time Image Feature Extraction and Defect/Fault Classification
  • The Virtual Sensor
  • Fusion or Integration Technologies
  • Usage-Pattern Tracking
  • Database Management Methods

Day 3

Fault Diagnosis

  • The Diagnostic Framework
  • Historical Data Diagnostic Methods
  • Data-Driven Fault Classification and Decision Making
  • Dynamic Systems Modeling
  • Physical Model–Based Methods
  • Model-Based Reasoning
  • Case-Based Reasoning (CBR)
  • Methods for Fault Diagnosis
  • A Diagnostic Framework for Electrical/Electronic Systems
  • Case Study: Vibration-Based Fault Detection and Diagnosis for Engine Bearings

Fault Prognosis

  • Introduction
  • Model-Based Prognosis Techniques
  • Probability-Based Prognosis Techniques
  • Data-Driven Prediction Techniques
  • Case Studies

Day 4

Fault Diagnosis and Prognosis Performance Metrics

  • Introduction
  • CBM/PHM Requirements Definition
  • Feature-Evaluation Metrics
  • Fault Diagnosis Performance Metrics
  • Prognosis Performance Metrics
  • Diagnosis and Prognosis Effectiveness Metrics
  • Complexity/Cost-Benefit Analysis of CBM/PHM Systems

Day 5

Logistics: Support of the System in Operation

  • Product-Support Architecture, Knowledge Base, and Methods for CBM
  • Product Support without CBM
  • Product Support with CBM
  • Maintenance Scheduling Strategies
  • A Simple Example
  • Course Methodology

This course is intended for all electrical, mechanical and industrial engineers. Also beneficial for those who are dealing with computer engineering and business management.

Course Schedules

  • 5 Days - Nov 2, 2026
  • english
  • face to face
  • Dubai - UAE
  • $ 4,500
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