Aerospace Predictive Maintenance: Fundamental Concepts, written by longtime practitioner Charles E. Dibsdale based in the UK, considers PdM a subset of Condition Based Maintenance (CBM), and must obey the same underlying rules and pre-requisites that apply to it. Yet, PdM is new because it takes advantage of emerging digital technology in sensing, acquiring data, communicating the data, and processing it. This capability can autonomously analyse the data and send alerts and advice to decision makers, potentially reducing through-life cost and improving safety. Aerospace Predictive Maintenance: Fundamental Concepts provides a history of maintenance, and how performance, safety and the environment make direct demands on maintenance to deliver more for less in multiple industries. It also covers Integrated Vehicle Health Management (IVHM) that aims to provide a platformcentric framework for PdM in the mobility domain. The book discusses PdM maturity, offering a context of the transformation of data through information and knowledge. Understanding some of the precepts of knowledge management provides a really useful and powerful perspective on PdM as an information system. On the other hand, Aerospace Predictive Maintenance: Fundamental Concepts also discusses disadvantages of PdM and shows how these may be addressed. One of the fundamental changes PdM implies is a shift from deterministic black-and-white thinking to more nuanced decision making informed by probabilities and uncertainty. Other concerns such as data management, privacy and ownership are tackled as well. Aerospace Predictive Maintenance: Fundamental Concepts covers additional technologies, such as the Industrial Internet of Things (IIOT) that will result in proliferation of cheap, wireless, ultra-low-power sensors, and will transform PdM into a more economical option. The book brings in the future possibilities of nano technology, which can be used for new sensors, micro-robotics for inspections and self-healing/repairing of systems which can be intergrated with PdM.

• Cover
• CHAPTER 1 An Engineer’s Journey
  • Who Is This Book Intended For?
  • How This Book Is Organized
  • Reference
• CHAPTER 2 A History of Maintenance and How Maintenance Is Done Today
  • Most Functional Failures in Complex Machinery Are Random
  • What Is the Difference between Failure Modes and Failure Mechanisms?
  • Intrinsic and Achieved Reliability
  • Applied Systems Thinking
  • MSG-3 Overview
  • Key Take-Away Points
  • References
• CHAPTER 3 What Is Predictive Maintenance (PdM) and How Does It Fit into a Maintenance Regime?
  • What Are Functions?
  • What Are Operating Performance Levels?
  • The Major Influences of Failure
  • What Is an Operating Context?
  • What Is an Operating Environment?
  • Who Are Asset Stakeholders?
  • A Taxonomy of Maintenance Tasks
  • The Taxonomy of Maintenance
  • Preventative Maintenance
  • Deeper Explanation of PdM
  • Levels of Diagnostic Capability
  • Diagnostics
  • Diagnostic Effectiveness
  • Prognostics
    • Type 1 Models
    • Type 2 Models
    • Type 3 Models
    • Type 4 Models
  • Fallacies and Hype Surrounding PdM
    • The ‘Real Time’ Label of Superior PdM Misunderstanding
    • A Data-driven Approach Negates the Need for Engineering Domain Knowledge
    • What Is the Difference between Condition Monitoring and Condition-Based Maintenance?
  • Immature Systems Are Sold as PdM Systems
  • Wasteful Number of Inspections
  • How Does PdM Impact Maintenance Planning and Scheduling?
    • What Is a Maintenance Schedule?
    • What Is a Maintenance Plan?
  • Digital Twin
  • Key Take-Away Points
  • References
• CHAPTER 4 How Does PdM Fit with Integrated Vehicle Health Management (IVHM)?
  • What Are Maintenance Credits?
  • Key Take-Away Points
  • References
• CHAPTER 5 Why Is PdM Generally Better than Traditional Maintenance? (How to Build a Business Case)
  • Why Choose On-Condition over Scheduled Discard/Replacement or Restoration?
  • Exceptions to the Rule — When Is Scheduled Replacement Better?
  • When Should Median and Mean Measures Be Used?
  • Why Is the Use of MTBF Persisted?
  • Building the Business Case
  • Business Cases Built on Reliability–Availability–Maintainability (RAM) Simulation
  • Key Take-Away Points
  • References
• CHAPTER 6 How Does PdM Relate to Reliability-Centered Maintenance (RCM)?
  • How Can Severity of Failure Be Categorized?
  • How Can the Likelihood of Failure Be Categorized?
    • The Applicability of Weibull Analysis
    • FMEA Storage and Tools for Analysis
  • Key Take-Away Points
  • References
• CHAPTER 7 What Are the Key Features in a PdM Maturity Model?
  • Data , Information and Knowledge
  • Data Quality
  • The Breakdown of PdM into Functional Blocks
  • Sense
  • Data Sampling Rates
  • Acquire
  • Transfer
  • Analyze
  • Learn
  • People and Competencies
  • Maturity Model
  • Key Take-Away Points
  • References
• CHAPTER 8 Specifying Predictive Maintenance
  • Assumptions
  • Basic Requirements
  • Key Take-Away Points
  • Reference
• CHAPTER 9 What Are the Disadvantages of PdM and How Should They Be Addressed?
  • Daniel Kahneman: Thinking Fast, Thinking Slow
  • Nassim Nicholas Taleb: Fooled by Randomness
  • The Resnikoff Conundrum
  • Key Take-Away Points
  • References
• CHAPTER 10 How PdM Will Likely Transform with the Emergence of New Technology
  • Big Data and Cloud Services
  • The Emergence of the Industrial Internet of Things (IIoT)
  • Industry 4.0
  • Nanotechnology
  • Configuration Management
  • The Advent of the Citizen Data-Scientist
    • The Apache Software
    • Python
    • Other Open-Source Capabilities
  • Key Take-Away Points
  • References
• CHAPTER 11 A Summary, Future States, and Things to Look For
  • How Do You Start Implementing PdM?
  • PdM Analogies
  • Key Take-Away Points
• CHAPTER 12 An Example PdM Case Study Using Open-Source Development Tools
  • Plots
  • Summary Notes
  • Code Notes
  • Reference
• Glossary
• Author Bio