Prognostics and Health Management

A Practical Approach to Improving System Reliability Using Condition-Based Data

Douglas Goodman, James P. Hofmeister, Ferenc Szidarovszky

• Publisher: Wiley
• Year: 2019
• Print ISBN: 9781119356653
• eBook ISBN: 9781119356707
• Edition: 1
• Topic: Technology & Engineering
• Subtopic: Quality Control in Engineering
• Index: Technology & Engineering

A comprehensive guide to the application and processing of condition-based data to produce prognostic estimates of functional health and life.

Prognostics and Health Management provides an authoritative guide for an understanding of the rationale and methodologies of a practical approach for improving system reliability using conditioned-based data (CBD) to the monitoring and management of health of systems. This proven approach uses electronic signatures extracted from conditioned-based electrical signals, including those representing physical components, and employs processing methods that include data fusion and transformation, domain transformation, and normalization, canonicalization and signal-level translation to support the determination of predictive diagnostics and prognostics.

Written by noted experts in the field, Prognostics and Health Management clearly describes how to extract signatures from conditioned-based data using conditioning methods such as data fusion and transformation, domain transformation, data type transformation and indirect and differential comparison. This important resource:

• Integrates data collecting, mathematical modelling and reliability prediction in one volume
• Contains numerical examples and problems with solutions that help with an understanding of the algorithmic elements and processes
• Presents information from a panel of experts on the topic
• Follows prognostics based on statistical modelling, reliability modelling and usage modelling methods

Written for system engineers working in critical process industries and automotive and aerospace designers, Prognostics and Health Management offers a guide to the application of condition-based data to produce signatures for input to predictive algorithms to produce prognostic estimates of functional health and life.

1
Introduction to Prognostics

1.1 What Is Prognostics?

Prognostics is predictive diagnostics and provides the state of degraded health and makes an accurate prediction of when a resulting future failure in the system is likely to occur. The purpose of prognostics is to detect degradation and create predictive information such as estimates of state of health (SoH) and remaining useful life (RUL) for systems. Doing so yields the following benefits: (i) provides advance warning of failures; (ii) minimizes unscheduled maintenance; (iii) predicts the time to perform preventive replacement; (iv) increases maintenance cycles and operational readiness; (v) reduces system sustainment costs by decreasing inspection, inventory, and downtime costs; and (vi) increases reliability by improving the design and logistic support of existing systems (Pecht 2008; Kumar and Pecht 2010; O'Connor and Kleyner 2012).

Prognostics, as defined and used in this book, includes data acquisition (DA) and data manipulation (DM) by sensors (S) and processing within a sensor framework; DA, DM, and state detection (SD) employing processing and computational routines within a feature‐vector framework to produce feature data (FD) consisting of condition indicators that are leading indicators of failure (signatures); and health assessment (HA) and progno...

Table of contents

• Cover
• Table of Contents
• List of Figures
• Series Editor's Foreword
• Preface
• Acknowledgments
• 1 Introduction to Prognostics
• 2 Approaches for Prognosis and Health Management/Monitoring (PHM)
• 3 Failure Progression Signatures
• 4 Heuristic‐Based Approach to Modeling CBD Signatures
• 5 Non‐Ideal Data: Effects and Conditioning
• 6 Design: Robust Prototype of an Exemplary PHM System
• 7 Prognostic Enabling: Selection, Evaluation, and Other Considerations
• Index
• End User License Agreement