Prognostics and Health Management of Engineering Systems -- An Introduction

by Nam-Ho Kim, Dawn An and Joo-Ho Choi

This book introduces methods for predicting the future behavior of a system's health and the remaining useful life to determine an appropriate maintenance schedule. The authors introduce the history, industrial applications, algorithms, and benefits and challenges of PHM (Prognostics and Health Management) to help readers understand this highly interdisciplinary engineering approach that incorporates sensing technologies, physics of failure, machine learning, modern statistics, and reliability engineering.

Among the many topics discussed in-depth are:

  • Prognostics tutorials using least-squares
  • Bayesian inference and parameter estimation
  • Physics-based prognostics algorithms including nonlinear least squares, Bayesian method, and particle filter
  • Data-driven prognostics algorithms including Gaussian process regression and neural network
  • Comparison of different prognostics algorithms

The authors also present several applications of prognostics in practical engineering systems, including wear in a revolute joint, fatigue crack growth in a panel, prognostics using accelerated life test data, fatigue damage in bearings, and more. Prognostics tutorials with a Matlab code using simple examples are provided, along with a companion website that presents Matlab programs for different algorithms as well as measurement data. Each chapter contains a comprehensive set of exercise problems, some of which require Matlab programs, making this an ideal book for graduate students in mechanical, civil, aerospace, electrical, and industrial engineering and engineering mechanics, as well as researchers and maintenance engineers in these fields.

Book in amazon.com or Barns and Noble

 

Contents:

 
Prefacev
1. Introduction1
2. Tutorials for Prognostics31
3. Bayesian Statistics for Prognostics83
4. Physics-based prognostics 145
5. Data-drive prognostics 201
6. Study on attributes of prognostics methods 269
7. Applications of prognostics 309
Index383

MATLAB Codes

  1. Matlab Programs. Need to change the extension ".txt" to ".m" after download.

    NLS.m Nonlinear least squares method (baseline)
    BM.m Overall Bayesian method (baseline)
    PF.m Particle filter method (baseline)
    GP.m Gaussian process method (baseline)
    NN.m Neural network method (baseline)
    POST.m Postprocessing module
    Metric.m Prognostics metrics

Prognostics Examples

  1. Particle Filter

    PF_Battery.m Battery degradation prognostics

  2. Bayesian Method

    BM_Battery.m Battery degradation prognostics
    BM_Crack.m Crack growth prognostics

  3. Nonlinear Least-Squares Method

    NLS_Battery.m Battery degradation prognostics
    NLS_Crack.m Crack growth prognostics

  4. Gaussian Process

    GP_Battery.m Battery degradation prognostics
    GP_Crack.m Crack growth prognostics

  5. Neural Network


Other codes

  1. Crack growth data

    CrackData.m Matrlab file for crack data
    CrackData Matlab mat file
    Huang.m Huang's crack growth model