A review on machinery diagnostics and prognostics implementing condition-based maintenance

This chapter introduces the subject of statistical pattern recognition (SPR). It starts by considering how features are defined and emphasizes that the nearest neighbor algorithm achieves error rates comparable with those of an ideal Bayes’ classifier. The concepts of an optimal number of features, representativeness of the training data, and the need to avoid overfitting to the training data are stressed. The chapter shows that methods such as the support vector machine and artificial neural networks are subject to these same training limitations, although each has its advantages. For neural networks, the multilayer perceptron architecture and back-propagation algorithm are described. The chapter distinguishes between supervised and unsupervised learning, demonstrating the advantages of the latter and showing how methods such as clustering and principal components analysis fit into the SPR framework. The chapter also defines the receiver operating characteristic, which allows an optimum balance between false positives and false negatives to be achieved.

Statistical Pattern Recognition

In this paper the authors derive a higher-order shear deformation theory for modeling functionally graded plates accounting for extensibility in the thickness direction. The explicit governing equations and boundary conditions are obtained using the principle of virtual displacements under Carrera’s Unified Formulation. The static and eigenproblems are solved by collocation with radial basis functions. The efficiency of the present approach is assessed with numerical results including deflection, stresses, free vibration, and buckling of functionally graded isotropic plates and functionally graded sandwich plates.

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Static, free vibration and buckling analysis of isotropic and sandwich functionally graded plates using a quasi-3D higher-order shear deformation theory and a meshless technique

Characterization and analysis of delamination fracture in composites: An overview of developments from 1990 to 2001

This paper addresses a data-driven prognostics method for the estimation of the Remaining Useful Life (RUL) and the associated confidence value of bearings The proposed method is based on the utilization of the Wavelet Packet Decomposition (WPD) technique, and the Mixture of Gaussians Hidden Markov Models (MoG-HMM) The method relies on two phases: an off-line phase, and an on-line phase During the first phase, the raw data provided by the sensors are first processed to extract features in the form of WPD coefficients The extracted features are then fed to dedicated learning algorithms to estimate the parameters of a corresponding MoG-HMM, which best fits the degradation phenomenon The generated model is exploited during the second phase to continuously assess the current health state of the physical component, and to estimate its RUL value with the associated confidence The developed method is tested on benchmark data taken from the “NASA prognostics data repository” related to several experiments of failures on bearings done under different operating conditions Furthermore, the method is compared to traditional time-feature prognostics and simulation results are given at the end of the paper The results of the developed prognostics method, particularly the estimation of the RUL, can help improving the availability, reliability, and security while reducing the maintenance costs Indeed, the RUL and associated confidence value are relevant information which can be used to take appropriate maintenance and exploitation decisions In practice, this information may help the maintainers to prepare the necessary material and human resources before the occurrence of a failure Thus, the traditional maintenance policies involving corrective and preventive maintenance can be replaced by condition based maintenance

/pdf/a-data-driven-failure-prognostics-method-based-on-mixture-of-49ddd6mw8p.pdf

A Data-Driven Failure Prognostics Method Based on Mixture of Gaussians Hidden Markov Models

A general theory for laminated plates with delaminations

A generalized multilength scale nonlinear composite plate theory with delamination

A three-dimensional, higher-order, elasticity-based micromechanics model

https://paginas.fe.up.pt/~iccs16/CD/481-520/515ToddWilliams.pdf

A new theoretical framework for the formulation of general, nonlinear, multiscale plate theories

Funding for the Los Alamos Damage Prognosis Initiative is being provided by the Department of
Energy through Laboratory Directed Research Development. In addition to the authors, the
Damage Prognosis research team includes Los Alamos staff members Matt Bement, Irene
Beyerlein, Norm Hunter, Cheng Liu, Brett Nadler, and Jeni Wait. Los Alamos graduate research
assistants Tim Fasel, Jan Goethals and Trevor Tippetts, and. Professor Dan Inman and graduate
student David Allen at Virginia Tech.

/pdf/a-coupled-approach-to-developing-damage-prognosis-solutions-14omay23yv.pdf

Todd O. Williams

Papers

A general theory for laminated plates with delaminations

A generalized multilength scale nonlinear composite plate theory with delamination

A three-dimensional, higher-order, elasticity-based micromechanics model

A new theoretical framework for the formulation of general, nonlinear, multiscale plate theories

A Coupled Approach to Developing Damage Prognosis Solutions