Distributed Damage Estimation for Prognostics based on Structural Model Decomposition

Model-based prognostics approaches capture system knowledge in the form of physics-based models of components, and how they fail. These methods consist of a damage estimation phase, in which the health state of a component is estimated, and a prediction phase, in which the health state is projected forward in time to determine end of life. However, the damage estimation problem is often multi-dimensional and computationally intensive. We propose a model decomposition approach adapted from the diagnosis community, called possible conflicts, in order to both improve the computational efficiency of damage estimation, and formulate a damage estimation approach that is inherently distributed. Local state estimates are combined into a global state estimate from which prediction is performed. Using a centrifugal pump as a case study, we perform a number of simulation-based experiments to demonstrate the approach.

[1]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[2]  Jeffrey K. Uhlmann,et al.  New extension of the Kalman filter to nonlinear systems , 1997, Defense, Security, and Sensing.

[3]  Gautam Biswas,et al.  Factoring Dynamic Bayesian Networks based on structural observability , 2009, Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference.

[4]  M. Staroswiecki,et al.  ANALYTICAL REDUNDANCY IN NON LINEAR INTERCONNECTED SYSTEMS BY MEANS OF STRUCTURAL ANALYSIS , 1989 .

[5]  Simon J. Godsill,et al.  On sequential Monte Carlo sampling methods for Bayesian filtering , 2000, Stat. Comput..

[6]  Kai Goebel,et al.  Modeling Li-ion Battery Capacity Depletion in a Particle Filtering Framework , 2009 .

[7]  Gautam Biswas,et al.  Factoring Dynamic Bayesian Networks using Possible Conflicts , 2010 .

[8]  K. Goebel,et al.  Improving Computational Efficiency of Prediction in Model-Based Prognostics Using the Unscented Transform , 2010 .

[9]  Sankalita Saha,et al.  Metrics for Offline Evaluation of Prognostic Performance , 2021, International Journal of Prognostics and Health Management.

[10]  Carsten Skovmose Kallesøe,et al.  Fault Detection and Isolation in Centrifugal Pumps , 2005 .

[11]  G. Biswas,et al.  A Hierarchical Model-based approach to Systems Health Management , 2007, 2007 IEEE Aerospace Conference.

[12]  K. Goebel,et al.  Multiple damage progression paths in model-based prognostics , 2011, 2011 Aerospace Conference.

[13]  片山 徹.  Subspace methods for system identification , 2005 .

[14]  Matthew Daigle,et al.  Model-based prognostics under limited sensing , 2010, 2010 IEEE Aerospace Conference.

[15]  Brian C. Williams,et al.  Decompositional, Model-based Learning and its Analogy to Diagnosis , 1998, AAAI/IAAI.

[16]  I. Hutchings.  Tribology: Friction and Wear of Engineering Materials , 1992 .

[17]  Carlos Alonso González,et al.  Possible conflicts: a compilation technique for consistency-based diagnosis , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[18]  Gautam Biswas,et al.  Ef fi cient on-line parameter estimation in TRANSCEND for nonlinear systems , 2009 .

[19]  Michel Verhaegen,et al.  Identifying MIMO Wiener systems using subspace model identification methods , 1996, Signal Process..

[20]  Sankalita Saha,et al.  A Distributed Prognostic Health Management Architecture , 2009 .

[21]  Gautam Biswas,et al.  Generating Possible Conflicts From Bond Graphs Using Temporal Causal Graphs , 2009, ECMS.

[22]  Krishna R. Pattipati,et al.  Model-Based Prognostic Techniques Applied to a Suspension System , 2008, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[23]  B. Moor,et al.  Subspace identification for linear systems , 1996 .