Machinery health prognostics: A systematic review from data acquisition to RUL prediction

http://relialab.org/Upload/files/RESS2013%2c%20vol_130%2c%20pp_175%e2%80%93189.pdf

Inverse Gaussian process models for degradation analysis: A Bayesian perspective

Degradation observations of modern engineering systems, such as manufacturing systems, turbine engines, and high-speed trains, often demonstrate various patterns of time-varying degradation rates. General degradation process models are mainly introduced for constant degradation rates, which cannot be used for time-varying situations. Moreover, the issue of sparse degradation observations and the problem of evolving degradation observations both are practical challenges for the degradation analysis of modern engineering systems. In this paper, parametric inverse Gaussian process models are proposed to model degradation processes with constant, monotonic, and S-shaped degradation rates, where physical meaning of model parameters for time-varying degradation rates is highlighted. Random effects are incorporated into the degradation process models to model the unit-to-unit variability within product population. A general Bayesian framework is extended to deal with the degradation analysis of sparse degradation observations and evolving observations. An illustrative example derived from the reliability analysis of a heavy-duty machine tool's spindle system is presented, which is characterized as the degradation analysis of sparse degradation observations and evolving observations under time-varying degradation rates.

Bayesian Degradation Analysis With Inverse Gaussian Process Models Under Time-Varying Degradation Rates

A review on pipeline integrity management utilizing in-line inspection data

Modern engineering systems are generally composed of multicomponents and are characterized as multifunctional. Condition monitoring and health management of these systems often confronts the difficulty of degradation analysis with multiple performance characteristics. Degradation observations generally exhibit an $s$ -dependent nature and sometimes experience incomplete measurements. These issues necessitate investigating multiple $s$ -dependent degradations analysis with incomplete observations. In this paper, a new type of bivariate degradation model based on inverse Gaussian processes and copulas is proposed. A two-stage Bayesian method is introduced to implement parameter estimation for the bivariate degradation model by treating the degradation processes and copula function separately. Degradation inferences for missing observation points, and for future observation points are investigated. A simulation study is presented to study the effectiveness of the dependence modeling and degradation inference of the proposed method. For demonstration, a bivariate degradation analysis of positioning accuracy and output power of heavy machine tools subject to incomplete measurements is provided.

Bivariate Analysis of Incomplete Degradation Observations Based on Inverse Gaussian Processes and Copulas

Inverse Gaussian process-based corrosion growth model for energy pipelines considering the sizing error in inspection data

This paper describes an inverse Gaussian process-based model to characterize the growth of metal-loss corrosion defects on energy pipelines. The model parameters are evaluated using the Bayesian methodology by combining the inspection data obtained from multiple inspections with the prior distributions. The Markov Chain Monte Carlo (MCMC) simulation techniques are employed to numerically evaluate the posterior marginal distribution of each individual parameter. The measurement errors associated with the ILI tools are considered in the Bayesian inference. The application of the growth model is illustrated using an example involving real inspection data collected from an in-service pipeline in Alberta, Canada. The results indicate that the model in general can predict the growth of corrosion defects reasonably well. Parametric analyses associated with the growth model as well as reliability assessment of the pipeline based on the growth model are also included in the example. The proposed model can be used to facilitate the development and application of reliability-based pipeline corrosion management.

Hao Qin

Papers

Inverse Gaussian process-based corrosion growth model for energy pipelines considering the sizing error in inspection data

Inverse Gaussian process-based corrosion growth modeling and its application in the reliability analysis for energy pipelines

Bayesian inferences of generation and growth of corrosion defects on energy pipelines based on imperfect inspection data

System fragility analysis of roof cladding and trusses for Australian contemporary housing subjected to wind uplift

Construction Defects and Wind Fragility Assessment for Metal Roof Failure: a Bayesian Approach