Weiwen Peng

Probabilistic Physics of Failure-based framework for fatigue life prediction of aircraft gas turbine discs under uncertainty

A probabilistic Physics of Failure-based framework for fatigue life prediction of aircraft gas turbine discs operating under uncertainty is developed. The framework incorporates the overall uncertainties appearing in a structural integrity assessment. A comprehensive uncertainty quantification (UQ) procedure is presented to quantify multiple types of uncertainty using multiplicative and additive UQ methods. In addition, the factors that contribute the most to the resulting output uncertainty are investigated and identified for uncertainty reduction in decision-making. A high prediction accuracy of the proposed framework is validated through a comparison of model predictions to the experimental results of GH4133 superalloy and full-scale tests of aero engine high-pressure turbine discs.

Inverse Gaussian process models for degradation analysis: A Bayesian perspective

This paper conducts a Bayesian analysis of inverse Gaussian process models for degradation modeling and inference. Novel features of the Bayesian analysis are the natural manners for incorporating subjective information, pooling of random effects information among product population, and a straightforward way of coping with evolving data sets for on-line prediction. A general Bayesian framework is proposed for degradation analysis with inverse Gaussian process models. A simple inverse Gaussian process model and three inverse Gaussian process models with random effects are investigated using Bayesian method. In addition, a comprehensive sensitivity analysis of prior distributions and sample sizes is carried out through simulation. Finally, a classic example is presented to demonstrate the applicability of the Bayesian method for degradation analysis with the inverse Gaussian process models.

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

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.

Mean stress effect correction in strain energy-based fatigue life prediction of metals

A new mean stress corrected strain energy model is proposed for fatigue life prediction of metals. Specifically, a mean stress sensitivity parameter is incorporated into modify the dissipated strain energy by introducing two mean stress correction factors. The prediction accuracy of the proposed model is compared with those of Walker, Smith–Watson–Topper, Morrow, and generalized damage parameter models by using 13 experimental data sets. All data points for each material are, respectively, fitted into a single mean stress corrected strain energy-life curve. More accurate predictions are achieved by the proposed model for all data sets with lower model prediction errors than others.

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

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 tools spindle system is presented, which is characterized as the degradation analysis of sparse degradation observations and evolving observations under time-varying degradation rates.

A Bayesian Approach for System Reliability Analysis With Multilevel Pass-Fail, Lifetime and Degradation Data Sets

Reliability analysis of complex systems is a critical issue in reliability engineering. Motivated by practical needs, this paper investigates a Bayesian approach for system reliability assessment and prediction with multilevel heterogeneous data sets. Two major imperatives have been handled in the proposed approach, which provides a comprehensive Bayesian framework for the integration of multilevel heterogeneous data sets. In particular, the pass-fail data, lifetime data, and degradation data at different system levels are combined coherently for system reliability analysis. This approach goes beyond the alternatives that deal with solely multilevel pass-fail or lifetime data, and presents a more practical tool for real engineering applications. In addition, the indices for reliability assessment and prediction are constructed coherently within the proposed Bayesian framework. It gives rise to a natural manner of incorporating this approach into a decision-making procedure for system operation and management. The effectiveness of the proposed approach is illustrated with reliability analysis of a navigation satellite.

Reliability assessment of complex electromechanical systems under epistemic uncertainty

The appearance of macro-engineering and mega-project have led to the increasing complexity of modern electromechanical systems (EMSs). The complexity of the system structure and failure mechanism makes it more difficult for reliability assessment of these systems. Uncertainty, dynamic and nonlinearity characteristics always exist in engineering systems due to the complexity introduced by the changing environments, lack of data and random interference. This paper presents a comprehensive study on the reliability assessment of complex systems. In view of the dynamic characteristics within the system, it makes use of the advantages of the dynamic fault tree (DFT) for characterizing system behaviors. The lifetime of system units can be expressed as bounded closed intervals by incorporating field failures, test data and design expertize. Then the coefficient of variation (COV) method is employed to estimate the parameters of life distributions. An extended probability-box (P-Box) is proposed to convey the present of epistemic uncertainty induced by the incomplete information about the data. By mapping the DFT into an equivalent Bayesian network (BN), relevant reliability parameters and indexes have been calculated. Furthermore, the Monte Carlo (MC) simulation method is utilized to compute the DFT model with consideration of system replacement policy. The results show that this integrated approach is more flexible and effective for assessing the reliability of complex dynamic systems.

Life cycle reliability assessment of new products—A Bayesian model updating approach

Abstract The rapidly increasing pace and continuously evolving reliability requirements of new products have made life cycle reliability assessment of new products an imperative yet difficult work. While much work has been done to separately estimate reliability of new products in specific stages, a gap exists in carrying out life cycle reliability assessment throughout all life cycle stages. We present a Bayesian model updating approach (BMUA) for life cycle reliability assessment of new products. Novel features of this approach are the development of Bayesian information toolkits by separately including “reliability improvement factor” and “information fusion factor”, which allow the integration of subjective information in a specific life cycle stage and the transition of integrated information between adjacent life cycle stages. They lead to the unique characteristics of the BMUA in which information generated throughout life cycle stages are integrated coherently. To illustrate the approach, an application to the life cycle reliability assessment of a newly developed Gantry Machining Center is shown.

Reliability of complex systems under dynamic conditions: A Bayesian multivariate degradation perspective

Degradation analysis is critical to reliability assessment and operational management of complex systems. Two types of assumptions are often adopted for degradation analysis: (1) single degradation indicator and (2) constant external factors. However, modern complex systems are generally characterized as multiple functional and suffered from multiple failure modes due to dynamic operating conditions. In this paper, Bayesian degradation analysis of complex systems with multiple degradation indicators under dynamic conditions is investigated. Three practical engineering-driven issues are addressed: (1) to model various combinations of degradation indicators, a generalized multivariate hybrid degradation process model is proposed, which subsumes both monotonic and non-monotonic degradation processes models as special cases, (2) to study effects of external factors, two types of dynamic covariates are incorporated jointly, which include both environmental conditions and operating profiles, and (3) to facilitate degradation based reliability analysis, a serial of Bayesian strategy is constructed, which covers parameter estimation, factor-related degradation prediction, and unit-specific remaining useful life assessment. Finally, degradation analysis of a type of heavy machine tools is presented to demonstrate the application and performance of the proposed method. A comparison of the proposed model with a traditional model is studied as well in the example.

Reliability analysis of direct drive electrohydraulic servo valves based on a wear degradation process and individual differences

Electrohydraulic servo valves play critical roles in modern servo control systems, which require high reliability and high safety. The reliability analysis of a direct drive electrohydraulic servo valve is conducted in this article. First, the failure mechanism of the direct drive electrohydraulic servo valve is investigated by analyzing the structure and the working principle of the direct drive electrohydraulic servo valve. It shows that clamping stagnation, internal leakages and spring fatigue are the main failure modes of direct drive electrohydraulic servo valve. The structure degradation caused by wear enlarges the clearance and results in the increase in null leakages. Then, a gamma process is adopted to describe the internal structure degradation based on the failure mechanism analysis. Heterogeneity among different samples of direct drive electrohydraulic servo valves is studied and handled by introducing unit-specific random effects into the gamma process degradation model. Additionally, in this article, a Bayesian method is used to facilitate the degradation analysis and reliability estimation. The reliability models of sealing, springs and spool valves are presented. Finally, a brief introduction of the experiment of the direct drive electrohydraulic servo valves and an illustrative example of reliability analysis are presented to demonstrate the introduced failure mechanism analysis and the proposed reliability analysis method for direct drive electrohydraulic servo valves.