Linkan Bian

Stochastic modeling and real-time prognostics for multi-component systems with degradation rate interactions

Many conventional models that characterize the reliability of multi-component systems are developed on the premise that component failures in a system are independent. By contrast, this article offers a unique perspective on modeling component interdependencies and predicting their residual lifetimes. Specifically, the article provides a stochastic modeling framework for characterizing interactions among the degradation processes of interdependent components of a given system. This is achieved by modeling the behaviors of condition-/degradation-based sensor signals that are associated with each component. The proposed model is also used to estimate the residual lifetime distributions of each component. In addition, a Bayesian framework is used to update the predicted residual lifetime distributions using sensor signals that are correlated with the real-time dynamics associated with the interactions. The robustness and prediction accuracy of the methodology are investigated through a comprehensive simulation study that compares the performance of the proposed model to a counterpart benchmark that does not account for degradation interactions.

Computing and updating the first-passage time distribution for randomly evolving degradation signals

This article considers systems that degrade gradually and whose degradation can be monitored using sensor technology. Different degradation modeling techniques, such as the Brownian motion process, gamma process, and random coefficients models, have been used to model the evolution of sensor-based degradation signals with the goal of estimating lifetime distributions of various engineering systems. A parametric stochastic degradation modeling approach to estimate the Residual Life Distributions (RLDs) of systems/components that are operating in the field is presented. The proposed methodology rests on the idea of utilizing in situ degradations signals, communicated from fielded components, to update their respective RLDs in real time. Given the observed partial degradation signals, RLDs are evaluated based on a first-passage time approach. Expressions for the first-passage time for a base case linear degradation model, in which the degradation signal evolves as a Brownian motion, are derived. The model is tested using simulated and real-world degradation signals from a rotating machinery application.

Designing a reliable bio-fuel supply chain network considering link failure probabilities

Presents a pre-disaster planning model to strengthen the links between the multi-modal facilities.Failure probability of links between the multi-modal facilities is estimated using a spatial static model, which developed from real world data.Conduct a case study of biofuel supply chain with data from Mississippi and Alabama.The model saves

Degradation modeling for real-time estimation of residual lifetimes in dynamic environments

0.27/gallon when a disaster happens. This study presents a pre-disaster planning model that seeks to strengthen a bio-fuel supply chain systems multi-modal facility links while accounting for limited budget availability. The model presented here determines which set of facilities and links to select that will maximize post-disaster connectivity and minimize bio-fuel supply chain related costs. The failure probability of the links between the multi-modal facilities is estimated using a spatial statistic model, which is developed from real world data. This paper develops a generalized Benders decomposition algorithm to solve this challenging NP -hard problem. The proposed algorithm is validated via a real-world case study with data from Mississippi and Alabama. Computational results show that the proposed solution approach is capable of solving the problem efficiently. Several experiments are conducted to demonstrate the applicability of this model by testing various model parameters on bio-fuel supply chain network performance, including reliability improvement cost, availability of budget, biomass supply changes, and the risk averseness degree for decision makers. Numerical analysis indicates that, under normal conditions, the minimum cost model determines a unit bio-fuel delivery cost of

Accelerated process optimization for laser-based additive manufacturing by leveraging similar prior studies

3.56/gallon. However, in case of a disaster, the unit bio-fuel delivery cost provided by the minimum cost model increases to

Stochastic methodology for prognostics under continuously varying environmental profiles

3.96/gallon, compared to

Mechanical and Microstructural Properties of Selective Laser Melted 17-4 PH Stainless Steel

3.69/gallon provided by the reliable model solution.

Modeling, Simulation and Experimental Validation of Heat Transfer During Selective Laser Melting

This article presents a methodology for modeling degradation signals from components functioning under dynamically evolving environment conditions. In situ sensor signals related to the degradation process are utilized as well as the environment conditions, to predict and update, in real-time, the distribution of a component’s residual lifetime. The model assumes that the time-dependent rate at which a component’s degradation signal increases (or decreases) is affected by the severity of the current environmental or operational conditions. These conditions are assumed to evolve as a continuous-time Markov chain. Unique to the proposed model is the union of historical data with real-time, sensor-based data to update the signal parameters, environment parameters, and the residual lifetime distribution of the component within a Bayesian framework.

Mechanical properties and microstructural characterization of selective laser melted 17-4 PH stainless steel

ABSTRACT Manufacturing parts with target properties and quality in Laser-Based Additive Manufacturing (LBAM) is crucial toward enhancing the “trustworthiness” of this emerging technology and pushing it into the mainstream. Most of the existing LBAM studies do not use a systematic approach to optimize process parameters (e.g., laser power, laser velocity, layer thickness, etc.) for desired part properties. We propose a novel process optimization method that directly utilizes experimental data from previous studies as the initial experimental data to guide the sequential optimization experiments of the current study. This serves to reduce the total number of time- and cost-intensive experiments needed. We verify our method and test its performance via comprehensive simulation studies that test various types of prior data. The results show that our method significantly reduces the number of optimization experiments, compared with conventional optimization methods. We also conduct a real-world case study that optimizes the relative density of parts manufactured using a Selective Laser Melting system. A combination of optimal process parameters is achieved within five experiments.

Residual Life Prediction of Multistage Manufacturing Processes With Interaction Between Tool Wear and Product Quality Degradation

We present a stochastic modeling framework for sensor-based degradation signals that predicts, in real time, the residual lifetime of individual components subjected to a time-varying environment. We investigate the future environmental profile that is deterministic and evolves continuously. Unique to our model is the union of historical data with real-time sensor-based data to update the degradation model and the residual life distribution (RLD) of the component within a Bayesian framework. The performance of our model is evaluated on the basis of degradation signals from both numerical experiments and a case study using real bearing data. The results show that our approach accurately estimates the RLD by incorporating the environmental effects and utilizing the real-time observations.