Guru Prakash

Structural health monitoring provides a measurement-informed quantitative and systematic framework to better assess the state of fatigue in aging infrastructure. In this paper, a general methodology for probabilistic modeling of fatigue damage accumulation is presented from a structural health monitoring perspective, which allows for estimating the remaining fatigue life from strain measurements. The method presented uses a damage-sensitive feature derived from the Palmgren–Miner rule—a commonly used measure in fatigue analysis and design—as a surrogate for degradation and probabilistically models the process of degradation. A Bayesian approach is employed to estimate the parameters of this degradation model, and the remaining fatigue life is predicted using Markov chain Monte Carlo (MCMC) simulations. In the simplified case of constant stress amplitude, an analytical solution for the fatigue life has been derived. Moreover, techniques are presented to account for various challenges such as the lack of structural health monitoring data (SHM) for the initial unmonitored period and the absence of the continuous monitoring program. The main contribution of the paper is to develop a probabilistic degradation modeling framework using a damage-sensitive feature derived from the Miner rule for the prediction of the remaining fatigue life of critical bridge components. A numerical case study is presented to demonstrate the proposed methodology, and the major challenges of its implementation in the field are demonstrated through illustrations.

This paper presents a static deflection-based method for structural health monitoring of in-service bridges. In the past, the use of static as well as dynamic measurements has been investigated for damage detection in bridge elements. However, most of these methods are complex and work only in laboratory settings. Moreover, implementing such methods for in-service bridges is a challenging task. The method presented in this paper is based on a simple structural analysis principle and can be implemented for in-service bridges. Using the principle of virtual work, an analytical expression for deflection change (DC) (i.e. the difference in deflection for healthy and damaged beams) and a consistency function are derived. A plot of the DC curve reveals the damage location, and the consistency function is used to assess the damage severity. Numerical case studies and laboratory experiments are presented to investigate the effectiveness of the proposed method in different cases, such as a simply supported beam subjected to uniform loading and a cantilever beam subjected to uniform loading. Satisfactory damage localization and quantification results were achieved even with a small number of sensors. Both numerical and experimental analysis results show that the proposed model performs reasonably well at detecting and assessing the damage in bridge elements.

This paper presents a change-point-based Wiener process degradation model for remaining useful life estimation of a system. The advantage of using Weiner process for degradation modelling is twofold – first, degradation path needs not to be strictly monotonic, which often occurs when noise is present in the measurements or the degradation measure fluctuates itself; and secondly, a closed-form expression for failure time simplifies the Bayesian computations significantly. In this paper, a unified degradation modelling approach based on the Wiener process is presented, where both gradual degradation and sudden change due to shock events are identified in a unified formulation. The change detection in degradation path is posed as a hypothesis testing and novel test statistics based on log-likelihood function is proposed to accept or reject the null hypothesis. For a given degradation path, first, this test statistic is compared with the pre-set threshold, and an appropriate model (i.e. one or two phases) is selected. The parameters of the chosen model and remaining useful life are continuously updated, where the prior obtained from the historically available run-to-failure degradation paths is integrated with the data obtained from the in situ units in a Bayesian framework. A numerical example and a case study are presented to illustrate the proposed methodology.

This paper presents two Bayesian hierarchical models—one utilizing the life-time data and other using the structural health monitoring (SHM) data, for degradation modeling and reliability assessment of rolling element bearings. The main advantage of the proposed life-time data based model is that, it accounts for the variability in failure times caused due to the difference in material properties, initial degradation, operating and environmental conditions by introducing Bayesian hierarchy in the model parameters. On the other hand, SHM data (such as vibration and strain) based model focuses on stochastic nature of bearing degradation, and models it using a two-phase Wiener process. In this model, the point of phase-transition is the time when the damage initiates. The detection of such a point is undertaken using Bayesian change point algorithms. For both the models, the model parameters and reliability are updated as more data becomes available. In this manner, the prior domain knowledge and life-time data or SHM data collected from the field can effectively be integrated to get updated reliability. Two case studies for rolling element bearings are presented to demonstrate the applicability to life-time as well as SHM data.