No abstract

Enormous data are continuously collected by the structural health monitoring system of civil infrastructures. The structural health monitoring data inevitably involve anomalies caused by sensors, transmission errors, or abnormal structural behaviors. It is important to identify the anomalies and find their origin (e.g. sensor fault or structural damage) to make correct interventions. Moreover, online anomaly identification of the structural health monitoring data is critical for timely structural condition assessment and decision-making. This study proposes an online approach for detecting anomalies of the structural health monitoring data based on the Bayesian dynamic linear model. In particular, Bayesian dynamic linear model, consisting of various components, is implemented to characterize the feature of real-time measurements. Expectation maximization algorithm and Kalman smoother are combined to estimate the Bayesian dynamic linear model parameters and generate log-likelihood functions. The subspace identification method is introduced to overcome the initialization issue of the expectation maximization algorithm. The log-likelihood difference of consecutive time steps is then used to determine thresholds without introducing extra anomaly detectors. The proposed Bayesian dynamic linear model-based approach is first illustrated by the simulation data and then applied to the structural health monitoring data collected from two long-span bridges. The results indicate that the proposed method exhibits good accuracy and high computational efficiency and also allows for reconstructing the strain measurements to replace anomalies.

In this paper, a fault detection and diagnosis (FDD) scheme is studied for general stochastic dynamic systems subjected to state time delays. Different from the formulation of classical FDD problems, it is supposed that the measured information for the FDD is the probability density function (PDF) of the system output rather than its actual value. A B-spline expansion technique is applied so that the output PDF can be formulated in terms of the dynamic weights of the B-spline expansion, by which a time delay model can be established between the input and the weights with non-linearities and modelling errors. As a result, the concerned FDD problem can be transformed into a classic FDD problem subject to an uncertain non-linear system with time delays. Feasible criteria to detect the system fault are obtained and a fault diagnosis method is further presented to estimate the fault. Simple simulations are given to demonstrate the efficiency of the proposed approach.

The Yellow River, the second largest river in China, is the most important resource of water supply in North China. In the last 40 years, even in the upper Yellow River, with the development of industry and agriculture, more and more contaminants have been discharged into this river and greatly polluted the water. Although a routine chemical component analysis has been performed, little is known about the real toxic effects of the polluted water on organisms at environmental level. To explore whether the pollutants induced oxidative stress and damage to aquatic organisms, malondialdehyde (MDA) level and activities of superoxide dismutase (SOD), catalase (CAT), glutathione peroxidase (GPx) and glutathione S-transferase (GST) in hepatopancreas, kidney and intestine of the field-collected carp Cyprinus carpio from a mixed polluted (Lanzhou Region, LZR) and a relatively unpolluted (Liujiaxia Region, LJXR) sites of the upper Yellow River were measured. The results showed that when the values of LZR compared with those of LJXR, SOD and GST activities increased and GPx activity decreased significantly in all the three organs (P < 0.05-0.01); CAT activity decreased but MDA level increased significantly (P < 0.05-0.01) only in kidney and intestine. In conclusion, the results of this study suggest that the pollutants can induce obvious oxidative damage in the carp, and the SOD, GST and GPx might be better indicators for the oxidative damage in aquatic organisms.