SummaryThis paper presents a statistical framework to monitor the performance of an operational concrete arch dam using sensory data acquired during its initial service life.One of the major challenges in dealing with a newly constructed dam is to predict its long-term behaviour by forecasting appropriate thresholds using limited data exhibiting nonstationarity. In this paper, a hybrid model is implemented to predict dam responses using environmental-hydrostatic, seasonal, and temperature-as well as age-related variables. The data from multiple sensors are first analyzed using principal component analysis to incorporate overall dam behaviour into a prediction model. The proposed prediction framework is then employed to estimate the residuals and control limits required to calculate thresholds under nonstationary operating conditions during its initial service life. The dam performance is then monitored using statistical control charts and anomalies are detected by comparing the test statistics, square prediction error, and Hotelling T-squared, calculated from the residuals with the preset control limits. The issue of limited data is addressed by updating the model parameters and thresholds periodically, which is aimed at minimizing the false alarm rate. The proposed method is demonstrated using a 130-m-high double-arch concrete dam located in Bulgaria.

A robust hybrid hidden Markov model-based fault detection method is proposed to perform multi-state fault classification of rotating components. The approach presented in this paper enhances the performance of the standard hidden Markov model (HMM) for fault detection by performing a series of pre-processing steps. First, the de-noised time-scale signatures are extracted using wavelet packet decomposition of the vibration data. Subsequently, the Teager Kaiser energy operator is employed to demodulate the time-scale components of the raw vibration signatures, following which the condition indicators are calculated. Out of several possible condition indicators, only relevant features are selected using a decision tree. This pre-processing improves the sensitivity of condition indicators under multiple faults. A Gaussian mixing model-based hidden Markov model (HMM) is then employed for fault detection. The proposed hybrid HMM is an improvement over traditional HMM in that it achieves better separation of the feature space leading to more robust state estimation under multiple fault states and measurement noise scenarios. A simulation employing modulated signals and two experimental validation studies are presented to demonstrate the performance of the proposed method.

Safety and reliability of large critical infrastructure systems such as long-span bridges, high-rise buildings, nuclear power plants, high-voltage transmission towers, rolling-element bearing and so on are important for a modern society. Research on reliability and safety analysis started with a `small data' business dealing with relative scarce lifetime or failure data. Later, degradation modelling that uses performance deterioration or condition data collected from inservice inspections or online health monitoring became an important analytical tool for reliability prediction and maintenance planning of highly reliable engineering systems. Over the past decades, a large number of degradation models have been developed to characterize and quantify the underlying degradation mechanism using direct and indirect measurements. Recent advancements in artificial intelligence, remote sensing, big data analytics, and Internet of Things are making far-reaching impacts on almost every aspect of our life. How these changes will affect the degradation modelling, health prognosis, and safety management is an interesting questions to explore. This paper presents a comprehensive, forward-looking review of the various degradation models and their practical applications to damage prognosis and management of critical infrastructure. The degradation models were classified into four categories: physics-based, knowledge-based, data-driven, and hybrid approaches.

In this article, we present a probabilistic approach for fault detection and prognosis of rolling element bearings based on a two-phase degradation model. One of the main issues in dealing with bearing degradation is that the degradation mechanism is unobservable and can only be inferred through appropriate surrogate measures obtained from indirect sensory measurements. Furthermore, the stochastic nature of the degradation path renders fault detection and estimating the end-of-life characteristics from such data extremely challenging. When such components are a part of a larger system, the exact degradation path depends on both the operating and loading conditions, which means that the most effective condition monitoring approach should estimate the degradation model parameters under operational conditions, and not solely from isolated component testing or historical information. Motivated by these challenges, a two-phase degradation model using surrogate measures of degradation from vibration measurements is proposed and a Bayesian approach is used to estimate the model parameters. The underlying methodology involves using priors from historical data, while the posterior calculations are undertaken using surrogate measures obtained from a monitored unit combined with the aforesaid priors. The problem of fault detection is posed as a change point location problem. This allows the prior knowledge obtained from the past failures to be integrated for maintenance planning of a currently working unit in a systematic way. The correlation between the degradation rate and the time of occurrence of the change point, an often overlooked aspect in prognosis, is also considered in here. A numerical example and a case study are presented to illustrate the overall methodology and the results obtained using this approach.

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