Failure Diagnosis and Prognosis of Safety Critical Systems: Applications in Aerospace Industries

Many safety-critical systems such as aircraft, space crafts, and large power plants are required to operate in a reliable and efficient working condition without any performance degradation. As a result, fault diagnosis and prognosis (FDP) is a research topic of great interest in these systems. FDP systems attempt to use historical and current data of a system, which are collected from various measurements to detect faults, diagnose the types of possible failures, predict and manage failures in advance. This thesis deals with FDP of safety-critical systems. For this purpose, two critical systems including a multifunctional spoiler (MFS) and hydro-control value system are considered, and some challenging issues from the FDP are investigated. This research work consists of three general directions, i.e., monitoring, failure diagnosis, and prognosis. The proposed FDP methods are based on data-driven and model-based approaches. The main aim of the data-driven methods is to utilize measurement data from the system and forecast the remaining useful life (RUL) of the faulty components accurately and efficiently. In this regard, two dierent methods are developed. A modular FDP method based on a divide and conquer strategy is presented for the MFS system. The modular structure contains three components:1) fault diagnosis unit, 2) failure parameter estimation unit and 3) RUL unit. The fault diagnosis unit identifies types of faults based on an integration of neural network (NN) method and discrete wavelet transform (DWT) technique. Failure parameter estimation unit observes the failure parameter via a distributed neural network. Afterward, the RUL of the system is predicted by an adaptive Bayesian method. In another work, an innovative data-driven FDP method is developed for hydro-control valve systems. The idea is to use redundancy in multi-sensor data information and enhance the performance of the FDP system. Therefore, a combination of a feature selection method and support vector machine (SVM) method is applied to select proper sensors for monitoring of the hydro-valve system and isolate types of fault. Then, adaptive neuro-fuzzy inference systems (ANFIS) method is used to estimate the failure path. Similarly, an online Bayesian algorithm is implemented for forecasting RUL. Model-based methods employ high-delity physics-based model of a system for prognosis task. In this thesis, a novel model-based approach based on an integrated extended Kalman lter (EKF) and Bayesian method is introduced for the MFS system. To monitor the MFS system, a residual estimation method using EKF is performed to capture the progress of the failure. Later, a transformation is utilized to obtain a new measure to estimate the degradation path (DP). Moreover, the recursive Bayesian algorithm is invoked to predict the RUL. Finally, relative accuracy (RA) measure is utilized to assess the performance of the proposed methods

IMPACTS OF URBAN DEVELOPMENT PATTERN ON RUNOFF PEAK FLOWS AND STREAMFLOW FLASHINESS OF PERI-URBAN CATCHMENTS: ASSESSING THE PERFORMANCE OF PHYSICAL AND DATA-DRIVEN MODELS FOR REAL-TIME ENSEMBLE FLOOD FORECASTING

Urban growth is a global phenomenon, and the associated impacts on hydrology from land development are expected to increase, especially in peri-urban catchments, which are newly developing catchments in proximity of growing cities. In northern climates, hydrologic response of peri-urban catchments change with the water budget and climatic conditions. As a result, runoff response of northern peri-urban catchments can vary immensely across seasons. During warm seasons, the evapotranspiration (ET) and infiltration rates are high, so urban floods are expected to occur during high intensity, low duration storm events. During cold seasons and below freezing temperatures, surficial soils are typically frozen and nearly impervious. In addition, the ET rate is low throughout winter. Therefore, the difference in runoff response between peri-urban and natural catchments is least in winter. Furthermore, winter snow redistribution by plowing and endogenous urban heat affect the snowmelt timing and frequency. Due to the limited availability of data on snow removal and redistribution activities in northern peri-urban catchments, cold-season hydrologic modeling for peri-urban catchments remains a challenging task in urban hydrology. Research on the cold season hydrologic response of peri-urban catchments are mostly limited to Finland, Sweden, and Canada. The resulting research gap on seasonal change in hydrologic response of peri-urban catchments is common to many northern settings. In the first phase of this study, I use intensive discharge monitoring records at several peri-urban catchments near Syracuse, NY to calculate and compare seasonal runoff peak flows among several peri-urban catchments. These are selected to provide a range of drainage area and imperviousness to clarify the impact of urban development and catchment size on seasonal hydrologic behavior of peri-urban catchments. It is well understood that greater peak flows and higher stream flashiness are associated with increased surface imperviousness and storm location. However, the effect of the distribution of impervious areas on runoff peak flow response and stream flashiness of peri-urban catchments has not been well studied. In the second phase of this dissertation, I define a new geometric index, Relative Nearness of Imperviousness to the Catchment Outlet (RNICO), to correlate imperviousness distribution of peri-urban catchments with runoff peak flows and stream flashiness. The study sites for this phase of the study include ninety peri-urban catchments in proximity of 9 large US cities: New York, NY (NYC), Syracuse, NY, Baltimore, MD, Portland, OR, Chicago, IL, Austin, TX, Houston, TX, San Francisco, CA, and Los Angeles, CA. Based on RNICO, all development patterns are divided into 3 classes: upstream, centralized, and downstream. Analysis results showed an obvious increase in runoff peak flows and decrease in time to peak as the centroid of imperviousness moves downstream. This indicates that RNICO is an effective tool for classifying urban development patterns and for macroscale understanding of the hydrologic behavior of small peri-urban catchments, despite the complexity of urban drainage systems. Results for nine cities show strong positive correlations between RNICO and runoff peak flows and stream flashiness index for small peri-urban catchments. However, the area threshold used to distinguish small and large catchments differs slightly by location. For example, for Chicago, IL, NYC, NY, Baltimore, MD, Houston, TX, and Austin, TX area threshold values of 55, 40, 50, 42, and 32 km2 emerged, runoff peak flows in catchments with drainage area below these values were positively correlated to RNCIO. This first phase of this study suggests that RNICO is a stronger predictor of runoff peak flow and stream-flow regime in humid northern and southern US study sites, compared to more arid western US study sites. This difference is likely due to the greater precipitation rates and greater antecedent soil moisture contents for humid climates. The extent of urban infrastructure is less likely to control the effectiveness of RNICO for predicting runoff peak flows and R-B flashiness index for the selected study sites, due to the relatively similar urban development level within the peri-urban study catchments. Consistent forecast of peak flows across scales in flood hydrographs remains a challenge for most hydrologic models. Urbanization increases the magnitude and frequency of peak flows, often challenging the forecast ability for real-time flood prediction. Following advances in satellite and ground-based meteorological observations, global and continental real-time ensemble flood forecasting systems use a variety of physical hydrology models to predict urban peak flows. Artificial intelligence (AI) models provide an alternative approach to physical hydrology models for real-time flood forecasting. Despite recent advances in AI techniques for hydrologic prediction, ensemble stream-flow prediction by these methods has been limited. In addition, application of AI models for flood forecasting has been limited to large river basins, with very limited research on use of AI models for small peri-urban catchments. Flood forecasting in small urban catchments can be a critical task to urban safety due to the short time of concentration and quick precipitation runoff response. AI flood forecasting models typically apply upstream streamflow measurements to forecast downstream flood discharge. Therefore, the storm direction may change the flood travel time and time to peak, which challenges accurate flood forecasting. For example, if the storm direction is upstream through an AI model trained on the upstream gage data may fail to accurately predict peak flow magnitude and timing, at the outlet, this is due to the quicker runoff response of the downstream gage compared to the upstream station. There has been very limited focus on the impact of storm direction on peak flow response of urban catchments and available literature are limited to lab-scale prototypes and rainfall simulators. These may not fully represent real-world flooding scenarios. Therefore, the impact of storm direction on flood forecasting performance of peri-urban catchments is another important research gap in real-time urban flood forecasting. In the third phase of my dissertation project, I initially assess the impact of storm direction on the flood forecasting performance of an Adaptive Neuro Fuzzy Inference System (ANFIS) at a peri-urban catchment in proximity of Syracuse, NY. Next, I compare the relative utility of physical hydrology and AI approaches to predict flood hydrograph in peri-urban catchments. For this comparison, I selected ANFIS, and Sacramento Soil Moisture Accounting Model (SAC-SMA) for real-time ensemble re-forecasting of streamflow in several small to medium size suburban catchments near NYC for Hurricane Irene and a smaller storm event. The SAC-SMA model is a physical hydrology model that was initially developed by Burnash et al. (1973). The National Oceanic and Atmospheric Administration (NOAA) selected the SAC-SMA lumped model as a comparison baseline for participating distributed hydrologic models in the Distributed Model Intercomparison Project (DMIP), which aimed to identify the most suitable model for National Weather Service (NWS) streamflow prediction across the US (http://www.nws.noaa.gov/ohd/hrl/dmip/). More importantly, the NWS is currently using the lumped form of SAC-SMA for ensemble flood forecasting across the US (Emerton et al., 2016). For these reasons, I chose to employ a lumped version of SAC-SMA in my dissertation project. SAC-SMA performed well for both large and small events and for lead times of three to 24 hours, but ANFIS predicted the Hurricane Irene flood discharge well only for short lead times in small study catchments. ANFIS had reasonable percent bias (PBIAS) for predicting the small storm event for all lead times, indicating the utility of ANFIS for small events. In addition, the accuracy of both SAC-SMA and ANFIS models for ensemble flood prediction did not change significantly with catchment size and imperviousness. Overall, results of the third phase of this study suggest that the lumped SAC-SMA model may be a reliable option for local urban flood forecasting for evacuation plan lead time up to 24 hours. Due to the uncertainties in future climatic conditions, my study emphasizes the importance of using physical hydrology models for real-time flood forecasting of large events in small urban catchments. This recommendation is based on the finding that the performance of data-driven models may greatly decrease with the storm scale if the training period includes storms of magnitude less than storms in the validation period

A quick review of the applications of artificial neural networks (ANN) in the modelling of thermal systems

Thermal systems play a main role in many industrial sectors. This study is an elucidation of the utilization of artificial neural networks (ANNs) in the modelling of thermal systems. The focus is on various heat transfer applications like steady and dynamic thermal problems, heat exchangers, gas-solid fluidized beds, and others. Solving problems related to thermal systems using a traditional or classical approach often results to near feasible solutions. As a result of the stochastic nature of datasets, using the classical models to advance exclusive designs from the experimental dataset is often a function of trial and error. Conventional correlations or fundamental equations will not proffer satisfactory solutions as they are in most cases suitable and applicable to the problems from where they are generated. A preferable option is the application of computational intelligence techniques focused on the artificial neural network model with different structures and configurations for effective analysis of the experimental dataset. The main aim of current study is to review research work related to artificial neural network techniques and the contemporary improvements in the use of these modelling techniques, its up-and-coming application in addressing variability of heat transfer problems. Published research works presented in this paper, show that problems solved using the ANN model with regression analysis produced good solutions. Limitations of the classical and computational intelligence models have been exposed and recommendations have been made which focused on creative algorithms and hybrid models for future modelling of thermal systems.http://www.etasr.com/index.php/ETASR/indexdm2022Mechanical and Aeronautical Engineerin

Geospatial Artificial Intelligence (GeoAI) in the Integrated Hydrological and Fluvial Systems Modeling: Review of Current Applications and Trends

This paper reviews the current GeoAI and machine learning applications in hydrological and hydraulic modeling, hydrological optimization problems, water quality modeling, and fluvial geomorphic and morphodynamic mapping. GeoAI effectively harnesses the vast amount of spatial and non-spatial data collected with the new automatic technologies. The fast development of GeoAI provides multiple methods and techniques, although it also makes comparisons between different methods challenging. Overall, selecting a particular GeoAI method depends on the application's objective, data availability, and user expertise. GeoAI has shown advantages in non-linear modeling, computational efficiency, integration of multiple data sources, high accurate prediction capability, and the unraveling of new hydrological patterns and processes. A major drawback in most GeoAI models is the adequate model setting and low physical interpretability, explainability, and model generalization. The most recent research on hydrological GeoAI has focused on integrating the physical-based models' principles with the GeoAI methods and on the progress towards autonomous prediction and forecasting systems

Establishment of a novel predictive reliability assessment strategy for ship machinery

There is no doubt that recent years, maritime industry is moving forward to novel and sophisticated inspection and maintenance practices. Nowadays maintenance is encountered as an operational method, which can be employed both as a profit generating process and a cost reduction budget centre through an enhanced Operation and Maintenance (O&M) strategy. In the first place, a flexible framework to be applicable on complex system level of machinery can be introduced towards ship maintenance scheduling of systems, subsystems and components.;This holistic inspection and maintenance notion should be implemented by integrating different strategies, methodologies, technologies and tools, suitably selected by fulfilling the requirements of the selected ship systems. In this thesis, an innovative maintenance strategy for ship machinery is proposed, namely the Probabilistic Machinery Reliability Assessment (PMRA) strategy focusing towards the reliability and safety enhancement of main systems, subsystems and maintainable units and components.;In this respect, the combination of a data mining method (k-means), the manufacturer safety aspects, the dynamic state modelling (Markov Chains), the probabilistic predictive reliability assessment (Bayesian Belief Networks) and the qualitative decision making (Failure Modes and Effects Analysis) is employed encompassing the benefits of qualitative and quantitative reliability assessment. PMRA has been clearly demonstrated in two case studies applied on offshore platform oil and gas and selected ship machinery.;The results are used to identify the most unreliability systems, subsystems and components, while advising suitable practical inspection and maintenance activities. The proposed PMRA strategy is also tested in a flexible sensitivity analysis scheme.There is no doubt that recent years, maritime industry is moving forward to novel and sophisticated inspection and maintenance practices. Nowadays maintenance is encountered as an operational method, which can be employed both as a profit generating process and a cost reduction budget centre through an enhanced Operation and Maintenance (O&M) strategy. In the first place, a flexible framework to be applicable on complex system level of machinery can be introduced towards ship maintenance scheduling of systems, subsystems and components.;This holistic inspection and maintenance notion should be implemented by integrating different strategies, methodologies, technologies and tools, suitably selected by fulfilling the requirements of the selected ship systems. In this thesis, an innovative maintenance strategy for ship machinery is proposed, namely the Probabilistic Machinery Reliability Assessment (PMRA) strategy focusing towards the reliability and safety enhancement of main systems, subsystems and maintainable units and components.;In this respect, the combination of a data mining method (k-means), the manufacturer safety aspects, the dynamic state modelling (Markov Chains), the probabilistic predictive reliability assessment (Bayesian Belief Networks) and the qualitative decision making (Failure Modes and Effects Analysis) is employed encompassing the benefits of qualitative and quantitative reliability assessment. PMRA has been clearly demonstrated in two case studies applied on offshore platform oil and gas and selected ship machinery.;The results are used to identify the most unreliability systems, subsystems and components, while advising suitable practical inspection and maintenance activities. The proposed PMRA strategy is also tested in a flexible sensitivity analysis scheme

Metaheuristics algorithms to identify nonlinear Hammerstein model: A decade survey

Metaheuristics have been acknowledged as an effective solution for many difficult issues related to optimization. The metaheuristics, especially swarm’s intelligence and evolutionary computing algorithms, have gained popularity within a short time over the past two decades. Various metaheuristics algorithms are being introduced on an annual basis and applications that are more new are gradually being discovered. This paper presents a survey for the years 2011-2021 on multiple metaheuristics algorithms, particularly swarm and evolutionary algorithms, to identify a nonlinear block-oriented model called the Hammerstein model, mainly because such model has garnered much interest amidst researchers to identify nonlinear systems. Besides introducing a complete survey on the various population-based algorithms to identify the Hammerstein model, this paper also investigated some empirically verified actual process plants results. As such, this article serves as a guideline on the fundamentals of identifying nonlinear block-oriented models for new practitioners, apart from presenting a comprehensive summary of cutting-edge trends within the context of this topic area

Advances in Modeling and Management of Urban Water Networks

The Special Issue on Advances in Modeling and Management of Urban Water Networks (UWNs) explores four important topics of research in the context of UWNs: asset management, modeling of demand and hydraulics, energy recovery, and pipe burst identification and leakage reduction. In the first topic, the multi-objective optimization of interventions on the network is presented to find trade-off solutions between costs and efficiency. In the second topic, methodologies are presented to simulate and predict demand and to simulate network behavior in emergency scenarios. In the third topic, a methodology is presented for the multi-objective optimization of pump-as-turbine (PAT) installation sites in transmission mains. In the fourth topic, methodologies for pipe burst identification and leakage reduction are presented. As for the urban drainage systems (UDSs), the two explored topics are asset management, with a system upgrade to reduce flooding, and modeling of flow and water quality, with analyses on the transition from surface to pressurized flow, impact of water use reduction on the operation of UDSs, and sediment transport in pressurized pipes. The Special Issue also includes one paper dealing with the hydraulic modeling of an urban river with a complex cross-section