F2G: A hybrid fault-function graphical model for reliability analysis of complex equipment with coupled faults

Dynamic Model-based Saddle-point Approximation for Reliability and Reliability-based Sensitivity Analysis

The transient proper orthogonal decomposition (TPOD) method is applied for order reduction in the rotor-bearing system with the coupling faults in this paper. A 24 degrees of freedom (DOFs) rotor model supported by a pair of sliding bearings with both crack and rub-impact faults is established by the discrete modeling method. The complexity of dynamic behaviors of the rotor system with the coupling faults is discussed via the comparison of the rotor system with the single fault (crack or rub-impact). The proper orthogonal mode (POM) energy method is proposed to confirm the DOF number of the reduced model. The TPOD method is used in the coupling faults system to obtain the optimal order reduction model based on the POM energy. The efficiency of the order reduction method is verified by comparing the bifurcation behaviors between the original and the reduced system. The TPOD method provides the optimal order reduction model to study the non-linear dynamic characteristics of the complex rotor system with the coupling faults.

The transient proper orthogonal decomposition (TPOD) method is applied to order reduction in the rotor-bearing system with the coupling faults in this chapter. A 24 degrees of freedom (DOFs) rotor model supported by a pair of sliding bearings with both crack and rub-impact faults is established with the discrete modeling method. The complexity of dynamic behaviors of the rotor system with the coupling faults is analyzed via the comparison of the rotor system with the single fault (crack or rub-impact). The proper orthogonal mode (POM) energy method is proposed to confirm DOF number of the reduced model, and TPOD method is used in the coupling faults system to obtain the optimal order reduction model based on POM energy. The presented order reduction method is verified by comparing the bifurcation behaviors between the original and the reduced system. As one conclusion, it can be drawn that TPOD method could provide the optimal order reduction model to study the nonlinear dynamic behaviors of the complex rotor system with the coupling faults.

Reliability analysis has become a tool of fundamental importance to virtually all electrical and computer engineers because of the extensive usage of hardware systems in safety and mission critical domains, such as medicine, military, and transportation. Due to the strong relationship between reliability theory and probabilistic notions, computer simulation techniques have been traditionally used to perform reliability analysis. However, simulation provides less accurate results and cannot handle large-scale systems due to its enormous CPU time requirements. To ensure accurate and complete reliability analysis and thus more reliable hardware designs, we propose to conduct a formal reliability analysis of systems within the sound core of a higher order logic theorem prover (HOL). In this paper, we present the higher order logic formalization of some fundamental reliability theory concepts, which can be built upon to precisely analyze the reliability of various engineering systems. The proposed approach and formalization is then utilized to analyze the repairability conditions for a reconfigurable memory array in the presence of stuck-at and coupling faults.

Structural-graphic modelling is considered: concept, connections, classification and application in cartographic research. The principles of modelling in cartography (the possibility of using maps using common epistemological categories; the use of general scientific methods of modelling and rational forms and variants of contacts with other types of modelling; the etymology of the term indicates the place of cartographic modelling as a means of research in the general system of cognition). The functions of structural-graphical modelling are defined. Functions of structural and graphical modelling are determined, and they are investigated, which allow: to carry out the selection of existing maps necessary for research; identify elements of systems that have not yet been mapped; determine the topic of maps and their placement within the complex cartographic works; use maps of other elements of the system when creating a series of maps of each specific element; ensure that the maps of this element are those of other elements; change the complex maps and their groups; determine the main columns of tables for collecting information in relational or electronic databases; identify sections of map legends; present legends in the form of graphical link models of lower-ranking system elements. Spatial modeling is investigated, the types and models of real-world spatial models are substantiated and identified, in particular: analogue (an analog model is defined as a large-scale model, representation of the real-world system, in which each part of the real system is modeled in miniature, very popular analog models of the real world are paper topographic, geographic and thematic maps); digital models (all operations are carried out using a computer, data is collected in the data model and encoded using different coding schemes that reduce the relevant aspects of the real world to structures of zeros and ones); discrete (models imitate the processes that occur between discrete entities, such as the forces acting between celestial bodies and controlling their movement, or the behavior of humans or animals when they interact in space); continuous models (on the other hand, are models in terms of variables that are continuous space functions, for example, atmospheric pressure or temperature, soil acidity or humidity. The concept of a continuous field describes the geographic world with a series of continuous maps, each of which represents the changes of a definite variable over the surface of the Earth); individual (you can model any system with a set of rules on the mechanical behavior of the main objects of the system); aggregate models (approach is to merge (aggregate) individual objects into a single whole and model the system through the behavior of these aggregates); static model (models can be static if the input and output are the same at the same time point, or dynamic, if the output represents a later time point than the input); dynamic models (dynamic models, on the other hand, constitute a process that changes or transforms some aspects of the Earth’s surface over time); cellular vending machines (in cellular automaton spatial variations are represented as a raster of fixed resolution, each cell of which is assigned one of a finite set of certain states); agent models (agent model is a series of interacting active objects that reflect objects and relationships in the real world, from the point of view of practical application, agent modeling can be defined as a modeling method that investigates the behavior of decentralized agents and how this behavior determines the behavior of the whole system as a whole).

The inference and modeling of network‐like structures in genomic data is of prime importance in systems biology. Complex stochastic associations and interdependencies can very generally be described as a graphical model. However, the paucity of available samples in current high‐throughput experiments renders learning graphical models from genome data, such as microarray expression profiles, a challenging and very hard problem. Here we review several recently developed approaches to small‐sample inference of graphical Gaussian modeling and discuss strategies to cope with the high dimensionality of functional genomics data. Particular emphasis is put on regularization methods and an empirical Bayes network inference procedure.

The temporal dimension of age is a universal truth. Since time immemorial, individuals from all walks of life have glorified age and the process of getting older from an abstract perspective. "The best tunes are played on the oldest fiddles!" - Ralph Waldo Emerson, "The longer I live, the more beautiful life becomes." - Frank Lloyd Wright are some of the examples where aging and maturing into a better version have been projected as synonymous. However, in the physical world of plants and machinery, age is generally looked down upon as a harbinger of reduced ability in terms of performance, dependability, efficiency etc. This is best resonated in the words of William Shakespeare – "Where Youth is full of sport, age's breath is short; Youth is nimble, age is lame; Youth is hot and bold, age is weak and cold; Youth is wild, and age is tame." In a typical Oil & Gas environment, all the three primary reliability indices i.e. Reliability, Availability, Maintainability of equipment/system get adversely affected by age for many of the equipment type/family. Wear and tear, Degradation in strength, Fatigue, Creep, Corrosion, Deterioration of mechanical, electrical, chemical and hydraulic nature, Poor service (Maintenance, Repair, Replacement) e.g. replacement of failed parts by partially aged ones, incorrect fitment, imperfect repair etc. are all reasons of aging and sources of age-related failure. This necessitates that during Reliability Analysis of equipment and systems, age is effectively captured and factored in the calculation process. Capturing age becomes even more important when predictions and forecasts are to be made over a substantially long duration of operation and inability to do so widens the gap between analysis outcome and reality.

PurposeThe purpose of this paper is to briefly summarize and review the theories and methods of complex structures’ dynamic reliability. Complex structures are usually assembled from multiple components and subjected to time-varying loads of aerodynamic, structural, thermal and other physical fields; its reliability analysis is of great significance to ensure the safe operation of large-scale equipment such as aviation and machinery.Design/methodology/approachIn this paper for the single-objective dynamic reliability analysis of complex structures, the calculation can be categorized into Monte Carlo (MC), outcrossing rate, envelope functions and extreme value methods. The series-parallel and expansion methods, multi-extremum surrogate models and decomposed-coordinated surrogate models are summarized for the multiobjective dynamic reliability analysis of complex structures.FindingsThe numerical complex compound function and turbine blisk are used as examples to illustrate the performance of single-objective and multiobjective dynamic reliability analysis methods. Then the future development direction of dynamic reliability analysis of complex structures is prospected.Originality/valueThe paper provides a useful reference for further theoretical research and engineering application.

Dynamic reliability analysis using the extended support vector regression (X-SVR)

For the current study of large-span bridges are mostly in the component level, the real structural system reliability analysis is still relatively small, so based on the theory of dynamic reliability analysis of structure, the overall bridge structure system for the calculation model is established, then reliability analysis under the multi-dimensional seismic load and the effect of dynamic flutter are performed, the results of sensitivity analysis with different parameters are obtained, so it is useful basis for the bridge design and evaluation.

Physics guided wavelet convolutional neural network for wind-induced vibration modeling with application to structural dynamic reliability analysis

This paper proposes a Graphical Modeling method for selecting input variables of short-term load forecasting in power systems. Short-term load forecasting plays a key role to smooth operation and planning such as economic load dispatching, unit commitment, etc. In addition, the deregulated power market players require more accurate prediction models for short-term load forecasting to maximize a profit and minimize the risk As a result, it is of importance to focus on the relationship between input and output variables. In this paper, a graphical modeling method is used to determine the appropriate input variables of ANN (artificial neural network) model in short-term load forecasting. It has advantage that more effective input variables are selected because of excluding the pseudo-correlation that gives more errors to the predicted value. The proposed method is tested for real data of short-term load forecasting.

BackgroundGraphical models were identified as a promising new approach to modeling high-dimensional clinical data. They provided a probabilistic tool to display, analyze and visualize the net-like dependence structures by drawing a graph describing the conditional dependencies between the variables. Until now, the main focus of research was on building Gaussian graphical models for continuous multivariate data following a multivariate normal distribution. Satisfactory solutions for binary data were missing. We adapted the method of Meinshausen and Bühlmann to binary data and used the LASSO for logistic regression. Objective of this paper was to examine the performance of the Bolasso to the development of graphical models for high dimensional binary data. We hypothesized that the performance of Bolasso is superior to competing LASSO methods to identify graphical models.MethodsWe analyzed the Bolasso to derive graphical models in comparison with other LASSO based method. Model performance was assessed in a simulation study with random data generated via symmetric local logistic regression models and Gibbs sampling. Main outcome variables were the Structural Hamming Distance and the Youden Index.We applied the results of the simulation study to a real-life data with functioning data of patients having head and neck cancer.ResultsBootstrap aggregating as incorporated in the Bolasso algorithm greatly improved the performance in higher sample sizes. The number of bootstraps did have minimal impact on performance. Bolasso performed reasonable well with a cutpoint of 0.90 and a small penalty term. Optimal prediction for Bolasso leads to very conservative models in comparison with AIC, BIC or cross-validated optimal penalty terms.ConclusionsBootstrap aggregating may improve variable selection if the underlying selection process is not too unstable due to small sample size and if one is mainly interested in reducing the false discovery rate. We propose using the Bolasso for graphical modeling in large sample sizes.

Graphical models facilitate the representation of psychological variables as complex systems of interacting variables structured as a network. However, their current statistical estimation procedures overlook the assumption of clustering, which refers to the grouping of subsets of variables that are more densely connected within the network, despite this assumption playing a central role in many psychological theories. We address this gap by proposing the use of the Stochastic Block Model (SBM) as a prior distribution on the network structure of a graphical model for binary and ordinal data. The SBM assumes that variables belong to latent clusters, where the probability of an edge depends on the cluster membership of the nodes. Embedding this prior in a Bayesian graphical modeling framework allows researchers to formally incorporate theoretical expectations about clustering, test hypotheses about the number of clusters, and estimate cluster assignments from cross-sectional data. We demonstrate the benefits of this approach in a simulation study and reanalyze 30 openly available empirical datasets to test for clustering. This work highlights how the Bayesian framework can embed theoretical assumptions into network models via priors and introduces a new tool for latent cluster inference in psychological network analysis.

The connection of clinical care with clinical research is the main purpose of the Learning Health System (LHS) integrating scientific information, informatics, and patient care. The LHS generates new medical knowledge as a by-product of the care process. For this purpose, the aggregation of data from Electronic Health Records (EHR), Case Report Forms (CRF), web questionnaires with other data sources like primary care databases and genetic data repositories is necessary for research purposes. This joining of healthcare and research processes results in challenges for the privacy protection framework of the LHS. Based on an exploration of EU legal requirements for data protection and privacy, different data access policies of data provider organizations as well as existing privacy frameworks of research projects, basic privacy principles and privacy requirements were extracted. Based on privacy principles and legal requirements a graphical model to display privacy protection requirements was created. This graphical model is based on concepts of requirements engineering and can be used like a model kit to create new privacy frameworks and to ease knowledge exchange with stakeholders of the LHS. Our model is built upon the concept of three privacy zones (Care Zone, Non-Care Zone and Research Zone) representing areas where similar legal requirements and rules apply. These zones contain databases, data transformation operators, such as data linkers and privacy filters and graphs to indicate the data flow necessary for research processes. The aim of the model is to help arrange its components in a way that creates a risk gradient for the data flow from a zone of high risk for patient identification to a zone of low risk. The model is applied to the analysis of several general clinical research usage scenarios and two research use cases from the TRANSFoRm project (finding patients for clinical research and linkage of databases). Both use cases represent different data collection aspects of the LHS. The model was used during discussions with data managers from the NIVEL Primary Care Database in the Netherlands and validated by representing an approved research case of using primary care data employing NIVEL services. Experiences with the graphic privacy model used to improve the privacy framework of TRANSFoRm and with the presentation of the model to LHS stakeholders and the research community are discussed.