Accepted Manuscripts

Jason C. York and Jeremy M. Gernand
ASME J. Risk Uncertainty Part B   doi: 10.1115/1.4036309
The potential benefits of a safety program, are generally only realized after an incident has occurred. Resource allocation in an organization's safety program has the imperative task of balancing costs and often unrealized benefits. Management can be wary to allocate additional resources to a safety program because it is difficult to estimate the return on investment, especially since the returns are a set of negative outcomes not manifested. One way that safety professionals can provide an estimate of potential return on investment is to forecast how the organizations incident rate can be affected by implementing different resource allocation strategies and what the expected incident rate would have been without intervention. This study evaluates forecasting methods used to predict incidents against one another against a common definition of performance accuracy to identify the method that would be the most applicable to use as part of a safety resource allocation model. By identifying the most accurate forecasting method, the uncertainty of which method a safety professional should utilize for incident rate prediction is reduced. Incident data from the Mine Safety and Health Administration (MSHA) was used to make short and long term forecasts. The performance of each of these methods was evaluated against one another to ascertain which method has the highest level of accuracy, lowest bias, and best complexity-adjusted goodness-of-fit metrics. The double exponential smoothing and ARMA statistical forecasting methods provided the most accurate incident rate predictions.
TOPICS: Mining, Safety, Mine safety, Goodness-of-fit tests, Performance, Resource allocation, Uncertainty, Risk
Zili Zhang, Biswajit Basu and Søren R.K. Nielsen
ASME J. Risk Uncertainty Part B   doi: 10.1115/1.4036310
Energy dynamics in buildings are inherently stochastic in nature due to random fluctuations from various factors such as the solar gain and the ambient temperature. This paper proposes a theoretical framework for stochastic modeling of the building thermal dynamics as well as its analytical solution strategies. Both the external temperature and internal gain are modeled as a stochastic process, composed of a periodic (daily) mean-value function and a zero-mean deviation process obtained as the output process of a unit Gaussian white noise passing through a rational filter. Based on the measured climate data, the indicated mean-value functions and rational filters have been identified for different months of a year. Stochastic differential equations in the state vector form driven by white noise processes have been established, and analytical solutions for the mean-value function and covariance matrix of the state vector are obtained. This framework would allow a simple and efficient way to carry out predictions and parametric studies on energy dynamics of buildings with random and uncertain climate effects. It would also provide a basis for the robust design of energy efficient buildings with predictive controllers.
TOPICS: Dynamics (Mechanics), Temperature, Risk, Structures, White noise, Climate, Filters, Stochastic processes, Control equipment, Fluctuations (Physics), Design, Differential equations, Modeling, Solar energy
Lida Naseh Moghanlou and Mohammad Pourgol-Mohammad
ASME J. Risk Uncertainty Part B   doi: 10.1115/1.4036064
Corrosion degradation is a common problem for boiler tubes in power plants, resulting in unscheduled plant shut down. In this research, degradation of the corrosion is investigated for a boiler tubes with the corrosion lifetime estimated. A special focus is made on the corrosion failures, the important failure modes and mechanisms for the metallic boiler tubes via Failure Modes and Effect Analysis (FMEA) method, evaluating the pitting corrosion as the most common failure mode in the tubes. Majority of the available approaches estimate lifetime of pitting corrosion by deterministic approaches, in which the results are valid only for limited conditions. In order to improve deficiencies of available models, a stochastic method is proposed here to study the corrosion life. The temporal behavior of metal degradation is analyzed in different conditions through the developed approach and a proper degradation model is selected. Uncertainty intervals/distributions are determined for some of the model parameters. The deterministic model is converted to a probabilistic model by taking to account the variability of the uncertain input parameters. The model is simulated using Monte Carlo method via simple sampling. The result of the life estimation is updated by the Bayesian framework using Monte Carlo Markov Chain. Finally, for the element that is subjected to the pitting corrosion degradation, the life distribution is obtained. Modeling results shows that pitting corrosion has stochastic behavior with lognormal distribution as proper fit for the pitting corrosion behavior. In order to validate the results, the estimations were compared with the power plant field failure data.
TOPICS: Corrosion, Boiler tubes, Risk, Failure mechanisms, Power stations, Failure, Failure data, Log normal distribution, Monte Carlo methods, Modeling, Metals, Failure mode and effects analysis, Chain, Uncertainty
Ricardo Cruz-Lozano, Fisseha M. Alemayehu, Stephen Ekwaro-Osire and Haileyesus B. Endeshaw
ASME J. Risk Uncertainty Part B   doi: 10.1115/1.4035867
Sketches can be categorized as personal, shared, persuasive, and handover sketches. Depending on each category, their level of ambiguity also varies. The applications of sketches includes conceptual design, eliciting user preferences, shape retrieval, and sketch-based modeling. There is a need for quantification of uncertainty in sketches in mapping of sketches to 3D models in sketch-based modeling, in eliciting user preferences, and in tuning the level of uncertainty in sketches at the conceptual design stage. This paper investigates the role of probability of importance in quantifying the level of uncertainty in sketches by raising the following three research questions: How are the features in a sketch ranked? What is the probability of importance of features in a sketch? What is the level of uncertainty in a sketch? This paper presents an improved framework for uncertainty quantification in sketches. The framework is capable of identifying and ranking the features in the sketch, determining their probability of importance, and finally quantifying the level of uncertainty in the sketch. Ranking the features of a sketch is performed by a hierarchical approach, whereas probability of importance is determined by assessing the probability of likeliness using a shape matching approach and a probability transformation. Quantification of uncertainty is accomplished by using the principle of normalization of entropy. A case study of a bicycle sketch is used to demonstrate that the framework eliminates the need of expert input in assessment of uncertainty in sketches, and, hence, can be used by design practitioners with limited experience.
TOPICS: Probability, Risk, Uncertainty, Conceptual design, Shapes, Modeling, Preferences, Uncertainty quantification, Ambiguity, Bicycles, Entropy, Design, Three-dimensional models
Bin Zhou and Kumar Bhimavarapu
ASME J. Risk Uncertainty Part B   doi: 10.1115/1.4035704
Industry has been implementing condition monitoring for turbines to minimize losses and to improve productivity. Deficient conditions can be identified before losses occur by monitoring the equipment parameters. For any loss scenario, the effectiveness of monitoring depends on the stage of the loss scenario when the deficient condition is detected. A scenario-based semi-empirical methodology was developed to assess various types of condition monitoring techniques, by considering their effect on the risk associated with mechanical breakdown of steam turbines in the forest products (FP) industry. A list of typical turbine loss scenarios was first generated by reviewing loss data and leveraging expert domain knowledge. Subsequently, condition monitoring techniques that can mitigate the risk associated with each loss scenario were identified. For each loss scenario, an event tree analysis was used to quantitatively assess the variations in the outcomes due to condition monitoring, and resultant changes in the risk associated with turbine mechanical breakdown. An application was developed following the methodology to evaluate the effect of condition monitoring on turbine risk mitigation.
TOPICS: Condition monitoring, Steam turbines, Risk, Risk mitigation, Turbines, Performance
Guozheng Song, Faisal Khan, Ming Yang and Hangzhou Wang
ASME J. Risk Uncertainty Part B   doi: 10.1115/1.4035438
The reliable prediction and diagnosis of abnormal events provides much needed guidance for risk management. Traditional Bayesian Network (traditional BN) has been used to dynamically predict and diagnose the abnormal events. However, its inherent limitation caused by discrete categorization of random variables degrades the assessment reliability. This paper proposes a continuous Bayesian Network (CBN) based model to reduce the above-mentioned limitation. To compute complex posterior distributions of CBN, Markov Chain Monte Carlo method (MCMC) was applied. A case study was conducted to demonstrate the application of CBN. A comparative analysis of the traditional BN and CBN was also presented. This work highlights that the use of CBN can overcome the drawbacks of traditional BN to make the dynamic prediction and diagnosis analysis more reliable.
TOPICS: Reliability, Chain, Monte Carlo methods, Risk management, Risk
Technical Brief  
Shruti Rapur Janani and Rajiv Tiwari
ASME J. Risk Uncertainty Part B   doi: 10.1115/1.4035440
When the hydraulic flow path is incompatible with the physical contours of the centrifugal pump (CP), flow instabilities occur. A prolonged operation in the flow-instability region may result in severe damages of the CP system. Hence, two of the major causes of flow instabilities such as the suction blockage (with five levels of increasing severity) and impeller defects are studied in the present work. Thereafter, an attempt is made to classify these faults and differentiate the physics behind the flow instabilities caused due to them. The tri-axial CP vibration data in time domain is employed for the fault classification. Multi-distinct and multi-coexisting fault classifications have been performed with different combinations of these faults using support vector machine (SVM) algorithm with radial basis function (RBF) kernel. Prediction results from the experiments and the developed methodology help to, segregate the faults into appropriate class, identify the severity of the suction blockage, and substantiate the practical applicability of this study.
TOPICS: Vibration, Centrifugal pumps, Fault diagnosis, Support vector machines, Risk, Flow instability, Suction, Impellers, Hydraulic flow, Algorithms, Damage, Physics
Jesus Luque, Rainer Hamann and Daniel Straub
ASME J. Risk Uncertainty Part B   doi: 10.1115/1.4035399
Corrosion in ship structures is influenced by a variety of factors that are varying in time and space. Existing corrosion models used in practice only partially address the spatial variability of the corrosion process. Typical estimations of corrosion model parameters are based on averaging measurements for one ship type over structural elements from different ships and operational conditions. Most models do not explicitly predict the variability and correlation of the corrosion process among multiple locations in the structure. This variability is of relevance when determining the necessary inspection coverage, and it can influence the reliability of the ship structure. In this paper, we develop a probabilistic spatio-temporal corrosion model based on a hierarchical approach, which represents the spatial variability of the corrosion process. The model includes as hierarchical levels vessel - compartment - frame - structural element - plate element. At all levels, variables representing common influencing factors (e.g. coating life) are introduced. Moreover, at the lowest level, which is the one of the plate element, the corrosion process can be modeled as a spatial random field. For illustrative purposes, the model is trained through Bayesian analysis with measurement data from a group of tankers. In this application it is found that there is significant spatial dependence among corrosion processes in different parts of the ships, which the proposed hierarchical model can capture. Finally, it is demonstrated how this spatial dependence can be exploited when making inference on the future condition of the ships.
TOPICS: Corrosion, Modeling, Ships, Risk, Structural elements (Construction), Vessels, Tankers, Coating processes, Coatings, Inspection, Reliability
Souvik Chakraborty, Tanmoy Chatterjee, Rajib Chowdhury and Sondipon Adhikari
ASME J. Risk Uncertainty Part B   doi: 10.1115/1.4035439
Optimization for crashworthiness is of vast importance in automobile industry. Recent advancement in computational prowess has enabled researchers and design engineers to address vehicle crashworthiness, resulting in reduction of cost and time for new product development. However, deterministic optimum design often resides at the boundary of failure domain, leaving little or no room for modelling imperfections, parameter uncertainties and/or human error. In this study, a operational model based robust design optimization (RDO) scheme has been developed for designing crashworthiness of vehicle against side impact. Within this framework, differential evolution algorithm (DEA) has been coupled with polynomial correlated function expansion (PCFE). It is argued that the coupled DEA-PCFE is more efficient and accurate, as compared to conventional techniques. For RDO of vehicle against side impact, minimization of the weight and lower rib deflection of the vehicle are considered to be the primary design objectives. Case studies by providing various emphasis on the two objectives have also been performed. For all the cases, DEA-PCFE is found to yield highly accurate results.
TOPICS: Optimization, Vehicles, Design, Crashworthiness, Risk, Uncertainty, Modeling, Weight (Mass), Engineers, Automotive industry, Deflection, Errors, Evolutionary algorithms, Failure, Polynomials, Product development
Kasmet T. Niyongabo and Scott B. Nokleby
ASME J. Risk Uncertainty Part B   doi: 10.1115/1.4032634
A proof-of-concept detector prototype capable of collecting and storing radiometric data in the Jet Boring System (JBS) during pilot hole drilling at the Cigar Lake uranium mine is presented. Cigar Lake is the world’s second highest known grade uranium mine and is located in northern Saskatchewan, Canada. Variant design is used to design, develop, test and implement the detector’s firmware, software and hardware. The battery powered detector is attached inside a JBS drill rod to collect radiometric data through the drilling cycle. A readout box is used to initiate the detector, recharge the battery and download radiometric data afterthe pilot hole drilling cycle is complete.Functional testing results are presented and comparative test results between the JBS gamma probe and the AlphaNUCLEAR Hi-Flux probe are evaluated. Field data collected from a pilot hole is plotted against the pilot hole’s driving layout and jetting recipe to show the accuracy of the readings collected.

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