Predicting Failures for Repairable System Subjected to Imperfect Maintenance

The purpose of this project is to develop a reliability model which results from reliability analysis conducted on repairable system subjected to imperfect maintenance. Hence, in order to perform the reliability analysis, field data from actual equipment failure were gathered and analyzed. In this project, the equipment selected was the centrifugal pump used in one of the petrochemical plants. Various stages had been conducted in order to achieve the objectives of the project. This includes data screening and analysis, determination of failure distribution as well as the maintenance effectiveness which denoted by q. All of these phases were performed by using the reliability software, Weibull ++7. The data analysis showed that the failure data displayed Weibull distribution while q value indicated the Generalized Renewal Process (GRP) is the most applicable probabilistic models that characterized the failure data. Thus, the reliability model was developed by using GRP model of Type I and Type II. The comparison between both models was conducted to select the suitable model to be used in developing the reliability model. Based on the likelihood value (LV), GRP model Type I was selected as it possessed higher LV and this model was used to predict the future failures of the system. Evaluation phase was conducted to verify that GRP model Type I was the most suitable model which fits best the failure data. In this phase, the reliability model was developed by using other probabilistic models such as Renewal Process (RP) and Non-Homogeneous Poisson Process (NHPP). The LV were compared which resulted in GRP model Type I produced the highest LV. Finally, the model was validated by using reliability models developed based on the different duration of operation days which were 1500 and 2000 operation days, respectively. The expected cumulative numbers of failures calculated by both models were then compared with the actual cumulative number of failures obtained from the model developed using 3000 operation days. Based on the comparison, both models produced similar values with the actual failure data. Hence, the developed reliability model could be used to predict the next failure of the system. It is hoped that this project and report could be used as a reference for further research and study

Predicting Failures for Repairable System Subjected to Imperfect Maintenance

The purpose of this project is to develop a reliability model which results from reliability analysis conducted on repairable system subjected to imperfect maintenance. Hence, in order to perform the reliability analysis, field data from actual equipment failure were gathered and analyzed. In this project, the equipment selected was the centrifugal pump used in one of the petrochemical plants. Various stages had been conducted in order to achieve the objectives of the project. This includes data screening and analysis, determination of failure distribution as well as the maintenance effectiveness which denoted by q. All of these phases were performed by using the reliability software, Weibull ++7. The data analysis showed that the failure data displayed Weibull distribution while q value indicated the Generalized Renewal Process (GRP) is the most applicable probabilistic models that characterized the failure data. Thus, the reliability model was developed by using GRP model of Type I and Type II. The comparison between both models was conducted to select the suitable model to be used in developing the reliability model. Based on the likelihood value (LV), GRP model Type I was selected as it possessed higher LV and this model was used to predict the future failures of the system. Evaluation phase was conducted to verify that GRP model Type I was the most suitable model which fits best the failure data. In this phase, the reliability model was developed by using other probabilistic models such as Renewal Process (RP) and Non-Homogeneous Poisson Process (NHPP). The LV were compared which resulted in GRP model Type I produced the highest LV. Finally, the model was validated by using reliability models developed based on the different duration of operation days which were 1500 and 2000 operation days, respectively. The expected cumulative numbers of failures calculated by both models were then compared with the actual cumulative number of failures obtained from the model developed using 3000 operation days. Based on the comparison, both models produced similar values with the actual failure data. Hence, the developed reliability model could be used to predict the next failure of the system. It is hoped that this project and report could be used as a reference for further research and study

Estimating Pump Reliability using Recurrent Data Analysis for Failure Modes

Major equipment such as centrifugal pumps play an important role in oil and gas business. The main concern which is highlighted is the pumps’ performance at site whether it is reliable or instead. In this study, the analysis is done in order to estimate pump reliability using recurrent data analysis (RDA) for failure modes. Thus four centrifugal pumps in Onshore Slugcatcher (OSC) terminal are used as the case study to verify the analysis done on repairable system. The reliability and availability of the centrifugal pumps could be determined using parametric recurrent data analysis approach. Thus the data regarding the centrifugal pumps operated in Onshore Slugcatcher terminal is collected from PETRONAS Carigali Sdn. Bhd before they will be further analysed using reliability software such as Weibull++ and BlockSim from ReliaSoft Corporation. Based on the explanatory results, the failure modes of respective centrifugal pumps are identified and categorized based on ISO 14224 standard. Next, Weibull++ software is used to determine the failure and repair distributions, while the reliability block diagram of the pump by failure modes is generated using BlockSim. The reliability and availability by failure modes and pump units are determined. The further analysis is hoped will benefit the maintenance team to come up with better maintenance strategy to improve the pumps’ performance. Keywords: Recurrent Data Analysis (RDA), Repairable System, Reliability Block Diagram, Centrifugal pump

EconWorks Tools for Assessing the Wider Economic Benefits of Transportation Implementation Assistance

The Indiana Department of Transportation (INDOT) is undertaking efforts to assess the potential economic development benefits associated with highway corridor improvements at the middle-stage planning level. The primary objective of this research is to demonstrate and document the use of the EconWorks W.E.B. (wider economic benefits) tools for assessing the wider economic benefits (reliability, accessibility, and intermodal connectivity) of transportation projects in the State of Indiana. A parallel analysis of selected projects using TREDIS was also conducted in order to compare the relative merit or synergies between the tools. In the short term, the implementation of this study will consist of a set of training sessions for INDOT and MPOs. These sessions will cover the theoretical background as well as demonstrate the use of the EconWorks W.E.B. tools. In the long-term, INDOT plans to use the EconWorks Connectivity tool on projects that provide linkages to multimodal facilities. INDOT has also identified future studies where the economic impacts of recommended strategies can be estimated using the EconWorks W.E.B. tools. Available staff resources and staff training in economic modeling were indicated as key challenges to a wide implementation of these tools

Modeling repairable system failure data using NHPP realiability growth mode.

Stochastic point processes have been widely used to describe the behaviour of repairable systems. The Crow nonhomogeneous Poisson process (NHPP) often known as the Power Law model is regarded as one of the best models for repairable systems. The goodness-of-fit test rejects the intensity function of the power law model, and so the log-linear model was fitted and tested for goodness-of-fit. The Weibull Time to Failure recurrent neural network (WTTE-RNN) framework, a probabilistic deep learning model for failure data, is also explored. However, we find that the WTTE-RNN framework is only appropriate failure data with independent and identically distributed interarrival times of successive failures, and so cannot be applied to nonhomogeneous Poisson process

Estimating Pump Reliability using Recurrent Data Analysis for Failure Modes

Major equipment such as centrifugal pumps play an important role in oil and gas business. The main concern which is highlighted is the pumps’ performance at site whether it is reliable or instead. In this study, the analysis is done in order to estimate pump reliability using recurrent data analysis (RDA) for failure modes. Thus four centrifugal pumps in Onshore Slugcatcher (OSC) terminal are used as the case study to verify the analysis done on repairable system. The reliability and availability of the centrifugal pumps could be determined using parametric recurrent data analysis approach. Thus the data regarding the centrifugal pumps operated in Onshore Slugcatcher terminal is collected from PETRONAS Carigali Sdn. Bhd before they will be further analysed using reliability software such as Weibull++ and BlockSim from ReliaSoft Corporation. Based on the explanatory results, the failure modes of respective centrifugal pumps are identified and categorized based on ISO 14224 standard. Next, Weibull++ software is used to determine the failure and repair distributions, while the reliability block diagram of the pump by failure modes is generated using BlockSim. The reliability and availability by failure modes and pump units are determined. The further analysis is hoped will benefit the maintenance team to come up with better maintenance strategy to improve the pumps’ performance. Keywords: Recurrent Data Analysis (RDA), Repairable System, Reliability Block Diagram, Centrifugal pump

Management. A continuing bibliography with indexes

This bibliography cites 604 reports, articles, and other documents introduced into the NASA scientific and technical information system in 1979 covering the management of research and development, contracts, production, logistics, personnel, safety, reliability and quality control. Program, project, and systems management; management policy, philosophy, tools, and techniques; decision making processes for managers; technology assessment; management of urban problems; and information for managers on Federal resources, expenditures, financing, and budgeting are also covered. Abstracts are provided as well as subject, personal author, and corporate source indexes

Spatio-temporal probabilistic methodology to estimate location-specific loss-of-coolant accident frequencies for risk-informed analysis of nuclear power plants

The United States Nuclear Regulatory Commission (NRC) has promoted the use of Probabilistic Risk Assessment (PRA) in nuclear regulatory activities. Since loss-of-coolant accidents (LOCAs) are critical initiating events for many PRA applications, the NRC has taken steps towards the quantification of LOCA frequencies for use in risk-informed applications. This research develops the Spatio-Temporal Probabilistic methodology to explicitly incorporate the underlying physics of failure mechanisms into the location-specific estimation of LOCA frequencies that are required for risk-informed regulatory applications such as risk-informed resolution of generic Safety Issue 191 (GSI-191). The essence of the risk-informed resolution of GSI-191 is that location-specific LOCA frequencies drive the risk. The most recent NRC-sponsored estimations of LOCA frequencies were developed through an expert elicitation approach, provided in NUREG-1829. These estimations provided an implicit incorporation of underlying physics, space, and time. In support of the South Texas Project Nuclear Operating Company (STPNOC) risk-informed pilot project to resolve GSI-191, Fleming and Lydell developed a study which laid the groundwork for the location-specific estimations of LOCA frequencies. This research performs a critical review and a step-by-step quantitative verification of Fleming and Lydell’s methodology and, thus, two key methodological gaps are identified: (a) lack of inclusion of non-piping reactor coolant system components, and (b) lack of explicit incorporation of the underlying physics of failure that lead to the occurrence of a LOCA. To address these gaps, first, this research qualitatively examines the significance of including the contributions of non-piping components into the estimations of LOCA frequencies by conducting industry-academia evidence seeking and screening processes. Then, the Spatio-Temporal Probabilistic methodology is developed that can be used to quantitatively compare non-piping and piping components with respect to LOCA frequencies. The proposed Spatio-Temporal Probabilistic methodology also integrates the following two types of modeling: (1) The Markov modeling technique to depict the renewal processes of components’ repair due to periodic maintenance after degradations; (2) Probabilistic Physics of failure (PPoF) models to explicitly incorporate the failure mechanisms, associated with the location and age of components, into the estimation of LOCA frequencies. PPoF models integrate the underlying mechanisms related to degradation into the Markov modeling technique and, subsequently, into location-specific LOCA frequency estimations. In most of Markov models developed in this area of research, transition rates are developed using solely data-driven approaches and utilizing service data. The main problems with the Markov models with the solely data-driven transition rates are (1) inaccuracy due to insufficient data and (2) the lack of explicit connections with location-specific physics of failure mechanisms associated with transition rates. There is only one existing research that combines the Markov modeling technique with a stress-strength model of erosion corrosion for the piping components of Pressurized Heavy Water Reactors (PHWR); however due to the underlying assumptions of the methodology, this study does not adequately provide explicit incorporation of physical factors associated with locations. The Spatio-Temporal probabilistic methodology is the first research that combines the Markov technique with PPoF models for LOCA frequency estimations and, has four key tasks including: Task #1: Defining Markov States of Degradation Task #2: Modeling and Quantification of the Transition Rates of Degradation o Task # 2.1: Developing and quantifying physics of failure causal models o Task #2.2: Propagating uncertainties in the physics of failure causal models to develop Probabilistic Physics of failure (PPoF) models o Task #2.3: Calculating transition rates of degradation based on the output of Probabilistic Physics of failure (PPoF) models o Task # 2.4: Bayesian integration of the estimated transition rate from PPoF models and the ones from solely data-oriented approaches Task #3: Modeling and Quantification of the Transition Rates of Repair Task #4: Developing the Time-dependent Distributions of State Probabilities The Spatio-Temporal Probabilistic methodology provides the possibility for explicitly including the effects of location-specific causal factors, such as operating conditions (e.g., temperature, pressure, pH), maintenance quality, and material properties (e.g., yield strength and corrosion resistance) on the probability of LOCA occurrence. This methodology is beneficial, not only for estimation of location-specific LOCA frequencies, but also for incorporation of spatio-temporal physics of failure into Probabilistic Risk Assessment (PRA); therefore, it helps advance risk estimation and risk prevention. The explicit incorporation of failure mechanisms helps more accurately estimate the likelihood of LOCA occurrences, dealing with limited historical data. Additionally, the explicit incorporation of the causal factors enables the use of sensitivity analyses, which allow the physical causal factors to be ranked in order of their risk significance. Ranking of causal factors helps optimize maintenance practices by indicating the most resource-efficient methods to reduce risks. To show the feasibility, the spatio-temporal probabilistic methodology is implemented to examine the effects of Stress Corrosion Cracking (SCC) on the rupture probability of steam generator tubes. This case study demonstrates the comparative capabilities of the methodology by showing the variation in rupture probability based on the selection of Stainless Steel and Alloy 690 materials for fabrication of the expansion-transition region of the steam generator tubes. Although the tasks in this case study are explained based on SCC, which is a dominant mechanism associated with LOCA in nuclear power plants, the Spatio-Temporal Probabilistic methodology can be applied for other failure mechanisms (e.g., wear, creep) and for any high-consequence industry that deals with containment of flowing liquids or gases, such as the oil and gas industry

Security in Mobile Networks: Communication and Localization

Nowadays the mobile networks are everywhere. The world is becoming more dependent on wireless and mobile services, but the rapid growth of these technologies usually underestimates security aspects. As wireless and mobile services grow, weaknesses in network infrastructures become clearer. One of the problems is privacy. Wireless technologies can reduce costs, increase efficiencies, and make important information more readily and widely available. But, there are also risks. Without appropriate safeguards, these data can be read and modified by unauthorized users. There are many solutions, less and more effective, to protect the data from unauthorized users. But, a specific application could distinguish more data flows between authorized users. Protect the privacy of these information between subsets of users is not a trivial problem. Another problem is the reliability of the wireless service. Multi-vehicle systems composed of Autonomous Guided Vehicles (AGVs) are largely used for industrial transportation in manufacturing and logistics systems. These vehicles use a mobile wireless network to exchange information in order to coordinate their tasks and movements. The reliable dissemination of these information is a crucial operation, because the AGVs may achieve an inconsistent view of the system leading to the failure of the coordination task. This has clear safety implications. Going more in deep, even if the communication are confidential and reliable, anyway the positioning information could be corrupted. Usually, vehicles get the positioning information through a secondary wireless network system such as GPS. Nevertheless, the widespread civil GPS is extremely fragile in adversarial scenarios. An insecure distance or position estimation could produce security problems such as unauthorized accesses, denial of service, thefts, integrity disruption with possible safety implications and intentional disasters. In this dissertation, we face these three problems, proposing an original solution for each one