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Failure Modes, Effects, and Criticality Analysis Model disregards failures of insignificant components. Assumptions used to establish baselines for reliability prediction and analytical method are listed. Techniques are applicable to any commercial design and can be extended into software or management systems.
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This paper proposes an improved model for the reliability assessment of power systems considering component failures of variable energy resources (VER). The inherent intermittency of VER such as solar photovoltaic (PV) and wind farms, along with their susceptibility to component failures, present significant challenges to reliable system operation. These issues, combined with power grid operation and network constraints, complicate the reliable operation of VER-integrated power systems. Here, to address these concerns, this paper introduces a reliability assessment framework that considers VER input variability, its impact on component availability, and their resulting impact on overall system reliability. Stochastic models based on discrete Markov processes are developed to incorporate variable irradiance, wind speeds, and their effects on PV and wind component failure rates. A next-event and state transition-based approach is then developed to integrate the stochastic models into a mixed-timing sequential Monte Carlo simulation framework for composite reliability assessment. Case studies on the RTS-GMLC system demonstrate the effectiveness of the proposed model in evaluating the reliability of VER-integrated systems.
Preliminary power system configuration tradeoffs to select optimized systems with respect to reliability and weight - manual
The objective of the study is to assess pathways for India to achieve its 2070 power sector goals reliably and cost-effectively by considering various supply- and demand-side factors to support planning by national stakeholders including the Central Electricity Authority (CEA).
Design and reliability of digital systems for hard cores of fault tolerant computers /hybrid- redundant systems/, discussing advantages over multiplexed systems
A corona vacuum test facility for nondestructive testing of power system components was built in the Reliability and Quality Engineering Test Laboratories at the NASA Lewis Research Center. The facility was developed to simulate operating temperature and vacuum while monitoring corona discharges with residual gases. The facility is being used to test various high-voltage power system components.
The reliability of 45 state-of-the-art strain gage systems under full scale engine testing was investigated. The flame spray process was used to install 23 systems on the first fan rotor of a YF-100 engine; the others were epoxy cemented. A total of 56 percent of the systems failed in 11 hours of engine operation. Flame spray system failures were primarily due to high gage resistance, probably caused by high stress levels. Epoxy system failures were principally erosion failures, but only on the concave side of the blade. Lead-wire failures between the blade-to-disk jump and the control room could not be analyzed.
The objective was to search for and demonstrate approaches and concepts for fast wafer probe tests of mechanisms affecting the reliability of MOS technology and, based on these, develop and optimize test chips and test procedures. Progress is reported on four important wafer-level reliability problems: gate-oxide radiation hardness; hot-electron effects; time-dependence dielectric breakdown; and electromigration.
The growing interest in onsite solar photovoltaic and energy storage systems is partially motivated by customer concerns regarding grid reliability. However, accurately assessing the effectiveness of PVESS in mitigating these interruptions requires a comprehensive understanding of location-specific outage patterns and the ability to simulate realistic scenarios. To address the gap, we introduce the Power Reliability Event Simulation TOol (PRESTO), the first publicly available tool that simulates location-specific power interruptions at the county level. PRESTO allows for a more realistic assessment of system reliability by considering the unpredictability and location-specific patterns of power interruptions. We applied PRESTO in a case study of a single-family home across three U.S. counties, examining the performance of a solar photovoltaic system with 10kWh of battery storage during short-duration power interruptions. Our findings show that this system reliably met 93% of energy demand for essential non-heating and cooling loads, fully serving these loads in 84% of events, despite the constraints of daily time-of-use bill management which limits the battery's state-of-charge reserve. However, when heating and cooling loads were included, system performance decreased significantly, with only 70% of demand met and full service in 43% of events. These results highlight the challenges of using solar photovoltaic and energy storage systems for short-duration outages, emphasizing the need to consider factors like battery size and grid charging strategies to improve reliability. Our study demonstrates the practical applications of PRESTO, providing valuable insights into potential mitigation strategies including grid charging and optimizing battery size.
By viewing a composite lamina as a homogeneous solid whose directional strengths are random variables, lamina reliability under multiaxial stresses may be determined using either an interactive or a noninteractive criterion. From the reliability values for the individual laminae comprising a given laminate, Thomas and Wetherhold (1991) have proposed a method for determining bounds for the overall laminate reliability. In this paper, simple physically plausible phenomenological rules are proposed for redistribution of load after a lamina has failed within the confines of a laminate. These rules are illustrated by application to (0/ +/-15)s and (90/ +/-45/0)s graphite/epoxy laminates, and the results are compared to the previously proposed bounds.
A methodology is developed and applied for quantitatively analyzing the reliability of a dual, fail-operational redundant strapdown inertial measurement unit (RSDIMU). A Markov evaluation model is defined in terms of the operational states of the RSDIMU to predict system reliability. A 27 state model is defined based upon a candidate redundancy management system which can detect and isolate a spectrum of failure magnitudes. The results of parametric studies are presented which show the effect on reliability of the gyro failure rate, both the gyro and accelerometer failure rates together, false alarms, probability of failure detection, probability of failure isolation, and probability of damage effects and mission time. A technique is developed and evaluated for generating dynamic thresholds for detecting and isolating failures of the dual, separated IMU. Special emphasis is given to the detection of multiple, nonconcurrent failures. Digital simulation time histories are presented which show the thresholds obtained and their effectiveness in detecting and isolating sensor failures.
RAM program determines probability of success for one or more given objectives in any complex system. Program includes failure mode and effects, criticality and reliability analyses, and some aspects of operations, safety, flight technology, systems design engineering, and configuration analyses.
A composite lamina may be viewed as a homogeneous solid whose directional strengths are random variables. Calculation of the lamina reliability under a multi-axial stress state can be approached by either assuming that the strengths act separately (modal or independent action), or that they interact through a quadratic interaction criterion. The independent action reliability may be calculated in closed form, while interactive criteria require simulations; there is currently insufficient data to make a final determination of preference between them. Using independent action for illustration purposes, the lamina reliability may be plotted in either stress space or in a non-dimensional representation. For the typical laminated plate structure, the individual lamina reliabilities may be combined in order to produce formal upper and lower bounds of reliability for the laminate, similar in nature to the bounds on properties produced from variational elastic methods. These bounds are illustrated for a (0/plus or minus 15)sub s Graphite/Epoxy (GR/EP) laminate. And addition, simple physically plausible phenomenological rules are proposed for redistribution of load after a lamina has failed. These rules are illustrated by application to (0/plus or minus 15)sub s and (90/plus or minus 45/0)sub s GR/EP laminates and results are compared with respect to the proposed bounds.
HRA is a method used to describe, qualitatively and quantitatively, the occurrence of human failures in the operation of complex systems that affect availability and reliability. Modeling human actions with their corresponding failure in a PRA (Probabilistic Risk Assessment) provides a more complete picture of the risk and risk contributions. A high quality HRA can provide valuable information on potential areas for improvement, including training, procedural, equipment design and need for automation.
Companies at risk of accidents caused by human error that result in catastrophic consequences include: airline industry mishaps, medical malpractice, medication mistakes, aerospace failures, major oil spills, transportation mishaps, power production failures and manufacturing facility incidents. Human Reliability Assessment (HRA) is used to analyze the inherent risk of human behavior or actions introducing errors into the operation of a system or process. These assessments can be used to identify where errors are most likely to arise and the potential risks involved if they do occur. Using the basic concepts of HRA, an evolving group of methodologies are used to meet various industry needs. Determining which methodology or combination of techniques will provide a quality human reliability assessment is a key element to developing effective strategies for understanding and dealing with risks caused by human errors. There are a number of concerns and difficulties in "tailoring" a Human Reliability Assessment (HRA) for different industries. Although a variety of HRA methodologies are available to analyze human error events, determining the most appropriate tools to provide the most useful results can depend on industry specific cultures and requirements. Methodology selection may be based on a variety of factors that include: 1) how people act and react in different industries, 2) expectations based on industry standards, 3) factors that influence how the human errors could occur such as tasks, tools, environment, workplace, support, training and procedure, 4) type and availability of data, 5) how the industry views risk & reliability, and 6) types of emergencies, contingencies and routine tasks. Other considerations for methodology selection should be based on what information is needed from the assessment. If the principal concern is determination of the primary risk factors contributing to the potential human error, a more detailed analysis method may be employed versus a requirement to provide a numerical value as part of a probabilistic risk assessment. Industries involved with humans operating large equipment or transport systems (ex. railroads or airlines) would have more need to address the man machine interface than medical workers administering medications. Human error occurs in every industry; in most cases the consequences are relatively benign and occasionally beneficial. In cases where the results can have disastrous consequences, the use of Human Reliability techniques to identify and classify the risk of human errors allows a company more opportunities to mitigate or eliminate these types of risks and prevent costly tragedies.
The modernization of existing and new nuclear power plants with digital instrumentation and control systems (DI&C) is a recent and highly trending topic. However, there lacks strong consensus on best-estimate reliability methodologies by both the United States (U.S.) Nuclear Regulatory Commission (NRC) and the industry. This has resulted in hesitation for further modernization projects until a more unified methodology is realized. In this work, we develop an approach called Orthogonal-defect Classification for Assessing Software Reliability (ORCAS) to quantify probabilities of various software failure modes in a DI&C system. The method utilizes accepted industry methodologies for software quality assurance that are also verified by experimental or mathematical formulations. In essence, the approach combines a semantic failure classification model with a reliability growth model to predict (and quantify) the potential failure modes of a DI&C software system. The semantic classification model is used to address the question: How do latent defects in software contribute to different software failure root causes? The use of reliability growth models is then used to address the question: Given the connection between latent defects and software failure root causes, how can we quantify the reliability of the software? A case study was conducted on a representative I&C platform (ChibiOS) running a smart sensor acquisition software developed by Virginia Commonwealth University (VCU). The testing and evidence collection guidance in ORCAS was applied, and defects were uncovered in the software. Qualitative evidence, such as condition coverage, was used to gauge the completeness and trustworthiness of the assessment while quantitative evidence was used to determine the software failure probabilities. The reliability of the software was then estimated and compared to existing operational data of the sensor device. It is demonstrated that by using ORCAS, a semantic reasoning framework can be developed to justify if the software is reliable (or unreliable) while still leveraging the strength of the existing methods.
The TIGER algorithm, the inputs to the program and the output are described. TIGER is a computer program designed to simulate a system over a period of time to evaluate system reliability and availability. Results can be used in the Deep Space Network for initial spares provisioning and system evaluation.