David G. Robinson

Impact of Distributed Energy Resources on the Reliability of Critical Telecommunications Facilities

This paper documents a probabilistic risk assessment of existing and alternative power supply systems at a large telecommunications office. The analysis characterizes the increase in the reliability of power supply through the use of two alternative power configurations. Failures in the power systems supporting major telecommunications service nodes are a main contributor to significant telecommunications outages. A logical approach to improving the robustness of telecommunication facilities is to increase the depth and breadth of technologies available to restore power during power outages. Distributed energy resources such as fuel cells and gas turbines could provide additional on-site electric power sources to provide backup power, if batteries and diesel generators fail. The analysis is based on a hierarchical Bayesian approach and focuses on the failure probability associated with each of three possible facility configurations, along with assessment of the uncertainty or confidence level in the probability of failure. A risk-based characterization of final best configuration is presented

A Hierarchial Bayes Approach to System Reliability Analysis

The Comprehensive Test Ban Treaty of 1996 banned any future nuclear explosions or testing of nuclear weapons and created the CTBTO in Vienna to implement the treaty. The U.S. response to this was the cessation of all above and below ground nuclear testing. As such, all stockpile reliability assessments are now based on periodic testing of subsystems being stored in a wide variety of environments. This data provides a wealth of information and feeds a growing web of deterministic, physics-based computer models for assessment of stockpile reliability. Unfortunately until 1996 it was difficult to relate the deterministic materials aging test data to component reliability. Since that time we have made great strides in mathematical techniques and computer tools that permit explicit relationships between materials degradation, e.g. corrosion, thermo-mechanical fatigue, and reliability. The resulting suite of tools is known as CRAX and the mathematical library supporting these tools is Cassandra. However, these techniques ignore the historical data that is also available on similar systems in the nuclear stockpile, the DoD weapons complex and even in commercial applications. Traditional statistical techniques commonly used in classical re liability assessment do not permit data from these sources to be easily included in the overall assessment of system reliability. An older, alternative approach based on Bayesian probability theory permits the inclusion of data from all applicable sources. Data from a variety of sources is brought together in a logical fashion through the repeated application of inductive mathematics. This research brings together existing mathematical methods, modifies and expands those techniques as required, permitting data from a wide variety of sources to be combined in a logical fashion to increase the confidence in the reliability assessment of the nuclear weapons stockpile. The application of this research is limited to those systems composed of discrete components, e.g. those that can be characterized as operating or not operating. However, there is nothing unique about the underlying principles and the extension to continuous subsystem/systems is straightforward. The framework is also laid for the consideration of systems with multiple correlated failure modes. While an important consideration, time and resources limited the specific demonstration of these methods.

Statistical Language Analysis for Automatic Exfiltration Event Detection

This paper discusses the recent development a statistical approach for the automatic identification of anomalous network activity that is characteristic of exfiltration events. This approach is based on the language processing method eferred to as latent dirichlet allocation (LDA). Cyber security experts currently depend heavily on a rule-based framework for initial detection of suspect network events. The application of the rule set typically results in an extensive list of uspect network events that are then further explored manually for suspicious activity. The ability to identify anomalous network events is heavily dependent on the experience of the security personnel wading through the network log. Limitations f this approach are clear: rule-based systems only apply to exfiltration behavior that has previously been observed, and experienced cyber security personnel are rare commodities. Since the new methodology is not a discrete rule-based pproach, it is more difficult for an insider to disguise the exfiltration events. A further benefit is that the methodology provides a risk-based approach that can be implemented in a continuous, dynamic or evolutionary fashion. This permits uspect network activity to be identified early with a quantifiable risk associated with decision making when responding to suspicious activity.

Reliability model development for photovoltaic connector lifetime prediction capabilities

This paper describes efforts to characterize different aspects of photovoltaic connector reliability. The resistance variation over a population of connections was examined by measuring 75 connectors from three different manufacturers. The comparison shows differences in average resistance of up to 9% between manufacturers. The standard deviation of resistance among the same manufacturer ranged from 6%-11%. In a separate experiment, the corrosive effects of grime on the connector pins during damp heat accelerated testing at 85°C/85% RH were studied. We observed a small resistance increase in the first 100 hours of damp heat and no further changes up to the current 450 hours of available data. With the exception of one connector, the effects of grime on connector performance during accelerated testing could not be measured during this time period.

Automated account reconciliation using probabilistic and statistical techniques

Purpose - The purpose of the present paper is to investigate how methods from statistics, natural language processing, information theory, and other scientific fields can be brought to bear on account reconciliation. Practically, the goal is to reduce the number of labor hours it takes to complete a task which is widespread in various subfields of accounting including fraud investigation. Design/methodology/approach - In this paper, the authors explore novel applications of data mining techniques from natural language processing and statistics to a particular account reconciliation problem. The authors are careful to avoid Findings - The paper finds that with careful tuning, it is possible to achieve near 100 percent precision (suggesting that the technique is highly accurate compared with an expert human reconciliation clerk) and close to 100 percent recall. Originality/value - The current approach, unlike many previous approaches, looks to general principles of information theory rather than relying on heuristics which may work for one problem but not another. This approach is therefore highly general, and would apply to virtually any kind of accounting data (including even data where transaction descriptions are in a language other than English).

Preliminary Results on Uncertainty Quantification for Pattern Analytics

This report summarizes preliminary research into uncertainty quantification for pattern ana- lytics within the context of the Pattern Analytics to Support High-Performance Exploitation and Reasoning (PANTHER) project. The primary focus of PANTHER was to make large quantities of remote sensing data searchable by analysts. The work described in this re- port adds nuance to both the initial data preparation steps and the search process. Search queries are transformed from does the specified pattern exist in the data? to how certain is the system that the returned results match the query? We show example results for both data processing and search, and discuss a number of possible improvements for each.

A Bayesian approach for health monitoring of critical systems

Bayesian medical monitoring is a concept based on using real-time performance-related data to make statistical predictions about a patients future health. The following paper discusses the fundamentals behind the medical monitoring concept and the application to monitoring the health of nuclear reactors. Necessary assumptions are discussed regarding distributions and failure-rate calculations. A simple example is performed to illustrate the effectiveness of the methods. The methods perform very well for the thirteen subjects in the example, with a clear failure sequence identified for eleven of the subjects

A Bayesian Approach for Aggregating Test Data Across Sub-Populations

In the process of conducting a reliability analysis of a system, quite often the population of interest is not homogenous; consisting of sub-populations which arise as production operations are adjusted, component suppliers are changed, etc. While these sub-populations are each unique in many ways, they also have much in common. It is also common for data to be available from a variety of different test regimes, e.g. environmental testing and fleet maintenance observations. Hierarchical Bayesian methods provide an organized, objective means of estimating the reliability of the individual systems, the sub-population reliability as well as the reliability of the entire population. This paper provides an introduction to a Bayesian approach that can be extended for more complicated situations.