John W. Sheppard

Bio: John W. Sheppard is an academic researcher from Montana State University. The author has contributed to research in topics: Bayesian network & Evolutionary algorithm. The author has an hindex of 22, co-authored 199 publications receiving 2392 citations. Previous affiliations of John W. Sheppard include Johns Hopkins University & Sikorsky Aircraft.

A Formal Analysis of Fault Diagnosis with D-matrices

Abstract: As new approaches and algorithms are developed for system diagnosis, it is important to reflect on existing approaches to determine their strengths and weaknesses. Of concern is identifying potential reasons for false pulls during maintenance. Within the aerospace community, one approach to system diagnosis--based on the D-matrix derived from test dependency modeling--is used widely, yet little has been done to perform any theoretical assessment of the merits of the approach. Past assessments have been limited, largely, to empirical analysis and case studies. In this paper, we provide a theoretical assessment of the representation power of the D-matrix and suggest algorithms and model types for which the D-matrix is appropriate. We also prove a surprising result relative to the difficulty of generating optimal diagnostic strategies from D-matrices. Finally, we relate the processing of the D-matrix with several diagnostic approaches and suggest how to extend the power of the D-matrix to take advantage of the power of those approaches.

Method and apparatus for diagnostic testing including a neural network for determining testing sufficiency

Abstract: A diagnostic tester evaluates at least one inputted test signal corresponding to test data relating to at least one predetermined parameter of a system being tested, to produce first and second candidate signals corresponding respectively to first and second possible diagnoses of the condition of the system respectively having the first and second highest levels of certainty of being valid, and first and second certainty signals corresponding respectively to values of the first and second highest levels of certainty. The diagnostic tester further determines the sufficiency of the testing that has taken place responsive to the first and second certainty signals, and produces an output signal indicative of whether sufficient test data has been evaluated to declare a diagnosis. Preferably, an uncertainty signal corresponding to a measure of the uncertainty that the evaluated at least one test signal can be validly evaluated is also produced and used to produce the output signal.

Aircraft condition analysis and management system

Abstract: The invention provides a health management system and method for a complex system having at least one information source with data sources, an Aircraft Condition Analysis and Management system (ACAMS) for monitoring the data sources, an information controller for collecting and processing the data sources and a diagnostic/prognostic reasoner for fusing the collected data sources to establish current and future states and conditions of the complex system.

System test and diagnosis

Abstract: Part One: Motivation. 1. Introduction. 2. Maintainability: a Historical Perspective. 3. Field Diagnosis and Repair: the Problem. Part Two: Analysis and Application. 4. Bottom-Up Modeling for Diagnosis. 5. System Level Analysis for Diagnosis. 6. The Information Flow Model. 7. System Level Diagnosis. 8. Evaluating System Diagnosability. 9. Verification and Validation. 10. Architecture for System Diagnosis. Part Three: Advanced Topics. 11. Inexact Diagnosis. 12. Partitioning Large Problems. 13. Modeling Temporal Information. 14. Adaptive Diagnosis. 15. Diagnosis -- Art versus Science. References. Index.

Agent-Based Modeling of Retail Electrical Energy Markets With Demand Response

Abstract: In this paper, we study the behavior of a day-ahead (DA) retail electrical energy market with price-based demand response from air conditioning (AC) loads through a hierarchical multiagent framework, employing a machine learning approach. At the top level of the hierarchy, a retailer agent buys energy from the DA wholesale market and sells it to the consumers. The goal of the retailer agent is to maximize its profit by setting the optimal retail prices, considering the response of the price-sensitive loads. Upon receiving the retail prices, at the lower level of the hierarchy, the AC agents employ a ${Q}$ -learning algorithm to optimize their consumption patterns through modifying the temperature set-points of the devices, considering both consumption costs and users’ comfort preferences. Since the retailer agent does not have direct access to the AC loads’ underlying dynamics and decision process (i.e., incomplete information) the data privacy of the consumers becomes a source of uncertainty in the retailer’s decision model. The retailer relies on techniques from the field of machine learning to develop a reliable model of the aggregate behavior of the price-sensitive loads to reduce the uncertainty of the decision-making process. Hence, a multiagent framework based on machine learning enables us to address issues such as interoperability and decision-making under incomplete information in a system that maintains the data privacy of the consumers. We will show that using the proposed model, all the agents are able to optimize their behavior simultaneously. Simulation results show that the proposed approach leads to a reduction in overall power consumption cost as the system converges to its equilibrium. This also coincides with maximization in the retailer’s profit. We will also show that the same decision architecture can be used to reduce peak load to defer/avoid distribution system upgrades under high penetration of photo-voltaic power in the distribution feeder.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).