Engineering topics

Smyth, Padhraic J.

Publications and source records attributed to Smyth, Padhraic J..

9 records

NASA NTRS1995

Hidden Markov models for fault detection in dynamic systems

The invention is a system failure monitoring method and apparatus which learns the symptom-fault mapping directly from training data. The invention first estimates the state of the system at discrete intervals in time. A feature vector x of dimension k is estimated from sets of successive windows of sensor data. A pattern recognition component then models the instantaneous estimate of the posterior class probability given the features, p(w(sub i) (vertical bar)/x), 1 less than or equal to i isless than or equal to m. Finally, a hidden Markov model is used to take advantage of temporal context and estimate class probabilities conditioned on recent past history. In this hierarchical pattern of information flow, the time series data is transformed and mapped into a categorical representation (the fault classes) and integrated over time to enable robust decision-making.

Smyth, Padhraic J.↗

NASA NTRS1995

Pseudogradient Training For A Class Of Neural Networks

Developmental second-order recurrent neural networks of special type modified to enhance stability in face of inputs beyond range of inputs on which trained. Second-order recurrent neural networks contain product feedback units and can be trained, by use of example inputs and outputs, to act as finite-state automatons. Particular second-order recurrent neural networks in question learn grammars in sense they are trained to generate binary responses to input training sequences of ones and zeros, each sequence being marked "legal" or "illegal" according to grammar to be learned.

Zeng, Zheng↗

NASA NTRS1995

Detecting Faults By Use Of Hidden Markov Models

Frequency of false alarms reduced. Faults in complicated dynamic system (e.g., antenna-aiming system, telecommunication network, or human heart) detected automatically by method of automated, continuous monitoring. Obtains time-series data by sampling multiple sensor outputs at discrete intervals of t and processes data via algorithm determining whether system in normal or faulty state. Algorithm implements, among other things, hidden first-order temporal Markov model of states of system. Mathematical model of dynamics of system not needed. Present method is "prior" method mentioned in "Improved Hidden-Markov-Model Method of Detecting Faults" (NPO-18982).

Smyth, Padhraic J.↗

NASA NTRS1994

Automated Induction Of Rule-Based Neural Networks

Prototype expert systems implemented in software and are functionally equivalent to neural networks set up automatically and placed into operation within minutes following information-theoretic approach to automated acquisition of knowledge from large example data bases. Approach based largely on use of ITRULE computer program.

Smyth, Padhraic J.↗

NASA NTRS1994

Improved Hidden-Markov-Model Method Of Detecting Faults

Method of automated, continuous monitoring to detect faults in complicated dynamic system based on hidden-Markov-model (HMM) approach. Simpler than another, recently proposed HMM method, but retains advantages of that method, including low susceptibility to false alarms, no need for mathematical model of dynamics of system under normal or faulty conditions, and ability to detect subtle changes in characteristics of monitored signals. Examples of systems monitored by use of this method include motors, turbines, and pumps critical in their applications; chemical-processing plants; powerplants; and biomedical systems.

Smyth, Padhraic J.↗

NASA NTRS1993

Hidden Markov models for fault detection in dynamic systems

The invention is a system failure monitoring method and apparatus which learns the symptom-fault mapping directly from training data. The invention first estimates the state of the system at discrete intervals in time. A feature vector x of dimension k is estimated from sets of successive windows of sensor data. A pattern recognition component then models the instantaneous estimate of the posterior class probability given the features, p(w(sub i) perpendicular to x), 1 less than or equal to i is less than or equal to m. Finally, a hidden Markov model is used to take advantage of temporal context and estimate class probabilities conditioned on recent past history. In this hierarchical pattern of information flow, the time series data is transformed and mapped into a categorical representation (the fault classes) and integrated over time to enable robust decision-making.

Smyth, Padhraic J.↗

NASA NTRS1992

Entropy-Based Bounds On Redundancies Of Huffman Codes

Report presents extension of theory of redundancy of binary prefix code of Huffman type which includes derivation of variety of bounds expressed in terms of entropy of source and size of alphabet. Recent developments yielded bounds on redundancy of Huffman code in terms of probabilities of various components in source alphabet. In practice, redundancies of optimal prefix codes often closer to 0 than to 1.

Smyth, Padhraic J.↗

NASA NTRS1992

Evaluation Of Threshold Functions For Searches Among Data

Paper presents information-theoretical analysis of problem of building system of inferences from large collection of data (data base). More specifically, problem to design searches of data base for informative conjunctive production rules. Results of analysis may lead to design of more-economical and more-effective electronic neural networks drawing inferences from data bases.

Goodman, Rodney M.↗

NASA NTRS1991

Algorithm Derives Rules From Data

Set of best rules based on quantitative measure. ITRULE (Information Theoretic RULE induction) algorithm induces rules from examples in data base. Algorithm not only learns rules for given concept (classification) but also simultaneously learns rules relating multiple concepts.

Smyth, Padhraic J.↗