A theory of diagnosis from first principles
Artificial Intelligence
A Bayesian method for constructing Bayesian belief networks from databases
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Learning belief networks from data: an information theory based approach
CIKM '97 Proceedings of the sixth international conference on Information and knowledge management
Machine Learning - Special issue on learning with probabilistic representations
LAZY propagation: a junction tree inference algorithm based on lazy evaluation
Artificial Intelligence
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Learning Bayesian networks from data: an information-theory based approach
Artificial Intelligence
Analysis in HUGIN of data conflict
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
A differential approach to inference in Bayesian networks
Journal of the ACM (JACM)
A unifying framework for detecting outliers and change points from non-stationary time series data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Naive Bayes models for probability estimation
ICML '05 Proceedings of the 22nd international conference on Machine learning
A Probabilistic Model for Information and Sensor Validation
The Computer Journal
Discovering the hidden structure of complex dynamic systems
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Learning the structure of dynamic probabilistic networks
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
On the detection of conflicts in diagnostic Bayesian networks using abstraction
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
International Journal of Computer Applications in Technology
International Journal of Approximate Reasoning
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We present a new methodology for detecting faults and abnormal behavior in production plants. The methodology stems from a joint project with a Danish energy consortium. During the course of the project we encountered several problems that we believe are common for projects of this type. Most notably, there was a lack of both knowledge and data concerning possible faults, and it therefore turned out to be infeasible to learn/construct a standard classification model for doing fault detection. As an alternative we propose a method for doing on-line fault detection using only a model of normal system operation. Faults are detected by measuring the conflict between the model and the sensor readings, and knowledge about the possible faults is therefore not required. We illustrate the proposed method using real-world data from a coal driven power plant as well as simulated data from an oil production facility.