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.