Embodiments of the present invention provide devices and techniques for detecting anomalies in monitored environments. In one embodiment of the invention, data may be acquired by monitoring an environmental variable. Based on the acquired data, statistics may be calculated which model the behavior of the environmental variable. Based on the calculated statistics, a long-term behavior and a short-term behavior of the statistics may also be calculated. A difference between the long-term statistics and the short-term statistics may then be calculated. If the difference between the long-term statistical behavior and short-term statistical behavior exceeds a dynamic or predefined threshold, an action may be taken.
A computer implemented method, system and program product for automatic fault classification. A set of abnormal data can be automatically grouped based on sensor contribution to a prediction error. A principal component analysis (PCA) model of normal behavior can then be applied to a set of newly generated data, in response to automatically grouping the set of abnormal data based on the sensor contribution to the prediction error. Data points can then be identified, which are indicative of abnormal behavior. Such an identification step can occur in response to applying the principal component analysis mode of normal behavior to the set of newly generated data in order to cluster and classify the data points in order to automatically classify one or more faults thereof. The data points are automatically clustered, in order to identify a set of similar events, in response to identifying the data points indicative of abnormal behavior.
