Anomaly detection is the problem of finding patterns in data that do not conform to an a priori expected behavior. This is related to the problem in which some samples are distant, in terms of a given metric, from the rest of the dataset, where these anomalous samples are indicated as outliers. Anomaly detection has recently attracted the attention of the research community, because of its relevance in real-world applications, like intrusion detection, fraud detection, fault detection and system health monitoring, among many others. Anomalies themselves can have a positive or negative nature, depending on their context and interpretation. However, in either case, it is important for decision makers to be able to detect them in order to take appropriate actions. The petroleum industry is one of the application contexts where these problems are present. The correct detection of such types of unusual information empowers the decision maker with the capacity to act on the system in order to correctly avoid, correct or react to the situations associated with them. In that application context, heavy extraction machines for pumping and generation operations, like turbomachines, are intensively monitored by hundreds of sensors each that send measurements with a high frequency for damage prevention. In this paper, we propose a combination of yet another segmentation algorithm (YASA), a novel fast and high quality segmentation algorithm, with a one-class support vector machine approach for efficient anomaly detection in turbomachines. The proposal is meant for dealing with the aforementioned task and to cope with the lack of labeled training data. As a result, we perform a series of empirical studies comparing our approach to other methods applied to benchmark problems and a real-life application related to oil platform turbomachinery anomaly detection.

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Currently design documentation rarely records the designer's decision process or the reasons behind those decisions. This paper describes an effort to improve design documentation by having the computer act as an intelligent apprentice to the designer to capture the rationale during the design process. The apprentice learns about the features that make a specific case different from the standard. Whenever the designer proposes a design action that differs from the apprentice's expectations, the interface will ask for the designer for justifications to explain the differences. Later queries for design rationale are answered using a combination of the apprentice's domain knowledge and the designer-supplied justifications. The apprentice model is being implemented in a prototype system called ADD (Augmenting Design Documentation). The initial focus of the work is on HVAC (Heating, Ventilation, and Air Conditioning) design. Our starting point for implementing the apprentice model is observing how people develop HVAC system designs and then explain those designs.

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