Anomaly detection for batch manufacturing via greybox feedback control models
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Authors
Vargas, Andrés
Issue Date
2019-08
Type
Electronic thesis
Thesis
Thesis
Language
ENG
Keywords
Mathematics
Alternative Title
Abstract
Our grey-box anomaly detection framework addresses the first three challenges of anomaly detection: anomaly concept ambiguity, class imbalance, and anomaly isolation. Anomaly concept ambiguity is addressed through a a novel anomaly scoring function. An anomaly scoring function is mathematical function that takes observations as input and outputs a real-valued quantity, called the anomaly score, that measures the anomalousness of the input. The anomaly scoring function defines the anomaly concept and must be carefully constructed to be appropriate, both mathematically and for the domain at hand. We define our anomaly score in a fashion that is both mathematically and domain appropriate. It is mathematically appropriate because it is defined to be the probability of the input observation occurring under a probability measure that defines non-anomalous data. This probability measure is learned by using an initial set of data that is assumed to be composed of non-anomalies. Our anomaly scoring function is domain-appropriate because it is constructed as a linear combination of a term that encodes sensor anomalousness and a term that encodes process anomalousness. Sensor anomalousness occurs when sensors are behaving differently from expected and process anomalousness occurs when the underlying data generating process is behaving differently from expected. Class imbalance is resolved by framing the anomaly detection task as a hypothesis test.
Description
August 2019
School of Science
School of Science
Full Citation
Publisher
Rensselaer Polytechnic Institute, Troy, NY