Novelty detection versus traditional fault detection methods by Mohammad Saleh Sadooghi* in Crimson Publishers: Journal of Electronics and Communication
Novelty detection is a method that defines the existence of a defect in a system without the need for prior training by data corresponding to the defect and simply by training by the data corresponding to the “normal assumed” function of the system. In the other words, in this method, the condition monitoring system is trained by purely normal (assumed) data and further, announces any deviation of the new system data from normal data. Therefore, it can be said that this method can detect and reveal the occurrence of any kind of fault and phenomenon, provided that there is a footprint or effect on the data which is extracted from the operation of the system. In the other words, this method is inherently unlimited in identifying the existence of any defect or phenomenon in a system. However, the term “normal assumed” is used here, if the term is merely “normal”, it might be a challenge that if traditional methods (other methods) do not have the ability to identify the rare and emerging defects and the novelty detection method is also in the training phase, how the normality of data is confirmed. So, in this way, the data sets that the condition monitoring system trains with them is the “normal assumed” data of the system.
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