Fault detection and diagnosis schemes based on data-driven statistical modelling are highly dependent on an accurate and exhaustive feature extraction procedure to deliver a superior performance as a monitoring strategy. Otherwise conducted, a deficient feature extraction procedure leads to a monitoring structure widely deviated from normal operating conditions. If an operating state is not identified as it, an increment in false alarm rate would be evidenced whenever the process shifts towards that condition and the monitoring scheme triggers the abnormal condition warning. On the other hand, if two similar operating conditions could not be individualized i.e. to be identified as a single operating state, a lack of sensitivity for minor - yet typical - deviations would render a monitoring strategy with prominent misdetection rates.
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