FFT-2PCA: A New Feature Extraction Method for Data-Based Fault Detection

The industrial environment requires constant attention for faults on processes. This concern has central importance both for workers safety and process efficiency. Modern Process Automation Systems are capable of produce a large amount of data; upon this data, machine learning algorithms can be trained to detect faults. However, this data high complexity and dimensionality causes a decrease in these algorithms quality metrics. In this work, we introduce a new feature extraction method to improve the quality metrics of data-based fault detection. Our method uses a Fast Fourier Transform (FFT) to extract a temporal signature from the input data, to reduce the feature dimensionality generated by signature extraction, we apply a sequence of Principal Component Analysis (PCA). Then, the feature extraction output feeds a classification algorithm. We achieve an overall improvement of 17.4% on F1 metric for the ANN classifier. Also, due to intrinsic FFT characteristics, we verified a meaningful reduction in development time for data-based fault detection solution.