Centrifugal Pump Fault Detection with Hybrid Feature Pool and Deep Learning

This research introduces an innovative hybrid technique for diagnosing faults in centrifugal pumps utilizing vibration sensor data. The initial signal, filtered through a low pass filter with a 4.6 kHz cutoff frequency, undergoes both Stockwell and continuous wavelet transformations concurrently. These transformations yield two sets of time-frequency scalograms as inputs for separate convolutional neural networks. These networks extract vital features from the scalograms, which are then amalgamated into a hybrid feature pool. Because the pool has many dimensions, principal component analysis is used to reduce the feature space to a manageable latent space while keeping the essential features from both sets of scalograms. Subsequently, a two-layer Artificial Neural Network is employed to categorize pump states based on this reduced-dimensional feature space. Impressively, our approach attains a classification accuracy of 95.60%, outperforming existing state-of-the-art methods. This shows how well our strategy works. It combines different transformation techniques, hybrid feature extraction, and dimensionality reduction to make a method for diagnosing centrifugal pump faults that is very effective and quick.