Resumo:
Accurate motor fault detection and flow rate estimation in industrial centrifugal pumps
is essential for diagnostics, performance monitoring, and process optimization, yet traditional
frequency-domain techniques such as Motor Current Signature Analysis (MCSA)
often rely on fixed spectral bands and assume stationarity, making them less effective
under variable-speed operation, changing loading conditions, and multiphase flow environments.
This work introduces a Koopman-theoretic framework for motor fault and
flow-rate classification based on Hankel-embedded Higher-Order Dynamic Mode Decomposition
(HODMD), enabling extraction of physically interpretable spectral-dynamical
features from multichannel current, voltage, and vibration measurements. The proposed
pipeline computes Koopman eigenvalues, modal energies, growth rates, temporal coherence,
and spectral entropy to construct a compact descriptor of pump electromechanical
behaviour, which is subsequently evaluated through supervised and unsupervised learning
pipelines incorporating SMOTE balancing, nonlinear UMAP projection, and extensive
hyperparameter optimization via TPESearchCV. Using 2 laboratory datasets, the first
consisting of 106 signals describing 4 motor faulty operation states with different input
frequencies, its results were as high as 96.8% cross validation accuracy and 80.7% balanced
test accuracy. The second dataset consists of 1304 signals spanning multiple rotational
speeds, heterogeneous fluid compositions, and 11 unbalanced flow-rate range classes, the
system achieves up to 97.8% cross-validation balanced accuracy and up to 92.6% balanced
test accuracy while revealing coherent manifold structure aligned with physical
flow-rate regimes. Although overlapping operating states and speed-dependent spectral
shifts introduce classification challenges, the results demonstrate that Koopman spectral
feature captures the essential dynamic structure of the pump more effectively than traditional
Fourier-based approaches, highlighting their potential for robust, reliable, and
generalizable monitoring and diagnostics of industrial pumping systems.
