Novel indicators for monitoring bearing condition using frequency-domain dictionary learning

Hannah Yun, Ciprian Doru Giurcăneanu, Gillian Dobbie

Abstract: We introduce a condition monitoring (CM) method that relies on spectral analysis of bearing vibration signals. Assuming that the signal is divided into non-overlapping segments, we design an automatic procedure to compare the estimated log-spectrum of a given segment with the log-spectra of past segments. The differences between the log-spectra of past segments and the current segment are stored as the columns of a matrix, which is factorized using dictionary learning (DL). The resulting matrix factors are the dictionary and the representation matrix. The size of the dictionary and the sparsity level of the representation matrix are selected based on an information theoretic criterion (ITC). We show that this factorization generalizes the factorization derived from the definition of the cepstrum. We define three new CM indicators: the ITC value, the estimated sparsity value, and the distance between the dictionary whose estimation involves the current segment and the dictionary estimated from the initial segments of the signal. We investigate the impact of the region of the frequency domain considered in the comparison and the norm used in the DL objective function on these indicators. The performance of the novel method is demonstrated through experiments conducted on three publicly available datasets representing various conditions: abrupt changes, gradual changes, and a combination of abrupt and gradual changes.

External IDs:doi:10.1007/s00170-025-16534-3