This paper proposes a Gaussian mixture model-based (GMM) bearing fault band selection (GMM-WBBS) method for signal processing. The proposed method benefits reliable feature extraction using fault frequency oriented Gaussian mixture model (GMM) window series. Selecting exclusively bearing fault frequency harmonics, it eliminates the interference of bearing normal vibrations in the lower frequencies, bearing natural frequencies, and the higher frequency contents that prove to be useful only for anomaly detection but do not provide any insight into the bearing fault location. The features are extracted from time- and frequency- domain signals that exclusively contain the bearing fault frequency harmonics. Classification is done using the Weighted KNN algorithm. The experiments performed with the data containing the vibrations recorded from artificially damaged bearings show the positive effect of utilizing the proposed GMM-WBBS signal processing to filter out the discriminative data of uncertain origin. All comparison methods retrofitted with the proposed method demonstrated classification performance improvements when provided with vibration data with suppressed bearing natural frequencies and higher frequency contents.

This paper proposes a new method for bearing fault diagnosis using wavelet packet transform (WPT)-based signal representation and informative factor linear discriminant analysis (IF-LDA). Time–frequency domain approaches for analyzing bearing vibration signals have gained wide acceptance due to their effectiveness in extracting information related to bearing health. WPT is a prominent method in this category, offering a balanced approach between short-time Fourier transform and empirical mode decomposition. However, the existing methods for bearing fault diagnosis often overlook the limitations of WPT regarding its dependence on the mother wavelet parameters for feature extraction. This work addresses this issue by introducing a novel signal representation method that employs WPT with a new rule for selecting the mother wavelet based on the power spectrum energy-to-entropy ratio of the reconstructed coefficients and a combination of the nodes from different WPT trees. Furthermore, an IF-LDA feature preprocessing technique is proposed, resulting in a highly sensitive set of features for bearing condition assessment. The k-nearest neighbors algorithm is employed as the classifier, and the proposed method is evaluated using datasets from Paderborn and Case Western Reserve universities. The performance of the proposed method demonstrates its effectiveness in bearing fault diagnosis, surpassing existing techniques in terms of fault identification and diagnosis performance.

A framework aimed to improve the bearing-fault diagnosis accuracy using a hybrid feature-selection method based on Wrapper-WPT is proposed in this paper. In the first step, the envelope vibration signal of the roller bearing is provided to the Wrapper-WPT. There, it is initially decomposed into several sub-bands using Wavelet Packet Transform (WPT), and a set out of nineteen time and frequency domain features are individually extracted from each sub-band of the decomposed vibration signal forming a wide feature pool. In the following step, Wrapper-WPT constructs a final feature vector using the Boruta algorithm, which selects the most discriminant features from the wide feature pool based on the important metric obtained from the Random Forest classifier. Finally, Subspace k-NN is used to identify the health conditions of the bearing, thus forming a hybrid signal processing and machine learning-based model for bearing fault diagnosis. In comparison with other state-of-the-art methods, the proposed method showed higher classification performance on two different bearing-benchmark vibration datasets with variable operating conditions.

Reliable condition monitoring of milling machines (MMs) is essential for maintaining quality of the output product and avoiding downtime, but traditional acoustic emission (AE)-based approaches using features such as root mean square (RMS), kurtosis, or band-limited energies either blur burst events with background activity or rely on ad hoc thresholding of AE hits. As a result, rotation-synchronous AE bursts generated by tool wear, bearing damage, and gear defects are incompletely captured and difficult to exploit for robust multi-fault diagnosis. This article presents Katz fractal dimension AE event scanner (KFD-AEEScan), a single-sensor diagnostic framework that processes AE signals into time-scale maps of local fractal complexity, replacing purely amplitude- or energy-based traces with a multiscale representation more sensitive to transient burst activity. A sliding-window Katz fractal-dimension estimator is applied at multiple window lengths, producing KFD traces in which burst-dominated regions gravitate toward the lower bound of KFD, while quasi-stationary background occupies higher-complexity levels. These traces are synchronized and stacked into compact two-dimensional time-scale maps, in which multiple KFD scales jointly encode the prominence and timing of AE bursts along each cutting pass. The framework is validated on real milling-machine AE data under multiple spindle speeds and four health states: normal, cutting tool wear, spindle bearing fault, and gearbox fault. KFD-AEEScan achieves high, well-balanced macro-F1 across all classes and outperforms representative wavelet-fractal, short-time Fourier transform-based, and Variational Mode Decomposition (VMD)-fractal baselines. The results indicate that localized KFD mapping of AE bursts provides an effective representation for multi-fault diagnosis and predictive maintenance in MMs.