Performance Evaluation of Daubechies Wavelet-Based Feature Extraction for Multi-State Remaining Useful Life Prediction in Roller Bearings Using Machine Learning Algorithms
The results show that the careful calibration of wavelet parameters, combined with an efficient machine learning model can provide a reliable solution for real-time machine health monitoring and predictive maintenance of rotating equipment.
Abstract
Determining the Remaining Useful Life (RUL) in roller bearings is of utmost importance in rotary machinery. Knowing the present state and acting before a failure occurs is the most important aspect in industrial setups. This research presents an effective methodology to determine the RUL state of roller bearings by successfully using different combinations of Daubechies order and decomposition levels of Wavelet Transforms and applying machine learning methods. A dataset comprising temperature and vibration signals collected from a roller bearing test rig was developed for this study. These signals were then filtered using Butterworth bandpass filter for vibration signal filtering and moving average filter for temperature signal filtering followed by splitting the signal into overlapping windows. Then the signals are subjected to Wavelet Packet Transform followed by statistical feature extraction. In the classification phase, machine learning models such as the Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Decision Tree (DT) and Naive Bayes (NB) were used to classify the RUL state in roller bearing. While analyzing different wavelet types (db1 to db10) through seven decomposition levels, this research determined that a db4 wavelet at the third level was identified as optimal for detecting RUL state in roller bearing. The results show that Support Vector Machine (SVM) classifier achieved maximum classification accuracy of 97.68 ± 0.64%, which is higher than the other classification models used in this study. These results show that the careful calibration of wavelet parameters, combined with an efficient machine learning model can provide a reliable solution for real-time machine health monitoring and predictive maintenance of rotating equipment.
This research presents an integrated condition monitoring framework for deep groove ball bearings by combining Complex Morlet Wavelet analysis, machine learning techniques, thermographic analysis, and SKF Machine Condition Advisor tools that demonstrates significant potential for predictive maintenance and intelligent condition monitoring applications.
M. Maurya, Chandrabhanu Malla, I. Panigrahi et al.· F1000Research· 0 citations
The findings affirm the efficacy of XGBoost in bearing fault classification and emphasise the diagnostic value of carefully selected time-domain features, as well as suggesting strong potential for deploying such models in real-time condition monitoring and predictive maintenance systems.
A. Bhende· Insight - Non-Destructive Te...· 0 citations
Accurate detection of faults within rotary machinery components is vital for assuring the structural reliability of equipment in manufacturing facilities and power generation systems. Automatic fault detection is an increasingly common approach whereby data acquired from sensors are analyzed by machine learning algorithms to distinguish between normal and faulty component states. Vibration signatures recorded from healthy and faulty ball bearings, taken from the Case Western Reserve University (CWRU) 12k Drive End database, were utilized in the present study. These vibration signatures were segmented into windows of 0.1 s duration. Standard statistical time-domain features (root mean square, kurtosis, peak value and crest factor) were extracted from each sample. A random forest (RF) classifier was trained on vibration data from healthy bearings and several types of faulty bearings, partitioned into training (70% of data) and testing (30%) sets. The RF classifier distinguished healthy from faulty bearings with 98.8% accuracy across ball, inner race, outer race and normal categories. Feature importance analysis indicated that RMS (50% importance) and peak value (28% importance) were the most influential contributors, together comprising about 78% of the decision making. The RF model further recorded a recall of 1.00 for the normal baseline category, indicating that healthy bearings were identified with zero false negatives. The results demonstrate that ensemble machine learning, combined with statistically significant time-domain features, provides a highly accurate and physically interpretable solution for industrial condition monitoring, serving as a foundation for more advanced rotor-dynamic fault detection.
Aiming at the non-stationary, nonlinear and noise-sensitive characteristics of rolling bearing vibration signals, as well as the low recognition accuracy of traditional deep learning in small-sample scenarios, this paper proposes a rolling bearing fault diagnosis method combining Continuous Wavelet Transform with Ridge Tracking (CWT-RT) and Multi-Scale Wavelet Scattering Network. First, Variational Mode Decomposition integrated with Cramer Von Misses statistic (VMD-CVM) is adopted to denoise the original signal and improve the signal-to-noise ratio. Then, CWT-RT is used to transform the denoised signal into time-frequency spectrograms for intuitive time-frequency feature representation. Multi-Scale Wavelet Scattering Network is further applied to extract multi-level structural features, which are fed into Multi-Layer Perceptron (MLP) to realize bearing fault identification. To eliminate data leakage, all original raw vibration files are split into training and test sets at a 7:3 ratio before sliding window sampling. Validation experiments on bearing datasets from South Ural State University, CWRU, and XJTU-SY show that the diagnostic accuracies on two small-sample conditions reach 98.78% and 98.09%, respectively. the 95% confidence intervals for the two accuracy values are [98.21%, 99.15%] and [97.43%, 98.67%], respectively. Across 10 repeated experiments, p-values < 0.001 confirm the statistical significance of the results. The model maintains high accuracy under different loads and noise levels (0/5/10/15 dB). Comparative and ablation experiments verify that the method has high diagnostic accuracy, strong noise robustness and superior small-sample learning ability, with each module effective, and the full-pipeline latency meets the real-time requirements of IoT edge deployment, providing support for industrial engineering applications.
Ling Hai, Lufan Wang, Wen Liu et al.· Information Technology and C...· 0 citations
Reliable fault diagnosis in rotating machinery is challenging due to the nonlinear and non-stationary nature of vibration signals. Although time–frequency analysis is widely used, it cannot capture the cross-scale coupling between amplitude-modulated (AM) and frequency-modulated (FM) components that carry essential diagnostic information. This study applies Holo-Hilbert Spectrum Analysis (HHSA) to extract amplitude–frequency modulation features and integrates them with six machine learning classifiers to identify four fault conditions. Random Forest, K-Nearest Neighbors, and Logistic Regression achieve accuracies of up to 99.95%, yielding higher accuracy than Fast Fourier Transform-based features. The proposed framework employs an HHSA-based feature extraction pipeline that effectively captures AM–FM coupling in nonlinear vibration signals. It also provides higher discriminative capability than traditional spectral approaches and maintains robustness across multiple classifiers. This method offers high diagnostic accuracy and strong potential for industrial predictive maintenance. Future work will focus on improving computational efficiency and evaluating the framework under more diverse and realistic operating conditions.
Received: 10 September 2025 | Revised: 20 April 2026 | Accepted: 10 June 2026
Conflicts of Interest
The authors declare that they have no conflicts of interest to this work.
Data Availability Statement
The VBL-VA001 datasets that support the findings of this study are openly available at https://doi.org/10.1007/s42417-023-00959-9, reference number [44].
Author Contribution Statement
Van-Trung Nguyen: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing - original draft, Writing - review & editing, Visualization. Ba-Tan Le: Investigation, Data curation, Writing - original draft, Writing - review & editing. Van-Phuong Dao: Methodology, Validation, Writing - review & editing.
Van-Trung Nguyen, Ba-Tan Le, van-Phuong Dao· Journal of Computational and...· 0 citations
Results show that using statistical vibration features with ensemble classifiers is a good way to diagnose multi-class bearing faults and establishes a comprehensive benchmark for ML- and DL-based rolling bearing FDD.
M. I. Quamar, Abdulrazaq Nafiu Abubakar, Ali Nasir· Journal of Vibration Enginee...· 0 citations
