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.
Abstract
This research presents an integrated condition monitoring framework for deep groove ball bearings by combining Complex Morlet Wavelet (CMW) analysis, machine learning techniques, thermographic analysis, and SKF Machine Condition Advisor tools. The proposed methodology employs Fast Fourier Transform (FFT) and Complex Morlet Wavelet-based vibration signal processing to extract discriminative time–frequency features for the early detection and diagnosis of bearing faults under both single and combined fault conditions. To evaluate fault classification performance, three machine learning algorithms, namely Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Random Forest (RF), are implemented and comparatively analyzed. Experimental investigations are conducted on a laboratory-scale bearing test rig operating under controlled conditions. The extracted wavelet-based features effectively characterize fault-induced vibration signatures, enabling accurate fault identification and classification. Comparative results indicate that Random Forest achieves the highest classification accuracy, followed by SVM and ANN. The average classification accuracies obtained using RF, SVM, and ANN are 97.48%, 95.27%, and 87.20%, respectively, demonstrating the superior robustness and generalization capability of the ensemble learning approach. Furthermore, thermographic analysis and SKF Machine Condition Advisor measurements provide complementary information for validating fault severity and machine health conditions, thereby enhancing diagnostic reliability. Although the present study is limited to constant-speed operation and controlled laboratory environments, the proposed framework demonstrates significant potential for predictive maintenance and intelligent condition monitoring applications. The integration of advanced time–frequency analysis, machine learning-based fault classification, and practical condition monitoring tools offers an effective and reliable solution for machinery health assessment in industrial environments.
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.
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
To address the issues of rolling bearing fault vibration signals being susceptible to noise interference and the support vector machine (SVM) relying on manual parameter settings, this paper proposes a fault diagnosis method based on ICEEMDAN and AIHO−SVM. Firstly, the Hippopotamus Optimization Algorithm is improved by incorporating Chebyshev chaotic mapping, refraction opposite learning, dynamic weighting, adaptive step size, and guided learning strategies, thereby enhancing convergence accuracy and speed. Secondly, ICEEMDAN is employed to decompose vibration signals for noise reduction, and the effective intrinsic mode function (IMF) components are selected according to the mutual information criterion to reconstruct the signal. Nine time−domain statistical features are then extracted to construct the fault feature vector. Thirdly, AIHO is used to collaboratively optimize the penalty factor and kernel parameters of SVM, establishing an AIHO−SVM classification model. Finally, the proposed method is validated on bearing datasets from Case Western Reserve University and Huazhong University of Science and Technology. Experimental results show that the average diagnostic accuracies on the two datasets reach 99.07% and 99.09%, respectively, demonstrating the effectiveness of the proposed method for rolling bearing fault diagnosis.
Liping Wang, Yao-Zheng Zhao, Yan Chen et al.· Information· 0 citations
It is confirmed that traditional machine learning models, optimized through manual feature engineering, can provide a ‘high-precision, low-risk’ solution for bearing fault diagnosis and offers significant reference value for the intelligent operation of aero-engines and other industrial equipment.
Qianxi Ye, Pengfang Gao· The 2026 International Confe...· 0 citations
This paper proposes a vibration-based approach for real-time condition monitoring of Friction Stir Welding (FSW) tools, which are widely used in the marine and automotive industries. Conventional inspection techniques such as visual examination and endoscopy are not practicable during active welding operations. The Locally Weighted Learning (LWL) algorithm, a lazy learning method, is used to address this limitation. Vibration signals are collected from a PLC-controlled FSW machine under five tool conditions, statistical features are extracted from the raw data, and a J48 decision tree is applied for feature selection to reduce computational overhead. Classification performance is evaluated using three lazy learning algorithms K-star (K*), LWL, and k-Nearest Neighbour (kNN) with LWL yielding the best result. The previously reported best accuracy for the same FSW setup was 73.16% at 1400 rpm using Random Forest; the proposed LWL-based approach achieves 92% accuracy under identical conditions, enabling earlier detection of tool faults before they result in weld defects or component failures.
Jegadeeshwaran Rakkiyannan, Balachandar Krishnamurthy, L. Jakkamputi et al.· Machines· 0 citations
Accurate diagnosis of rolling bearing faults is critical to the reliability of industrial equipment. However, rolling bearings often operate under complex operating conditions, and with data imbalances and noise interference, fault diagnosis of them remains extremely challenging. To address these issues, a novel mode entropy knowledge machine (MEKM) framework for robust bearing fault diagnosis is proposed in this study. For MEKM, the mode entropy space is firstly constructed to decompose the vibration signal into intrinsic mode components, and the noise-resistant feature extraction and dimensionality reduction are realized by principal component analysis. Secondly, a fast classifier based on extreme learning machines is introduced, and its parameters are automatically adjusted through a particle swarm optimization to establish an adaptive extreme learning machine diagnosis model, ensuring optimal generalization under different load and speed levels. Then, a collaborative optimization paradigm is developed to coordinate mode entropy features and classifier parameters through fully automated learning, in which entropy-driven feature characterization guides the iterative refinement of decision boundaries, while classifier feedback dynamically improves the selectivity of entropy features. Finally, validation is performed on bearings with multiple operating conditions, and the results indicated that the MEKM outperformed conventional deep learning methods in terms of diagnostic accuracy and generalization ability. The work provides a theoretical basis and an industrially feasible solution for health monitoring of mechanical equipment.
Hongchuang Tan, Yiheng Su, Jiang Ding et al.· Journal of Dynamics Monitori...· 0 citations