Wavelet  machine learning model for condition monitoring of ball bearings

Performance Evaluation of Daubechies Wavelet-Based Feature Extraction for Multi-State Remaining Useful Life Prediction in Roller Bearings Using Machine Learning Algorithms

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.

Multi-Class Fault Detection and Diagnosis of Rolling Bearings: a Machine Learning Approach

A Rolling Bearing Fault Diagnosis Method Based on ICEEMDAN and AHO−SVM

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.

Machine learning-based bearing failure research

To ensure the safe operation of aircraft engine bearings under extreme conditions such as high temperatures, high pressures, and high-speed rotation, and to address their susceptibility to failure, this study explores a machine learning-based bearing fault diagnosis method. The core of the research lies in enhancing diagnostic accuracy through effective feature engineering strategies: first, multidimensional features are extracted from both the time and frequency domains of bearing vibration signals; subsequently, key features are selected using variance analysis and the Gini coefficient, with Principal Component Analysis employed for dimensionality reduction to retain core information. Performance comparisons of models including One-Dimensional convolutional neural networks, logistic regression, random forests, and gradient-boosted trees demonstrated that random forests combined with Gini coefficient feature selection achieved optimal results. This approach attained an exceptionally high accuracy of 0.9872 on the test set while exhibiting robust generalisation capabilities. This research confirms that traditional machine learning models, optimized through manual feature engineering, can provide a ‘high-precision, low-risk’ solution for bearing fault diagnosis. It offers significant reference value for the intelligent operation and maintenance of aero-engines and other industrial equipment.

Vibration Signal-Based Fault Detection and Classification in Friction Stir Welding Process Using Statistical Features and Lazy Learning Classifiers

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.

Mode entropy knowledge machine: a fully automated bearing fault diagnosis model for complex operating conditions

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.