The paper presents a fast, accurate and simple systematic approach for online condition monitoring and severity identification of ball bearings. This approach utilizes compact 1D convolutional neural networks (CNNs) to identify, quantify, and localize bearing damage. The proposed approach is verified experimentally under several single and multiple damage scenarios. The experimental results demonstrated that the proposed approach can achieve a high level of accuracy for damage detection, localization and quantification. Besides its real-time processing ability and superior robustness against the high-level noise presence, the compact and minimally-trained 1D CNNs in the core of the proposed approach can handle new damage scenarios with utmost accuracy.

Robust and real-time detection of faults has become an ultimate objective for predictive maintenance on rotating machinery. Vibration-based Deep Learning (DL) methodologies have become the de facto standard for bearing fault detection as they can produce state-of-the-art detection performances under certain conditions. Despite such particular focus on the vibration signal, the utilization of sound, on the other hand, has been widely neglected. As a result, no large-scale benchmark motor fault dataset exists with both sound and vibration data. The novel and significant contributions of this study can be summarized as follows. This study presents and publically shares the Qatar University Dual-Machine Bearing Fault Benchmark dataset (QU-DMBF), which encapsulates sound and vibration data from two different motors operating under 1080 working conditions. Then, we focus on the major limitations and drawbacks of vibration-based fault detection due to numerous installation and operational conditions. Finally, we propose the first DL approach for sound-based fault detection and perform comparative evaluations between the sound and vibration signals over the QU-DMBF dataset. A wide range of experimental results shows that the sound-based fault detection method is significantly more robust than its vibration-based counterpart, as it is entirely independent of the sensor location, cost-effective (requiring no sensor and sensor maintenance), and can achieve the same level of the best detection performance by its vibration-based counterpart. This study publicly shares the QU-DMBF dataset, the optimized source codes in PyTorch, and comparative evaluations with the research community.

Early detection of tooth cracks is crucial for effective condition‐based monitoring and decision making. The scope of this work was to bring more insight into the vibration behavior of spur gears in the presence of single and multiple simultaneous tooth cracks. The investigation was conducted in both time and frequency domains. A finite element analysis was performed to determine the variation in stiffness with respect to the angular position for different combinations of crack lengths. A simplified nonlinear lumped parameter model of a one‐stage gearbox with six degrees of freedom was then developed to simulate the vibration response of faulty external spur gears. Four different multiple‐crack scenarios were proposed and studied. The performances of various statistical fault detection indicators were considered and investigated. The simulation results obtained via MATLAB indicated that, as the severity of a single crack increases, the values of the time domain statistical indicators increase also, but at different rates. Moreover, the number of cracks was found to have a negative effect on the values of all the performance indicators, except for the RMS. The number and amplitude of the sidebands in the frequency spectrum were also considered, while assessing the severity of the faults in each scenario. It was observed that, in the case of consecutive tooth cracks, the number of spectrum peaks and the number of cracks were consistent in the frequency range of 4‐5 kHz. The main finding of this study was that the peak spectral amplitude was the most sensitive indicator of the number and severity of cracks.

In rotating machinery, the second most common fault after imbalance is misalignment. Misalignment can have a severe impact on equipment and may reduce the machine’s lifetime considerably. In this paper, the simultaneous effect of imbalance and misalignment (parallel or angular) on the vibration spectra of rotating machinery will be discussed. A numerical model is developed and used to obtain the time and frequency responses of the rotor-coupling-bearing system to the simultaneous effect of these faults. The numerical model shows that the imbalance was mainly related to the peak located around 1X, whereas misalignment was linked to the peak around 2X. In addition, the parallel misalignment fault magnifies the 2X amplitude of the displacement response, whereas the response of angular misalignment is captured at the 2X and 4X amplitudes. This study also examines the effects of changing the model’s rotational speed, misalignment level, and coupling type for angular and parallel misalignments.

This article presents a new concept design for magnetorheological dampers, where the excitation circuit and magnetic field are applied from outside the magnetorheological chamber. This magnetorheological damper was designed and built to decrease the intrusive manufacturing operations and to maximize the working efficiency. The experimental tests made on the first prototype featuring this new technology was promising. The excitation of a set of 12 coils surrounding the body of the damper, by an electric current of 5 A, managed to increase the damping coefficient by up to 75%. A similar performance could be obtained by a current 9.4 times lower if the magnetic circuit is designed correctly. Compared to other devices, the actual design tolerates more the temperature elevation caused by the feeding of coils with high-intensity current, just because the heat is radiated outwards instead of being transferred directly to the magnetorheological fluid like in conventional designs. Finally, the numerical simulations made on Matlab show that the new magnetorheological damper, when mounted on a commercial vehicle, can considerably enhance its dynamic behavior and bring it back quickly to its stable position when the tires hit a bump on the road.