Enhancing the damping effect of MRF damper using an external magnetic excitation system

The magnetic field generated by the damper’s magnetic circuit governs the yield stress value of the Magnetroholgical Fluid (MRF) damper and hence its damping effect. This paper contributes to the literature on the development of MRF dampers by introducing a new design feature to improve the damper’s performance. The presented novel feature tends to amplify the magnetic field value and concentrate its flux within the MR fluid region. The excitation sources consist of 12 coils placed in radial directions surrounding the MRF to focus the energizing magnetic effects. However, the search for efficient solutions is not only focused on generating more energy but also on minimizing its loss. Therefore, a metallic ring was placed around the coils to close the magnetic circuit, guide the flux lines, and avoid any energy dispersion to the surrounding air. As a proof of concept, two materials were tested for the surrounding ring: plastic acrylonitrile butadiene styrene (ABS) and mild steel. The performance of both solutions was assessed experimentally with a Gaussmeter and numerically by using a model developed via COMSOL Multiphysics. Both techniques confirmed the efficiency of the solution based on a steel ring in preventing the flux dispersion into the surrounding air. In addition, an increase of the excitation current from 0 to 5A was found to elevate the magnetic field by 35%, compared with the ABS ring. In the second step, a test rig was designed and built to investigate the damping efficiency of the MRF experimentally. The testing apparatus consisted of a sliding-bearing mechanism connected to a variable speed motor. The damping effect was assessed based on the force and displacement data provided by a linear variable displacement transducer (LVDT) and a force cell. Damping forces were observed at a constant frequency of 0.36 Hz (22 rpm) when the testing system and the attached damper were functioning smoothly away from its resonant frequency. Moreover, the magnetic field excitation current was elevated from 0 A to 5 A with a 1 A step. Again, the metallic ring was found to produce a 112% greater damping coefficient than the case of the plastic ring when the excitation current reached 5A.Financial support for this research was graciously provided by Qatar National Research Fund (a member of Qatar Foundation) via the National Priorities Research Project under Grant No. NPRP-11S-1220-170112

Zero-Shot Motor Health Monitoring by Blind Domain Transition

Continuous long-term monitoring of motor health is crucial for the early detection of abnormalities such as bearing faults (up to 51% of motor failures are attributed to bearing faults). Despite numerous methodologies proposed for bearing fault detection, most of them require normal (healthy) and abnormal (faulty) data for training. Even with the recent deep learning (DL) methodologies trained on the labeled data from the same machine, the classification accuracy significantly deteriorates when one or few conditions are altered. Furthermore, their performance suffers significantly or may entirely fail when they are tested on another machine with entirely different healthy and faulty signal patterns. To address this need, in this pilot study, we propose a zero-shot bearing fault detection method that can detect any fault on a new (target) machine regardless of the working conditions, sensor parameters, or fault characteristics. To accomplish this objective, a 1D Operational Generative Adversarial Network (Op-GAN) first characterizes the transition between normal and fault vibration signals of (a) source machine(s) under various conditions, sensor parameters, and fault types. Then for a target machine, the potential faulty signals can be generated, and over its actual healthy and synthesized faulty signals, a compact, and lightweight 1D Self-ONN fault detector can then be trained to detect the real faulty condition in real time whenever it occurs. To validate the proposed approach, a new benchmark dataset is created using two different motors working under different conditions and sensor locations. Experimental results demonstrate that this novel approach can accurately detect any bearing fault achieving an average recall rate of around 89% and 95% on two target machines regardless of its type, severity, and location.Comment: 13 pages, 9 figures, Journa