• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.7
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• pp.773-778
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• 2011

Accelerometers are widely used to measure the lateral vibrations of pipe-like structures such as a gun tube under impulse loads. Stress waves that precede the lateral vibrations due to the explosion within a gun contribute little to the vibrations, but saturate the accelerometer input. A mechanical filter eliminates this high-frequency stress wave and only transmits the signal corresponding to the lateral vibrations. The mechanical filter consists of a mechanical structure for mounting the accelerometers and a damping material. The low-pass filter characteristics are determined from the equivalent damping and stiffness property of this damping material. In this paper, we tested nine commercially available damping materials for their vibration characteristics by using a test rig. We also observed the change in the vibration characteristics while compressing the material. We designed and manufactured a mechanical filter and verified its filtering performance.

• Advances in aircraft and spacecraft science
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• v.6 no.6
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• pp.529-550
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• 2019

The effect of multiple process parameters on a set of continuous response variables is, especially in experimental designs, difficult and intricate to determine. Due to the complexity in aeroacoustic and vibroacoustic studies, the often-performed simple one-factor-at-a-time method turns out to be the least effective approach. In contrast, the statistical Design of Experiments is a technique used with the objective to maximize the obtained information while keeping the experimental effort at a minimum. The presented work aims at giving insights on Design of Experiments applied to aeroacoustic and vibroacoustic problems while comparing different experimental designs and approximation models. For this purpose, an experimental rig of a ducted low-pressure fan is developed that allows gathering data of both, aerodynamic and aeroacoustic nature while analysing three independent process parameters. The experimental designs used to sample the design space are a Central Composite design and a Box-Behnken design, both used to model a response surface regression, and Latin Hypercube sampling to model an Artificial Neural network. The results indicate that Latin Hypercube sampling extracts information that is more diverse and, in combination with an Artificial Neural network, outperforms the quadratic response surface regressions. It is shown that the Latin Hypercube sampling, initially developed for computer-aided experiments, can also be used as an experimental design. To further increase the benefit of the presented approach, spectral information of every experimental test point is extracted and Artificial Neural networks are chosen for modelling the spectral information since they show to be the most universal approximators.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.37 no.6
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• pp.551-558
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• 2013

A support vector machine (SVM) is a very powerful classification algorithm when a set of training data, each marked as belonging to one of several categories, is given. Therefore, SVM techniques have been used as one of the diagnostic tools in machine learning as well as in pattern recognition. In this paper, we present the results of classifying ball bearing fault types and severities using SVM with an optimized feature set based on the minimum distance rule. A feature set as an input for SVM includes twelve time-domain and nine frequency-domain features that are extracted from the measured vibration signals and their decomposed details and approximations with discrete wavelet transform. The vibration signals were obtained from a test rig to simulate various bearing fault conditions.

• Tribology and Lubricants
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• v.39 no.1
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• pp.1-7
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• 2023

Fluid pumps in chemical processes are typically driven by electric motors. Even if the motor is separated from the pump with seals, wear resulting from friction and misalignment can lead to leakage of chemical fluid, causing corrosion in the bearing supporting the motor, and, eventually, failure of the motor. It is thus a standard procedure to replace bearings at regular intervals. In this article, we propose 3D-printed plastic ball bearings for use as an alternative to commercial stainless-steel ball bearings. The plastic bearings are easy to manufacture, require less time to replace, and are chemically resistant. To validate the applicability of the plastic bearings, we first conducted chemical resistance tests. Bearings were immersed in 30 caustic acid and 30 nitric acid for 30 min and 24 h, respectively. The test results showed no corrosive damage to the bearings. A test rig was set up to compare the performance of the plastic bearings with that of the commercially equivalent deep-groove ball bearings. Loading test results showed that the plastic bearings performed as well as the commercial bearing in terms of vibration level and load-handling capability. Finally, a plastic bearing was subjected to a clean-in-place process for three months. It actually outperformed the commercial bearing in terms of chemical resistance. Thus, 3D-printed plastic bearings are a viable alternative to stainless-steel ball bearings.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.34 no.1
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• pp.49-54
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• 2010

Fretting wear behavior under elastic deformable contacts was experimentally examined by using a simulated dual cooled fuel rod and its supporting structure. As this fuel rod has larger outer diameter than the typical solid rod to accommodate sufficient internal flow, new supporting structure geometries should be designed and their reliabilities (i.e. vibration characteristics, fretting wear resistance, etc.) are also examined with both analytical and experimental methods. In this study, the supporting structure characteristics and fretting wear behaviors are analyzed and examined by using one of the supporting structure candidates which has an embossing shape. The supporting structure characteristics were examined by using a specially designed test rig and their results were compared with that of analytical method. Based on the test results, the relationship between the supporting structure characteristics and their fretting wear behaviors was discussed in detail.

• Tribology and Lubricants
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• v.40 no.4
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• pp.111-117
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• 2024

Due to global CO₂ emission reductions and fuel efficiency regulations, the trend toward transitioning from internal combustion engine vehicles to electric vehicles (EVs) has accelerated. Consequently, the problem of EV failures has become a focal point of active research. The parasitic capacitance generated during motor-shaft rotation induces voltage that deteriorates the raceway and ball surfaces of bearings, causing electrical damage in EVs. Despite numerous attempts to address this issue, most studies have been conducted under high viscosity lubricant and low load conditions. However, due to factors such as high-speed operation, rapid acceleration and deceleration, motor heating, and motor system-decelerator integration, current EV applications have shown diminished stability in lubrication films of motor bearings, thereby leveraging the investigation to address the risk of electrical damage. This study investigates the electrical damage to rolling bearing elements in EV motor drive systems. The experimental analysis focuses on the effects of electric currents and operational loads on bearing integrity. A test rig is designed to generate high-rate voltage specific to a motor system's parasitic capacitance, and bearing samples are exposed to these currents for specified durations. Component evaluation involves visual inspections and vibration measurements. In addition, a predictive model for electrical failure is developed based on accumulated data, which demonstrates the ability to predict the likelihood of electrical failure relative to the duration and intensity of current exposure. This in turn reduces uncertainties in practical applications regarding electrical erosion modes.