Papers by Keyword: Vibration

Abstract: Early fault diagnosis is a crucial element in maintaining the optimal operation of rotating machinery and avoiding sudden failure resulting in material and non-material losses. This research aims to select the salient features to diagnose the induction motor faults using an SVM model. The induction motor is simulated experiencing three fault scenarios: single fault, double faults, and multiple faults. These scenarios consist of stator fault, rotor fault, bearing fault, stator-bearing fault, stator-rotor fault, bearing-rotor fault, and stator-bearing-rotor fault. Vibration signals for each of these conditions are collected using an accelerometer sensor with a sampling frequency of 20 kHz. The study utilizes 12 statistical features, comprising 7-time time-domain features, namely mean, standard deviation, kurtosis, RMS, skewness, peak value, crest factor, and 5 frequency domain features, namely mean frequency, median frequency, spectral entropy, power spectral density, and spectral centroid. These features are selected using the ReliefF feature selection algorithm, and the selected features are then employed as classification parameters. The results indicate that the most relevant statistical features used for classification parameters are RMS, Standard Deviation, and Power Spectral Density. Meanwhile, the performance of the Support Vector Machine is excellent for three cases of the induction motor faults. The accuracies for single faults, double faults, and multiple faults are 99%, 100%, and 99% respectively.

Abstract: Transportation is one of the crucial factors in the development of a country. This can be observed from the increasing needs of transportation in supporting human activities in residential and urban areas. Therefore, it is essential to maintain the infrastructure that supports the needs of transportation. One of the infrastructures that need to be considered as the main factor of transportation is road condition. Damaged road conditions can cause obstacles to the transportation system. One way to detect road damage is by measuring the vibration values that occur under different road conditions using an accelerometer as a vibration recorder. The vibration data is classified into three groups based on the road conditions where the recordings took place, namely good road condition, speed bumps (bump), and potholes. A total of 52 data samples were collected for each road condition in Yogyakarta using a motorcycle, which were then processed into vibration data in the frequency domain. The vibration data processing was carried out using Jupyter Notebook software with Python programming language and the algorithms used in this research were Fast Fourier Transform (FFT), SG Filtering, and Power Spectral Density (PSD) to determine the strength of the vibration signal. After that classification was performed by applying supervised machine learning using the multiclass classification algorithm on Support Vector Machine (SVM) other than that, cross-validation process was implemented to know the performance of the machine learning model. The classification results show an accuracy value of 92.31% for predicting road condition labels in the training model and 97.44% for the testing model. Both models are calculated using 75% of the total data for the training model and 25% of the total data for the testing model.

Abstract: The frequent use of railway tracks in railway operations can cause damage or wear that can disrupt comfort and resulted vibrations on the trains. There are various types of damage that can occur to railway tracks, one of which is longitudinal level damage. Machine learning can be employed to predict the damage. However, it is quite difficult to predict based on real data with a high amount of data. Therefore, a railway miniature is fabricated with a controlled damage. Therefore, this study has purpose to predict the damage using the produced data from railway miniature. The vibrations was measured using an accelerometer device that available on smartphones with the Phypox application, and it will be mounted on a miniature railway track with three different track conditions: one normal and two abnormal, with each track condition has 50 data points. With the assistance of machine learning as the main brain behind the vibration detection program, vibration data can be classified based on the track conditions experienced. The data was processed into frequency domain using Fast Fourier Transform (FFT) algorithm, filtered using SG-Filter, and Power Spectral Density (PSD) will be used to assess the strength of the vibration signal. The vibration data processing was carried out using Jupyter Notebook software with Python programming language. Classification was performed by applying supervised machine learning using the classification method of Support Vector Machine (SVM). In classification process, results obtained show an accuracy of 88.19% for training model and an accuracy of 82.61% for testing model, computed using 85% of total data for training model and 15% of total data for testing model. The produced data and built machine learning can be further applied for checking the rail damage at uncontrollable environment.

Abstract: Machining processes on hybrid composite materials involve activities such as surface cutting, hole drilling and other cutting processes to achieve final shape and dimension of the composite product. There were several unexpected situations during the process, such as ununiform vibrations due to inconsistent of natural fiber structures and nonideal cutting conditions lead to progressive tool wear and low quality of the cutting surface. In this study, an experimental approach was conducted on the milling process of polyester matrix-based composites reinforced with abaca and glass fibers, produced through the press molding process. The milling process was utilized by a 10 mm diameter 4-flute carbide end mill cutter with a 45-degree helix angle. The study aimed to investigate the influence of cutting conditions (spindle speed, feed, and depth of cut) on vibration during the milling process of abaca-glass fiber composites. Three levels of each cutting parameters were determined based on cutting tool working capabilities, i.e. the spindle speed = 2000, 3000 and 5000 rpm, the feed = 0.004, 0.007 and 0.10 mm/tooth, and depth of cut = 1, 1.5 and 2 mm. The Design of Experiment (DOE) was constructed by Box-Behnken technique of Response Surface Methodology. The down milling process were conducted for all scenario of DOE, and the vibration was measured using a digital accelerometer. The results of the study indicated that vibration increased with the increase of spindle speed, feed, and depth of cut. The results show that the maximum vibration value (0.0206 m/s²) was obtained at a spindle speed of 5000 rpm with a feed of 0.07 mm/tooth and a depth of cut 2 mm. Meanwhile, the minimum vibration value (0.0143 m/s²) was obtained at the spindle speed 2000 rpm, feed 0.04 mm/tooth and depth of cut 1.5 mm.

Abstract: The vibration response of laminated sandwich beams, with a core layer filled with various foam materials, referred to as Foam-based Sandwich Laminated Composite (FSLC) beams, has been studied. First, to precisely capture the varying material properties across the thickness of the sandwich beams, a modified layerwise displacement theory was employed. This approach addresses the inhomogeneity of the foam material in the core, yielding more accurate results than conventional classical laminated plate theories typically used for analyzing laminated composite structures. Secondly, to assess the impact of foam properties on dynamic behavior, FSLC beams incorporating three distinct types of foam have been analyzed. Thirdly, a proof-of-concept experimental test was conducted to demonstrate the functionality of the proposed model under dynamic loading conditions. The natural frequencies and damping coefficients of the FSLC beams have been determined using the modified layerwise theory. The dynamic response of the FSLC beams under impulse loading has also been analyzed. It was observed that the addition of foam in the core layer enhances the damping properties of the sandwich beam by approximately ten percent while reducing the natural frequencies by approximately five percent under all types of loading considered.

Abstract: In recent years, electronic devices have become lighter, thinner, shorter, smaller, and more multifunctional, driving advancements in drilling technologies. To meet the demands of electronic applications, this study proposes a drilling machine tool with a counterbalanced vibration control mechanism. In this study, machining experiments were conducted using a machine tool equipped with a left-right ball screw counterbalance mechanism during the step operation. Observations were made with a high-speed camera and a thermal camera, followed by examinations of the drill and holes after machining. As a result, significant findings regarding the drill tip temperature and runout were obtained. It was also confirmed that drill wear and runout affect machining quality.

Abstract: The result of the research aims to provide a foundation structure that reduces the amplitude of oscillation of machines and equipment from various technical causes and natural phenomena, preventing resonant phenomena and economical use of foundation material. The proposed foundation, compared to existing foundations, provides a decrease in amplitude and prevents the phenomenon of resonance of vibrations of foundations for machines and equipment creating savings in the use of cement-concrete material. There is considered mathematical, structural, and practical modeling of anti-vibration foundations of mechanical installations in this article. The purpose of the study is to create a foundation design that reduces the amplitude of oscillation of structures from various technical reasons and natural phenomena, prevents resonant phenomena, and economical use of foundation-reinforced concrete material. The research results can be used in the design of foundations of piston machines and equipment used in the oil and gas industry.

Abstract: This study investigated the effects of tool runout on chatter vibration taking images of a machined surface to assess the vibration strength, number of vibrations, and phase difference depending on the spindle speed and axial depth of the cut. This study obtained significant results regarding the stability pocket represented by the spindle speed. We observed that the stability limit changed depending on tool runout.

Abstract: A comprehensive understanding of powder flow is essential, particularly in the synthesis of additives in the solid free-forming process and dosage dispensing in pharmaceutical industries. The vibration method is the most widely used for inducing powder flow. In this method, a hopper containing powder is vibrated by a piezoelectric system to allow powder flow. However, there are several drawbacks in this method, particularly in the degree of consistency of the resulting flow. This occurs because the vibration characteristics created have a single axis direction, resulting in which can lead to powder compaction. To resolve this problem, this research is conducted to determine the flow properties of titanium powder dispensed by 2-axis vibration. The titanium (Ti) powder having a size of less than 74 μm in the powder hopper was vibrated for 15 min by a DC motor and the weight of the dispensed powder was measured to evaluate the consistency of the resulting powder flow. The result shows that the powder flow generated by the DC motor was consistent during the period of dispensing. However, the powder flow rate dropped up to 8.6% during 5-10 min of dispensing at a speed of 1800 rpm. In conclusion, the 2-axis vibration by using a DC motor could prevent the powder compacting phenomenon and ensure a consistent flow of micro-sized powders during the dispensing process.

Abstract: The purpose of this research is to develop a vibration isolator using auxetic viscoelastic sheet technology. The procedure for the design and elaboration of the insulator using polymers and by means of fatigue analysis using impacts is exposed, the absorption of energy by cycles is compared and the damage that is produced in the viscoelastic is observed with an optical microscope. This is to compare the variation in the force that the fatigue equipment exerts to deform the sheet in the same magnitude and to be able to establish, through the ratio of absorbed energy and returned energy, criteria on the useful life of the element for future designs.