Modal Analysis Of Blended Wing-Body UAV

The modal analysis deals with the dynamic behavior of mechanical structures under the dynamic vibration. This study aims to analyze the vibration characteristic of the blended wing-body Unmanned Aerial Vehicle (UAV) using modal analysis. The numerical method is used to calculate the eigen frequencies of the system. The COMSOL Multiphysics is selected as the Finite Element Method (FEM) software to simulate the study. The resulted eigen frequencies are 278.05 Hz, 721.28 Hz, 816.39 Hz, 1601.7 Hz, 1699.5 Hz, and 1855.5 Hz. The study also evaluates the displacement of the leading edge of the wing in all axes to understand the modal shapes. The modal shapes found are updrift, swift back, flapping vertical, flapping horizontal, flapping opposite horizontal and flapping more wave in horizontal movement. The comparison of resulted eigen frequencies with a conventional aircraft wing is conducted to understand the difference in its vibration characteristics.

PENGUJIAN EKSPERIMENTAL UNTUK MEMPELAJARI KARAKTERISTIK GETARAN PADA MODEL ROTOR GANDA KIT MENGGUNAKAN ANALISIS PETA SPEKTRUM DAN ORBIT

In modern technology, vibration signal is to be utilized in predictive maintenance application,more often when Dynamic Signal Analyzer (DSA) is invented. Unballance in rotor cause significantdeflection on rotated shaft and possible damage the machine. DSA is used in monitoring a condition ofbig rotation machines such as turbin, compressor, pump and generator. This research used double rotorsshaft system which was supported with journal bearing as a modification from the real rotation machines.Two of vibration characteristic as a sign of abnormal condition in rotation machine are unballance andoil whirl phenomenon. Meanwhile, oil whirl is seen in many rotation machines that use journal bearingas support its shaft. For rotation machine with high speed, oil whirl phenomenon can cause resonance inthe system, and then become the oil whip. Based on this condition that is very reasonable for makingearly detection, identification, and looking for the solution to prevent not wanted incident.

Similitudes for vibration characteristic of composite laminated plates

Similitude laws can be used to extrapolate the vibration characteristic of a small, inexpensive, and easily tested model into structural behavior for the full-size prototype. In this article, a systematic similitude approach is proposed to predict the natural frequency, mode shape, and vibration response of composite laminated plates. The emphasis of this article is to predict the vibration characteristic of composite laminated plates in an effective and convenient way. Sensitivity analysis (SA) is introduced to improve the prediction accuracy of natural frequency. For distortion similarity, the prediction accuracy is improved close to 5%. Modal assurance criterion (MAC) measures the consistency of mode shapes of the full-size prototype and scaled models. The influence of stacking sequence on mode consistency is investigated. Similitude based on virtual mode and statistical energy (SVMSE) is proposed to extrapolate the transient response of the prototype to simulate the shock environment, such as satellite–rocket separation, etc. In conclusion, the prediction accuracy of natural frequency, mode consistency, and response coincidence are considered comprehensively to extrapolate the vibration characteristic of the full-size laminated plates.

An Intelligent Prediction for Detecting Bearing Vibration Characteristics Using a Machine Learning Model

Bearing failure in the heavy rotating machines results in shut down of many other machines and affects the overall cost and quality of the product. Condition monitoring of bearing systems acts as a preventive and corrective measure as it avoids breakdown and saves maintenance time and cost. This research paper proposes advanced strategies for early detection and analysis of taper rolling bearings. In view of this, mathematical model-based fault diagnosis and support vector machining (SVM) are proposed in this work. A mathematical model using dimension analysis by the matrix method (Dimension Analysis Method (DAMM)) and SVM is developed that can be used to predict the vibration characteristic of the rotor-bearing system. Types of defects are created using electrical discharge machining (EDM) and analyzed, and correlation is established between dependent and independent parameters. Experiments were performed to evaluate the rotor dynamic characteristic of healthy and unhealthy bearings. Experimental results are used to validate the model obtained by the DAMM and SVM. Experimental results showed that the vibration characteristic could be evaluated by using a theoretical model and SVM. Efforts have been made to extend the service life of the machines and the assembly lines and to improve their efficiency, so as to reduce bearing failure; what provides novelty to these efforts is the use of four machine learning techniques. Thus, an automatic online diagnosis of bearing faults has been made possible with the developed model based on DAMM and SVM.