جستجوی مقالات مرتبط با کلیدواژه "vibration analysis" در نشریات گروه "مکانیک"

This study focuses on the investigation of intelligent form-finding and vibration analysis of a triangular polyhedral tensegrity that is enclosed within a sphere and subjected to external loads. The nonlinear dynamic equations of the system are derived using the Lagrangian approach and the finite element method. The proposed form-finding approach, which is based on a basic genetic algorithm, can determine regular or irregular tensegrity shapes without dimensional constraints. Stable tensegrity structures are generated from random configurations and based on defined constraints (nodes located on the sphere, parallelism, and area of upper and lower surfaces), and shape finding is performed using the fitness function of the genetic algorithm and multi-objective optimization goals. The genetic algorithm's efficacy in determining the shape of structures with unpredictable configurations is evaluated in two distinct scenarios: one involving a known connection matrix and the other involving fixed or random member positions (struts and cables). The shapes obtained from the algorithm suggested in this study are validated using the force density approach, and their vibration characteristics are examined. The findings of the comparative study demonstrate the efficacy of the proposed methodology in determining the vibrational behavior of tensegrity structures through the utilization of intelligent shape seeking techniques.

This paper discusses the nonlinear dynamic behavior of nanocomposite microplates reinforced with carbon nanotubes in contact with static fluids. Equations of motion are derived using the first-order shear deformation theory of plates and considering the size effects and large deformations. Effective mechanical properties are determined by applying the law of mixtures. By use of the Galerkin method, the nonlinear equations governing motion are discretized and the numerical solution is obtained. The results have been verified and the effect of micro-plate geometrical characteristics, small size parameter, fluid height and weight fraction of the carbon nanotubes studied on natural linear and nonlinear frequencies and the dynamic response has been evaluated. The results indicate that the natural frequency of the system decreases with increasing fluid height. The reinforcement of microplates by carbon nanotubes also results in a softening of the stiffening behavior of the spring and a substantial bending of the response curve. Depending on the excitation frequency, this bending may result in jump instability. By examining the time response, phase, and Poincaré map of the system, periodic, quasi-periodic, and chaotic vibration behavior can be observed.

The monitoring system for induction motors (IMs) plays an important role in the majority of industrial plants. Bearing faults and shaft misalignment are common mechanical defects in induction motors. The aim of this paper is to detect simultaneously two common faults in induction motor including bearing defect and shaft misalignment. For this purpose, a test setup consisting of an induction motor coupled to a rotor shaft is designed and tested under different loading conditions and at different speeds. The diagnosis parameters of vibration signal are calculated by conventional signal processing methods as well as bispectrum analysis. Feature extraction and KNN classification techniques are applied to the calculated parameters to provide condition monitoring of the induction motor. The results show that the application of bispectrum analysis along with the conventional signal processing methods improves detecting bearing fault in induction motor and shaft misalignment in the case of single fault as well as multiple simultaneous faults.

In this study, a new method for the fault detection of the locomotive engine injector nozzle based on vibration analysis and statistical tests, inside artificial neural networks, is presented. For this point, first the under study received vibration signals in the frequency domain is divided into several smaller ranges and the RMS of each range is then extracted as a frequency property and given as an input to the neural network. Because the high selection of the features reduces the accuracy of the neural network, the extracted feature vector with different levels of significance passes through the T-test filters, firstly, and then enters the neural network as an input. Using of this method, the accuracy of the neural network increases from 78.4 to 94.6%, and also help to detect the frequency ranges. According to the results, the fault of the injector nozzle crack increases the intensity of vibrations in the upper band frequencies of 1500 Hz.

Vibration analysis is one of the most practical methods for monitoring and troubleshooting rotating equipment. In this research, vibration analysis and support vector machine algorithms were used for monitoring and troubleshooting Alstom locomotive blowers. First, vibration data were collected from the blowers and the received signals were categorized into four groups: healthy blowers and blowers with problems of unbalance, loose shaft (base), and warped blades. Sixteen frequency and time features were then extracted from the received signals. Because in rotating systems, the ratio of the intensity of vibrations in the harmonics of the rotation of the machine can help diagnose the faults, the ratios of all features were calculated and defined as new features. The accuracy of the network can be sometimes lowered by the multitude of features, thus, a t-test filter was inserted into the support vector machine algorithm to select the features. The results show that the t-test filter increased the accuracy of the support vector machine algorithm. Finally, the feature selection of this network was compared with the feature selection by the genetic algorithm. The results show that the network designed in this research has a better performance in feature selection than the genetic algorithm.

In recent years, with the advent of the Fourth Industrial Revolution concepts and the development of artificial intelligence technologies, new approaches such as the digital twin have been introduced. In a digital twin, a virtual counterpart of the physical system during its whole life is created, with abilities such as analyzing, evaluating, optimizing, and predicting. The first step in creating a digital twin model is to construct a (multi) digital health indicator that describes different aspects of the physical component state during the whole life of the component. In this research, a new method for constructing health indicators based on vibration measurement and a deep learning model has been introduced. For this purpose, the Continuous Wavelet Transform was used to convert the raw vibration signals into two-dimension images; Then, the deep learning model was used to extract features from the images and the health indicator is constructed based on the differences of the images in normal and failure stages. In this article, various Autoencoder architectures are discussed, and it is demonstrated that the Convolutional Autoencoder has better performance in terms of detecting incipient faults. The performance of the proposed model is evaluated by the vibration data of the bearing, and the constructed health indicator exhibited a monotonically increasing degradation trend and had good performance in terms of detecting incipient faults.

Determining the geometrical characteristics of the telescopic booms for the stability of the satellite and reducing its transmission vibrations is one of the most important issues that should be considered for designing a satellite stabilization booms. In this study, due to the high ratio of satellite mass to the booms, the booms is modeled as a cantilever beam with finite element method and the initial displacement in accordance with the shape of the first mode is considered for it. The supports forces and torques were calculated using the Newmark dynamic solution method for the finite element model. With these values, the Eulerian angles of the satellite have been evaluated. In this study, the critical displacement of the beam, which is its lateral displacement, has been investigated. For stability, the length of the booms should be increased, while for decreasing the vibrations of the satellite, a short length is desired. Using multi-objective optimization algorithm, the conflict has been resolved to achieve the appropriate geometry. The results show that the stability and vibrations transmitted from the beam to the satellite are very important and the correct choice of geometry has a good effect on these two issues.

Early fault detection of the rolling element bearings has a very important role in increasing the reliability of rotating machines.It leads to better decision-making for maintenance activities.  In recent decades, the shock pulse method has been developed to detect faults in the early stage of rolling element bearings degradation. In this paper, the accuracy of the remaining useful life estimation using extracted features from vibration signals and that from the shock pulse method are compared. In this regard, a set of accelerated life tests on rolling element bearings were designed and performed. Both shock pulse signals and vibration signals of the under-test rolling element bearings were recorded. Then two models based on feed-forward neural-network are developed to predict the remaining useful life of rolling element bearings. In the first model, only extracted features from vibration signals are fed for remaining useful life prediction. In the second model, the extracted features from shock pulse method are fed too. The results show that using shock pulse method-based features improves the accuracy of remaining useful life estimation. Also, using the health indicators extracted from vibration analysis and shock pulse method leads to a better estimating of the degradation behavior.