فهرست مطالب میرمحمد اتفاق

Since the maintenance and repairing costs of mechanical systems, such as structures and rotating machines are significantly high, one way to reduce these costs is to consider some approaches before any operational work to check for damages in such systems. In this study, a new method is presented for damage detection of offshore jacket structures in the presence of various uncertainties, such as modeling errors, measurement errors and environmental noises, based on the simulated model and intact state of the real model. In the proposed method, real intact structure data is used to update the simulated model parameters. Some parts of the signal that are not related to the nature of the system are removed using the complete ensemble empirical mode decomposition method. Frequency data is extracted from the vibrational signals using the frequency domain decomposition method. A deep auto-encoder neural network is designed to learn the damage-sensitive features from the frequency data and to damage detection of the structure. In order to train the proposed deep network, frequency data of the simulated model and real intact state are used; then the frequency data of the real structure is used to test the proposed deep network. The results show that the proposed method is capable for damage detection of the offshore jacket structure with more accurate results than the other comparative methods.

Condition monitoring of mechanical systems, such as structures and rotating machines is always a major challenge. This paper is presented a new method for damage detection of real mechanical systems in presence of the uncertainties such as modeling errors, measurement errors, varying loading conditions, and environmental noises based on a simulated model and real healthy state. In this method, data of a real healthy system is used to updating the parameters of the simulated model. Some parts of the signals that are not related to the nature of the system are removed using the complete ensemble empirical mode decomposition method. A deep convolutional network is designed to learn damage-sensitive features from raw frequency data of simulated model and real healthy state. Raw frequency data is extracted from vibration signals using the power spectral density method. In order to train the proposed deep network, raw frequency data of the simulated model andreal healthy state are used. Then, raw frequency data of the real model are used to test the proposed deep network. The proposed method is validated using an experimental beam structure. The results show that using the proposed algorithm for identification and damage detection of the beam-like structure has more accuracy with respect to the other comparative methods

Although the traditional automatic ball balancer (ABB) has numerous advantages, it has two major deficiencies, i.e., it has a limited balance stable region and it increases the vibration amplitude of the rotor at transient state. These deficiencies limit the applicability of ABBs. In this regard, a new type of ABB called “the Ball-spring autobalancer” has been proposed to resolve the mentioned deficiencies of the traditional ABBs. In order to investigate the capability of the Ball-spring AB in balancing rotors, it is necessary to study its dynamics accurately. The dynamics of a rotor with linear bearing equipped with a Ball-spring AB has been studied previously; however, in real situations, the bearings have nonlinear characteristics. Here, the dynamics of a rotor with nonlinear bearings equipped with a Ball-spring AB is investigated by the multiple scales method for the first time. The results show that the nonlinearity at the rotor bearings does not impair the advantages of the Ball-spring AB.

In recent years, there has been much interest in the use of automatic dynamic ball balancers to balance the rotors with varying imbalances. An automatic dynamic ball balancer is a device that can automatically compensate the variable imbalances of a rotor in certain working conditions without having to stop the rotating equipment. Ball-spring autobalancer is a new type of automatic ball balancers which has two main advantages over the traditional ones, i.e. it causes the rotor vibration amplitude at the transient state to be small and it has a wider balanced stable region. Bearings are one of the most important mechanical components due to their considerable influence on the dynamic behavior and stability of the rotary systems. In previous studies, the dynamic behavior and stability of the rotor with isotropic bearings equipped with a Ball- spring autobalancer has been analyzed. However in practice, the bearings have generally anisotropic behavior due to manufacturing process. In this study, the dynamics and stability of a rotor with anisotropic bearings equipped with a Ball-spring autobalancer are investigated via the multiple scales method for the first time. The results show that the anisotropic bearings do not impair the main advantages of the Ball-spring autobalancers, and as the anisotropic parameter increases the balanced stable region decreases.

Some sport activities such as skiing causes athletes to be constantly exposed to vibrations caused by rough terrain which can lead to muscle fatigue in many repetitions. The purpose of this study is to investigate the effects of musculoskeletal fatigue on vibration transmissibility coefficient and muscle activity in subjects are exposed to floor vibrations. For this purpose, 12 young men were exposed to floor vibrations in the squat mode at frequency of 12 Hz and intensity of 0.9 m/s2 to achieve complete fatigue. The results of this study show that the occurrence of the fatigue in the musculoskeletal system of the body increases the amount of both variables of the study simultaneously. In other words, increasing of the muscle activity alone does not indicate fatigue phenomenon. Because according to the results, increasing the amount of muscle activity without occurrence of the fatigue reduces the vibration transmissibility coefficient in the muscle.

Most of structural failures are because of break in consisting materials. Beginning of these breaks is with crack, whose extension is a serious threat to behavior of structure; so, the methods of distinguishing and showing cracks are the most important subjects, which are being investigated. In this article, by experimental, a new smart portable mechanical system to detect damage in beam structures by wavelet packet energy rate index is introduced. At first, acceleration-time history is taken from the points of the simple support beam, using the accelerometer sensors, and then these signals are decomposed into packet wavelet components and the energy rate index is calculated for each, which is named by Wavelet Packet Energy Rate Index (WPERI). The results indicate that these values are a sensitive and accurate index for the identification of the cracks.

As the cost of maintaining and repairing floating wind turbines is high compared with conventional turbines, one way to reduce these costs is to investigate the defects in these structures before any practical work. The importance of the stability and structural safety of wind turbines becomes more important with water depth growth and one of the most important factors for the stability of floating wind turbines is the system of mooring cables. In this paper, the effects of broken mooring cables on the dynamic response of a TLP type wind turbine is investigated. For this purpose, the floating wind turbine is modeled accurately with non-linear equations so that this modeling is responsive to large rotation angles that may be caused due to the cutting-off of the mooring cables. After concluding turbine modeling and applying forces, by cutting various mooring cables, the dynamic response changes of the structure are investigated. Studying the results shows that unlike other platforms, breaking of the mooring lines in a TLP-type turbine does not cause extreme oscillations and drifts in the structure.

In this paper, a linear dynamic model of simply supported Above-Ground pipeline during pigging process has been developed and verified by experimental tests. The PIG (Pipeline Inspection Gadget), is an internal moving sprung mass pushed by the fluid pressure, which itself act as a flowing varying mass. The governing equations of motion for the system including the pipeline, moving PIG as a moving vibrational sub-system, and flowing fluid with varying mass were obtained using Hamilton’s principle. Then, the extracted equations were discretized and solved via finite element method. Modal parameters of the pipeline system were calculated during intermittent passage of PIG through the pipe under different fluid flow rates, and their variations were extracted. Validation of the model was carried out using an experimental setup, including a 2.5 meter length Carbon Steel pipe, a simple bi-directional PIG with rubber discs and a centrifugal pump, connected to a control valve, providing required fluid pressure to push the PIG through the pipe. Using data acquisition system to acquire the vibration signals, and employing experimental modal analysis, frequency responses of the system at different points were obtained and the modal parameters were extracted and compared to that of the simulated model. A comparable results have been achieved between theoretical and experimental methods. Also variation of the system natural frequency versus speed and position of PIG in the pipe, were investigated. Moreover, the displacement of the mid-span of considered pipe during pigging process has been obtained using suggested theoretical model.

One of the new methods for powering low-power electronic devices employed in the sea, is using of mechanical energies of sea waves. In this method, piezoelectric material is employed to convert the mechanical energy of sea waves into electrical energy. The advantage of this method is based on not implementing the battery charging system. Although, many studies have been done about energy harvesting from sea waves, energy harvesting with considering random JONWSAP wave theory is not fully studied up to now. The random JONSWAP wave model is a more realistic approximation of sea waves in comparison of Airy wave model. Therefore, in this paper a vertical beam with the piezoelectric patches, which is fixed to the seabed, is considered as energy harvester system. The energy harvesting system is simulated by MATLAB software, and then the vibration response of the beam and consequently the generated power is obtained considering the JONWSAP wave theory. In addition, the reliability of the system and the effect of piezoelectric patches uncertainties on the generated power are studied by statistical method. Furthermore, the failure possibility of harvester based on violation criteria is investigated.

Modeling and simulation of the bridge response during vehicle moving on it is very important in optimum bridge design. There are many researches in this field which some of them deals with studying of the structure and bridge uncertainty effects on the bridge-vehicle dynamic response. Some researchers are dealing with vehicle uncertainty that is uncertainty in mass, sprung, damping, velocity, numbers, incoming time and other mechanical parameters of the vehicles. Other researchers are considering the bridge parameters uncertainty like as module of elasticity, bearing immobility and mass of the bridge. Also in most of the researches, analytical and Monte-Carlo simulations have been used for obtaining the dynamic response of the bridge with considering mentioned uncertainty. However there isn’t any accurate and general statistical study on the bridge response with general uncertainty. In other words, the complete set of uncertainty of the vehicles or bridge has not been applied in modeling and also the exact statistical study of the dynamic response of the bridge has not been applied exactly. Therefore in this paper, after modeling of the bridge-vehicle system with coupled finite element beam and discrete vehicle model, most of the uncertainties of the vehicles have been produced with Gaussian probability distribution function and the statistical parameters of the response have been extracted by Monte-Carlo simulation. The selected model applied in this paper is a discrete model of vehicle with four degree of freedom mass sprung system. Also, the Euler-Bernoulli model was used for bridge and the coupled dynamic equation was extracted using finite element modeling of the beam with Hermitian interpolation function for suitably dividing the vehicle forces, applied on the beam elements nodes. In addition the different road surface using ISO standard was applied on the finite element beam model. One of the major contributions of this paper is considering the type of the vehicle crossing on the bridge as an additional uncertain parameter which is not mentioned in the previous literatures. In other words, three classes of the vehicle that is heavy, semi-heavy and light ones was applied in simulation as uncertain parameters. The mechanical characteristics of the vehicle were derived by CARSIM software and by Monte-Carlo simulation using uniform probability distribution function. Then mentioned parameters was produced and entered in New-Mark simulation steps. With studying on the deflection, velocity, acceleration of the bridge and also considering corresponding upper and lower limit band (confidence interval) and variance of the mentioned parameters, extended results of uncertainties effects have been obtained. It should be mentioned, in this paper an exact statistical test method was used for obtaining the statistical properties of the dynamic response. Also, in different road surface and conditions, the simulation was carried out such that four surfaces of the bridge and three type of the vehicle classes was simulated separately and finally one simulation was done in complete set of the uncertainty. Very detailed and complete results were studied and one of the important results of this paper is reporting the most effective uncertainties on the bridge response. As one of the results, it was found that with increasing the velocity of the vehicle, surface roughness and weight of the vehicle, the uncertainty of the response are increasing. As another important result is that the fundamental frequencies of the bridge is less sensitive to the uncertainties of the vehicle's parameters. Derived results of this paper can be applied in optimum designs of the bridges and also in designing the damage identification methods.

One of the new methods for electrical powering of the bridge health monitoring systems is applying energy harvesting systems using bridge vibration. In this method, the piezoelectric material is employed for converting mechanical energy into electrical energy. The advantage of this method is based on not implementing the battery charging system. In previous research, some studies have been done on the energy harvesting from vibration of bridges, however, research on statistical analysis of produced power with considering uncertain vehicle type and specifications has not been considered. Therefore in this paper, after modelling the bridge-vehicle-piezoelectric system with complete vehicles uncertainties, the statistical analysis of produced voltage are carried out by Monte-Carlo simulation.

One of the new methods for reducing the vibrations of rotors with variable imbalance is implementing automatic ball balancer (ABB). Although, the ABB has numerous advantages, it has one major deficiency; increasing the rotor vibration amplitude at transient state that limits the use of this type of balancers. In the previous studies for diminishing the mentioned deficiency, a new type of ball balancer which is called the ball-spring ABB, is introduced and the dynamic behavior of Jeffcott rotor equipped with the ball-spring ABB is investigated. In the Jeffcott rotor model the gyroscopic effect is not considered, however, in practice and in many applications, due to asymmetry which comes from the offset of the rotor from the shaft mid-span, the gyroscopic effect is generated. In such conditions, the results of Jeffcott model are not reliable and dynamic behavior of the ball-spring ABB should be investigated in the presence of gyroscopic effect. In this paper by considering the asymmetry in the rotor-shaft system and taking into account the gyroscopic effect, the equations of motion of a rotor equipped with the ball-spring ABB are derived. The time responses of the system are computed and based on the Lyapanov first method, the stable regions are extracted. The results show that not only the gyroscopic effect does not affect on the performance of the ball-spring ABB, but also the magnitude of the Eulerian angles of the rotor equipped with the ball-spring ABB is less those the rotor equipped with the traditional one.

A variant to traditional liquid-phase sintering is to use prealloyed powders, which are heated to a temperature between the liquidus and solidus. The liquid forms inside the particles and spreads to the particle contacts. The liquid volume fraction increases with increase in sintering temperature and time and decreases rigidity of the sample. Beam bending test is the technique for measuring viscosity of the samples. In this paper, measurement of viscosity and its influence on the deflection behavior of LeadBronze pre-alloyed beam in different sintering temperatures was studied. Beam bending analysis is performed at various temperatures and times ranging from 845 to 865 °C. The midpoint deflection was measured by the in situ bending images. The viscosity as a function of the deflection rate, density, span length and height was measured by mathematical relations. It is concluded that increase in liquid volume fraction due to increasing temperature, decreases viscosity and then rigidity of the sample. Finally the deflection of Lead-Bronze beam as a function of time is linear and in the case of temperature is nonlinear and deflection rate increases with increasing time.

Caspian Sea is one of the most low-lying areas of the region which is located between latitudes 36.34 and 47.13 degrees north. Its considerable vast area and depth have provided an opportunity to gain renewable energy by different methods. This paper analyzes the performance and mechanism of a floating wave energy converter known as WaveStar, in the above-mentioned sea. Different parts of mechanism are examined under hydrodynamic forces of waves with certain periods and amplitudes. By using the frequency parameters, profile and velocity of the waves; and solving the governing dynamic equations for the model, the vibration response of system has been derived. The main part of this study is the investigation of the effect of changing the arm length, float diameter, wave period and wave amplitude on the structure using regular wave with Froude-Krylov force.

Most of structural failures are because of break in consisting materials. Beginning of these breaks is with crack which extension of them is a serious threat to behavior of structure, so the methods of distinguishing and showing of cracks are most important subjects which are being investigated. In this article, a new smart portable mechanical system to detect damage in beam structures form using fuzzy-genetic algorithm is introduced. Acceleration-time history of the three point of beam is obtained. The signals are then decomposed into smaller components using new EMD (Empirical Mode Decomposition) method with every IMF containing a specific range of the frequency. The dominate frequencies of the structure are obtained from these IMFs using Short-Time Fourier transform. Subsequently, a new method of damage detection in simply supported beams is introduced based on fuzzy-genetic algorithm. The new method is capable of identifying the location and severity of the damage. This algorithm is developed to detect the location and severity of the damage along the beam, which can detect the damage location and severity based on the pattern of beam frequency variations between undamaged and damaged states.

Fluid conveying pipe used in different fields such as petrochemical, oil, gas, hydraulic structures in transmission lines.The damage caused by vibrations damaging pipes or other components of a power plant industrial plants are related to each other, one of the main reasons for the loss of production time is considered damage and ultimately fires in industrial plants. So make a way as to identify damage in the fluid conveying pipe is of particular importance.The purpose of this study, the effect of the fluid inside the pipe vibration characteristic is a theoretical approach.Then for forced vibration analysis of fluid pipe considered passes through the external moving oscillator. And then a method to detect damage in pipes recommended based on fuzzy-genetic algorithm. First, fluid conveying pipe dynamic equations, modeled interaction between external moving, pipe and fluid then solving vibration system, acceleration time history extracted from midpoint of pipe. Subsequently, a new method of damage detection in simply supported pipe is introduced based on fuzzy-genetic algorithm. The new method is capable of identifying the location and severity of the damage. This algorithm is developed to detect the damage location along the beam, which can detect the damage location based on the pattern of beam frequency variations between undamaged and damaged states. The severity of damage is assessed based on quantitative variations in frequencies.

There are many researches on the vibration behavior of the multi-phase flow in the pipes. However, there isn’t any general statistical study on the dynamic response of such systems. Therefore in this paper, at the first step, the nonlinear equation governing the transverse vibration of the pipe is derived using the Hamilton's principle. The nonlinearity in the system is induced by considering large deflections. The interaction between the pipe and the multi-phase fluid flow and the resultant uncertainty is modeled by random excitation which is produced by using normal distribution function. After extraction of the governing equation and discretizing it by the Galerkin method, the equations are solved numerically. The statistical parameters of the response have been extracted by Monte-Carlo simulation. With studying on the deflection of one point on the pipe and also considering corresponding upper and lower limit band (confidence interval), extended results of uncertainties effects have been obtained. The results show that with increasing the velocity of the fluid, the uncertainty of the response is decreasing. Also by considering nonlinear model, the probabilities of failure are increased.

Nowadays, damage diagnosis of the engineering structures, applying vibration signal for early damage identifying during normal operation of the structure or after some disasters such as earthquake are very important from academic and technology viewpoints. Among different methods, proposed for damage diagnosis of the structures, the methods, based on FE model updating using modal analysis features are very important because of its practicability such that there are many researches in this field. Also because of node measurement limitations in real structures, model reduction is accompanied with damage detection methods. In addition for on-line applications of the mentioned methods for structural health monitoring with complex and intensive FE modeling, speed and accuracy increasing has attracted many researchers’ attentions. In this paper one of new model updating methods, which is lately proposed and proved to be accurate and fast, are applied for designing a damage diagnosis method. A beam, modeled with FE, has been considered with artificial damage on it is simulated in MATLAB for studying the proposed methods’ abilities and the noise effects on the method was studied exactly. Additionally, for studying the capability of the method in practice, the proposed method was applied on laboratory beam and the results were studied.

Structural health monitoring is essential for ensuring the structural safety performance during the service life. The process is of paramount importance in case of the floating wind turbine due to the structural parts subjected to the marine environmental risky conditions. In this paper a fuzzy-based damage identification method using dynamic response of the Spar floating wind turbine has been proposed. In the first step، the nonlinear equations of motion of the floating wind turbine system derived using the theorem of conservation of angular momentum and Newton’s second law. Then the variation values of the frequency characteristics of the structure in each DOFs due to stiffness changes of mooring lines (simulated damage) are considered as input features to the fuzzy system. Also the fuzzy system was trained based on calibrating of the membership functions by defining the damage classes properly. For validating the proposed method، noise with different SNRs was contaminated to the measured features and the success rate of the damage detection was calculated. The results showed that the proposed method is able to identify the damage classes with acceptable success rate.

In this paper, fatigue lives of tensile-shear multi-spot welded joints with four spot per specimen with different arrangements have been investigated. For this purpose, three types of joints with axial, transverse and almond-shaped have been selected. The prediction of fatigue life of spot welded joints has been carried out based on the available S-N curve of smooth specimens and the values of fatigue strength reduction factors. The experimental results and numerical predictions were compared and it was observed a good agreement between experimental and numerical ones. In addition, the Experimental Modal Analysis (EMA) was applied on the specimens before and after different fatigue loadings. Then percent of the identified natural frequencies decreasing was investigated and its correlation with crack initiation and total fatigue life of spot welded joints was studied.

In this paper, a new method is introduced for recognition, location, and severity of damage in engineering stochastic structures, based on Autoregressive Moving Average (ARMA) parametric model and fuzzy classification. The important aspect of the proposed method is the fuzzy viewpoint on stochastic structural damage diagnosis, which uses estimated ARMA parameters as feature vector. Moreover, the proposed method eliminates the optimization stage in finding membership functions parameters of fuzzy system by substituting the variances of estimated ARMA parameters directly as tuning parameters in membership functions. Another important aspect of the proposed method is the inessentiality to measure the excitation input force applied to the structure. A finite element model of a frame for diagnosing damage, wherein the damage is modeled by different stiffness reduction and location, is considered as a case study. After obtaining satisfactory results from numerical simulations, the proposed method is applied to a simply supported beam as an experimental laboratory structure, where the spring connected to the structure in different locations with different stiffness is considered as the damaged object. the results are considerably satisfactory.