The aim of this work is the application of the phase variation in VIBRATION SIGNAL for fault detection on rotating machines in the non stationary cases. The VIBRATION SIGNAL from the machine is modulated in amplitude and phase around a carrier frequency. The modulating SIGNAL in phase is determined after the Hilbert transform and is used, with the Fast Fourier Transform, to extract the harmonics spectrum in phase. This method is first validated on a simulator of VIBRATION, then it is used for detecting potential faults on a rotor of a centrifugal compressor. In the case study of the centrifugal compressor, the nature of the defect was not revealed by the spectral analysis of amplitude of the non stationary VIBRATION SIGNAL, unlike with the proposed method.

Monitoring of fluidized bed hydrodynamics is an essential issue. In this work, a new method, based on comparison of attractors of reference and evaluation time series, was applied to investigate the fluidized bed hydrodynamics by analyzing the bed VIBRATION signatures. The experiments were carried out gas-solid fluidized of 15 cm inner diameter and 2 m height for three particle sizes, different velocities, and three probe heights. The measured SIGNALs of different sand and aspect ratio were compared based on the null hypothesis. The attractor of a system has been reconstructed by applying time delay embedding theory on measured values. The results indicate that the S-statistics method can detect small Changes in gas velocities, the particle size and aspect ratio of the sand. The importance of this method is its ability on the detecting of changes in the hydrodynamic of fluidized bed and provide of changes for controlling these variations.

Fault detection of ball bearings by the complex and non-stationary VIBRATION SIGNALs with noise is very difficult, especially at the early stages. Also, many failure mechanisms and various adverse operating conditions in ball bearings involve significant nonlinear dynamical properties. The quality of chaotic VIBRATION of ball bearings is studied by the reconstructed phase space. The phase space demonstrates different chaotic VIBRATION of ball bearing for different healthy/faulty conditions. But, to easily use of this procedure in the ball bearing fault detection, the chaotic behavior of VIBRATION SIGNAL is quantified by a set of new features. The new set of features based on chaotic behavior, including the largest Lyapunov exponent, approximate entropy and correlation dimension are extracted to acquire more fault characteristic information. The effectiveness of the new features based on chaotic VIBRATIONs in the ball bearing fault detection is demonstrated by the experimental data sets. The proposed approach can reliably recognize different fault types and have more accurate results. Also, the performance of the new procedure is robust to the variation of load values and shows good generalization capability for various load values.

Degradation of rivers and the associated loss of biodiversity reduces ecosystem health and water quality. One of the best practical approaches to understand ecological status of a water body and determine impacts of human activities in reducing water quality is the use of benthic macroinvertebrates as evaluation tools for monitoring their biological integrity and health. The aim of this study was to evaluate the health of Zarin Gol River using SIGNAL index. Macroinvertebrate samples were taken using Surber sampler (an area of 900 cm2) with 3 replicates in 4 sampling sites (upstream, entrance of fish farm, forest area) in winter and spring seasons on Zarin Gol River. The total number of abundance of macroinvertebrate were counted 1971 belonging to 8 order, 19 families. The result showed that SIGNAL index among all stations are same and lies in quadrant a. but SIGNAL 2 score result indicates that station 3 lies in quadrant b. In general, based on the results of the distribution of macroinvertebrates and biotic index, the influence of these factors on Zarin Gol river is quite evidence and the stations that were affected by a variety of effluents (2, 4) had undesirable conditions in the studied river.

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.

Considering the human and financial costs resulting from the destruction of dams, studying the seismic behavior of these structures and controlling their stability against events like earthquakes is crucially important. Numerical models are utilized to carry out this vital process. However, these models possess many assumptions that differ between mathematic models and reality. As a result, experts use system identification methods to ease such problems. Generally, system identification consists of two parts; experimental tests and SIGNAL processing. The first part refers to field experiments like ambient VIBRATION tests, and the latter section means analyzing records gathered from the field tests. From the estimated dynamic properties, the mathematical models can be calibrated to evaluate the possible responses of the building under study to future earthquakes.The ambient VIBRATION test, used in this paper, is one of the dynamic structural tests which records natural VIBRATIONs such as wind and human activities on the structures. These oscillations stimulate the structure’s dynamic properties on a marginal scale which can be seen and analyzed in seismic records. Recently, an ambient VIBRATION experiment was conducted on the double curvature concrete Dez dam, which is one of the highest dams in the country. The dam is currently 203 meters high, and a project is being undertaken to add around 8 meters to its current height. For this reason, evaluating the dynamic characteristics of its body and calibrating the existing dam’s numerical models is vital. In this regard, the ambient VIBRATION test with 19 seismometers and accelerometers was conducted when the height of the dam reservoir was at its maximum water level (MWL). Sensors were placed on the dam in four different layouts to obtain dynamic mode shapes from the plan and the dam’s height.In this paper, some stationary records have been selected from data recorded within several days, and the data are analyzed with three different methods. These methods are 4-Spectrum, Frequency Domain Decomposition (FDD), and Enhanced Frequency Domain Decomposition (EFDD). Each of the techniques used in this research has advantages and disadvantages. The 4-spectrum method is a reliable method for obtaining the modal frequencies owing to the simultaneous control of the four spectral with each other. However, since all sensors' spectrums must be separately controlled by different source points, some possible errors lie in this approach. In the FDD, the power spectrum density and correlation spectrum are automatically controlled by the computer, and a powerful mathematical tool called SVD is used to analyze the data matrix. The EFDD has a similar structure to the FDD method, while it uses a special algorithm to select the peaks in the spectrums. This makes it slightly more accurate than FDD. Furthermore, to calculate modal damping, the Half Power method was used in all these methods.After analyzing all the records with different system identification methods, natural frequencies in six modes were found in the frequency range of 2.63 Hz to 13.92 Hz and mode shapes in five modes were drawn. Likewise, the damping ratio was estimated at 1.8% to 0.6%, which shows the half-power method is not an accurate way to calculate the damping ratio in concrete dams. Finally, the process of system identification, using field experiments, is an experimental process, for obtaining more accuracy, the results extracted from the Dez dam were compared with the outcome of Birkeh Dam's essay, which has the same features as the Dez dam. The comparison of the system identification results shows that the extracted outcomes are relatively precise, which is suitable for calibrating the existing computer models.

Fault Diagnosis of gearboxes using VIBRATIONs is one of the most common methods in industry, which is performed by measuring and analyzing the transverse or torsional SIGNALs. The purpose of this research is to analyze the torsional VIBRATION SIGNALs in time and frequency domain in comparison with the transverse VIBRATION SIGNALs at different speeds, and then to determine the advantage of the torsional VIBRATION SIGNALs for detecting the crack defect of the gearbox. To this end, the time-varying mesh stiffness of the gear pair has been extracted using the potential energy method for healthy and defective teeth. Then, the dynamic equations of the system are derived by the lumped parameter method and the transverse and torsional VIBRATION SIGNALs of the gearbox are obtained. In the following, the VIBRATION SIGNALs at different speeds has been analyzed in the time and frequency domain. The results show that the amplitude of frequency components, including mesh frequency and side bands, in the transverse VIBRATION SIGNAL, is highly dependent on the working speed of the gearbox. However, by speed change, the torsional VIBRATION SIGNAL, the VIBRATION amplitudes at the mentioned components have little changes. This issue becomes important in the fault diagnosis process. Therefore, by using the torsional VIBRATION SIGNAL, it is possible to survey the defect growth at different speeds (specially low speeds) of the system better than the transverse VIBRATION SIGNAL.

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.

Strain gage mounted on the surface of the shafts with specified directions are one of the common methods for measurement of the torsion in shafts. The most criticalproblem in utilization of this method, particularly in the online monitoring of torsionalVIBRATION and instantaneous torque in rotary systems, is due to its SIGNAL transferprocess. In this paper, a simple, practical and feasible method is introduced based on the usage of an unionized fluid medium with adequate electrical conductivity, encapsulated in a cylindrical connector aligned along the shaft, with a rotatingchamber (rotor part) and a stationary one (stator part). In this method, there is no need for installation of the power supply on the shaft and both of the supply voltage and thegenerated electrical SIGNALs are transferred via the unionized fluid. Simplicity in designand high SIGNAL to noise ratio, may candidate this method for torsional VIBRATIONmeasurement. The efficiency of the measurement system is examined on a test setup, for static and dynamic (transient) torsions. Moreover, the applicability of the device isobserved on a rotating shaft.

Bearings are the most important and most used components in different industries. Early bearing fault diagnosis can prevent human and financial losses. One of the best methods for fault diagnosis of these elements is via VIBRATION analysis. In this paper Empirical Mode Decomposition (EMD) which is a fairly new SIGNAL processing method of nonlinear and nonstationary SIGNALs is used for analyzing VIBRATION SIGNALs extracted from bearings. This method was proposed by Huang in 1998. In this research, extracted SIGNAL from healthy and faulty bearings are decomposed in to empirical modes. By analyzing different empirical modes from 8 derived empirical modes for healthy and faulty bearings under different load conditions from zero to three horsepower, the first mode has the most information to classify bearing condition. From the first empirical mode six features in time domain were calculated for healthy bearing, bearing with inner race fault, bearing with outer race fault and bearing with ball fault. These eight features were used as input vector to a designed ANFIS network for bearing condition classification. The ANFIS network was able to detect different condition of bearing with 100% precession.