Nonlinear Vibration Characteristics of the Involute Spline Coupling in Aeroengine with the Parallel Misalignment

In order to control the vibration of the involute spline coupling in aeroengine well and reduce the fretting wear, a bending–torsion coupling nonlinear vibration model of the involute spline coupling with the misalignment was proposed, and a dynamic meshing stiffness function with multiteeth engagement was established. Then, the influence of different misalignment, wear, and rotation speeds with different misalignment on the nonlinear vibration characteristics of the involute aviation spline coupling was explored. The result shows that with an increase of the parallel misalignment, the system experienced the state of a single period, a quasiperiod, multiperiod, and chaos but finally only alternated between the quasiperiod and the chaos state. The uneven wear of each tooth of the spline displayed a significant influence on the vibration of the spline coupling, and the influence of the uniform wear was smaller under given conditions here. Furthermore, with an increase of the speed, the larger the misalignment was, the more times the system entered or left the chaos state were. The model proposed here is found to be closer to the actual working conditions, and the analysis results can provide more accurate external load conditions for the prediction of the fretting damage of the spline coupling in aeroengine.

Analisis Ketidaksesumbuan Poros (Misalignment) pada Rotordinamik Berdasarkan Sinyal Suara

This research aims to identify misalignment of the rotor dynamics based on sound spectrum characteristic. In this study, rotor dynamics consist of motor, shaft, coupling and bearings. Three types of misalignment were considered, namely parallel, angular, and combination misalignment. In order to obtain the best signal, microphones were used as sensors to capture sound signal placed on coupling and each bearing. The signal obtained was in time series. The sound signal in the time domain is then filtered to remove noise signals, which are then transferred to be signals in the frequency domain using Fast Fourier Transform (FFT). From the test results, it is found that in the case of parallel misalignment, the sound frequency spectrum is obtained with a peak amplitude at 2x rpm. The case of angular misalignment obtained a sound spectrum with a peak amplitude value and is dominant at 1x rpm than 2x rpm. Meanwhile, in the case of a combination of parallel and angular misalignment, a peak amplitude sound spectrum appears at 1x rpm and 2x rpm with relatively close spacing between the peaks of the sound spectrum. The result shows that sound signal can be used for identification of misalignment of the rotor dynamics.

Research on Fault Diagnosis of Steam Turbine Rotor Unbalance and Parallel Misalignment Based on Numerical Simulation and Convolutional Neural Network

The steam turbine rotor is still the main power generation equipment. Affected by the impact of new energy on the power grid, the steam turbine needs to participate in peak load regulation, which will make turbine rotor components more prone to failure. The rotor is an important equipment of a steam turbine. Unbalance and misalignment are the normal state of rotor failure. In recent years, more and more attention has been paid to the fault detection method based on deep learning, which takes rotating machinery as the object. However, there is a lack of research on actual steam turbine rotors. In this paper, a method of rotor unbalance and parallel misalignment fault detection based on residual network is proposed, which realizes the end-to-end fault detection of rotor. Meanwhile, the method is evaluated with numerical simulation data, and the multi task detection of rotor unbalance, parallel misalignment, unbalanced parallel misalignment coupling faults (coupling fault called in this paper) is realized. The influence of signal-to-noise ratio and the number of training samples on the detection performance of neural network is discussed. The detection accuracy of unbalanced position is 93.5%, that of parallel misalignment is 99.1%. The detection accuracy for unbalance and parallel misalignment is 89.1% and 99.1%, respectively. The method can realize the direct mapping between the unbalanced, parallel misalignment, coupling fault vibration signals and the fault detection results. The method has the ability to automatically extract fault features. It overcomes the shortcoming of traditional methods that rely on signal processing experience, and has the characteristics of high precision and strong robustness.

MISALIGNMENT DISTANCE ANALYSIS OF VIBRATION AND NOISE VALUE

Penelitian ini bertujuan untuk mengetahui nilai vibrasi yang terjadi pada saat misalignment 0,1, 0,2, 0,3, 0,4, dan 0,5 mm dan mengetahui nilai noise yang terjadi pada saat misalignment 0,1, 0,2, 0,3, 0,4, dan 0,5 mm. Variasi yang digunkan adalah parallel misalignment, angular misalignement dan combination misalignment dengan jarak misalignment 0,1, 0,2, 0,3, 0,4, dan 0,5 mm. Hasil penelitian menunjukkan untuk parallel misalignment vibrasi dan noise tertinggi terjadi pada saat parallel misalignment 0,5 posisi pengujian horizontal sebesar 8,97 mm/s overall velocity, 65,43 µm overall displacement, noise maximum 8 dB dan carpet -6 dB, sedangkan untuk angular misalignment vibrasi dan noise tertinggi terjadi pada saat angular misalignment 0,5 posisi pengujian axial sebesar 10,95 mm/s overall velocity, 76,95 µm overall displacement, noise maximum 5 dB carpet -11 dB dan untuk combination misalignment vibrasi dan noise tertinggi terjadi pada saat combination misalignment 0,5 posisi pengujian axial sebesar 16,33 mm/s overall velocity, 100,95 µm overall displacement noise maximum 13 dB dan carpet 3 dB.

Dynamic Response of a Rotating Assembly under the Coupled Effects of Misalignment and Imbalance

In rotating machinery, the second most common fault after imbalance is misalignment. Misalignment can have a severe impact on equipment and may reduce the machine’s lifetime considerably. In this paper, the simultaneous effect of imbalance and misalignment (parallel or angular) on the vibration spectra of rotating machinery will be discussed. A numerical model is developed and used to obtain the time and frequency responses of the rotor-coupling-bearing system to the simultaneous effect of these faults. The numerical model shows that the imbalance was mainly related to the peak located around 1X, whereas misalignment was linked to the peak around 2X. In addition, the parallel misalignment fault magnifies the 2X amplitude of the displacement response, whereas the response of angular misalignment is captured at the 2X and 4X amplitudes. This study also examines the effects of changing the model’s rotational speed, misalignment level, and coupling type for angular and parallel misalignments.

A novel health indicator developed using filter-based feature selection algorithm for the identification of rotor defects

In this work, a novel health indicator is developed for the identification of rotor defects. The indicator is developed by extracting features from vibration data acquired from horizontal and vertical directions of rotors. A total of 38 features were initially extracted from time-domain signal, frequency-domain signal, and time–frequency representation. Out of many features, six most important features were selected using filter-based feature selection process. Thereafter, important features were fused together using manifold learning to develop health indicator. The developed indicator is used to identify misalignments (angular misalignment and parallel misalignment), rub, and unbalance. The major benefit of the proposed method is that it not only indicates the presence of defect in the rotor but also indicates the severity of defect. The experimental study presented in this article justifies that the proposed method is sensitive to the increasing levels of horizontal and angular misalignment and unbalance. The developed indicator is sensitive enough to indicate the presence of rub.

Experimental Monitoring of Vibration on Centrifugal Pump

The main task of this Project was to summarize the reasons of turbo machine failure and a way of monitoring those causes during the operation of the turbo machine. Furthermore, the analysis of the results of Condition Monitoring (Vibration Monitoring) was explained with a practical experiment performed in the laboratory The results of our measurement indicate a significant variation in vibration trend as a function of operating conditions. The experimental results demonstrated that the vibration monitoring rig modeled various modes of machine failure. Failure can be caused either by single phenomenon or simultaneous phenomenon. Such phenomenon are as follows: Balancing Motor which can become unbalanced sometimes which will affect the measurement itself. Parallel Misalignment (misalignment of pump and motor) Blade pass and Vane pass Vortex shedding FEA Analysis for Modal, Random Vibrations Validation by FFT Base plate Design Furthermore, it can be emphasized that based on my observation during the measurement, when the revolution number was approaching 1500 [RPM], the noise of the turbo machine would dramatically increase when the flow rate was at its maximum. Thus, we can indicate that the noise was as result of high vibration. However, with the same revolution number (rotational speed) but lower flow rates the noise was not that much.