scholarly journals ІНТЕРПРЕТАЦІЯ ВІБРАЦІЙНИХ СИГНАЛІВ СКЛАДНОЇ РОТОРНОЇ СИСТЕМИ НА ОСНОВІ ФРАКТАЛЬНОГО АНАЛІЗУ

2019 ◽  
pp. 114-121
Author(s):  
Надія Іванівна Бурау ◽  
Ольга Ярославівна Паздрій
Keyword(s):  
Hurst Exponent ◽  
Additive Noise ◽  
Harmonic Excitation ◽  
Rotor Speed ◽  
Vibration Signals ◽  
Vibration Excitation ◽  
Modes Of Vibration ◽  
Stationary Vibration ◽  
Harmonic Components

The work analyzes vibration signals obtained by simulating a turbine of a complex rotor system, for example, an aviation gas turbine engine, under conditions of stationary and non-stationary excitations. Four modes of vibration excitation are considered: stationary poly-harmonic excitation with the frequency of rotor rotation and super-harmonic components; stationary poly-harmonic excitation with the frequency of rotor rotation and sub-harmonic components; non-stationary vibration excitation with a linear increase in the rotor speed with super-harmonic and sub-harmonic components of the instantaneous rotor speed. In the course of the turbine model, vibration signals are generated, which are further analyzed without taking into account and taking into account additive noise. For signal processing, fractal and time-scale (wavelet) analysis were used. The determination of the fractal structure of the simulated vibration signals is made based on R / S analysis, or the method of normalized scope, as a result of which the Hurst exponent is determined. The Hurst exponent is a number that is interpreted as the ratio of the “strength” of a trend to the signal noise level and is used in the study to interpret the received vibration signals. The results showed that the vibration signals obtained in all considered modes of vibration excitation without taking into account the additive noise, in terms of the Hurst exponent, are classified as anti-persistent trend-non-stable signals. Taking into account additive noise, the Hurst exponent increases, the vibration properties in stationary excitation modes approach persistence and the appearance of a trend, and in non-stationary vibration excitation signals approach to processes such as white noise. For the vibration signal obtained at stationary poly-harmonic excitation with super-harmonic components, a preliminary wavelet - decomposition was carried out into a set of approximations and details, followed by determination of the Hurst exponent for each element of decomposition. The results obtained showed an ambiguous change in the Hurst exponent for various decomposition elements. The obtained results can be used to improve the methodological and algorithmic support systems for functional diagnostics of complex rotor systems with the appearance and propagation of damage to their rotating elements.

Robot Manipulator Trajectory Synthesis for Minimal Vibrational Excitation

1997 ◽  
Author(s):  
W. Kim ◽  
J. Rastegar
Keyword(s):  
Robot Manipulator ◽  
Vibrational Excitation ◽  
High Harmonic ◽  
Harmonic Excitation ◽  
Tracking Accuracy ◽  
Natural Modes ◽  
Starting Point ◽  
Modes Of Vibration ◽  
Operating Space ◽  
Harmonic Components

Abstract As a robot manipulator is forced to track a given trajectory, the required actuating torques (forces) may excite the natural modes of vibration of the system. Due to their nonlinear dynamics, internally and externally induced high harmonic excitation torques are generally generated even though such harmonics have been eliminated from the synthesized trajectories and filtered from the drive inputs. It is therefore desirable to synthesize trajectories such that the actuating torques required to realize them do not contain higher harmonic components with significant amplitudes. In this paper, a systematic method is presented for synthesizing such trajectories. With such trajectories, a robot manipulator can operate at higher speeds and achieve higher tracking accuracy with suppressed residual vibration. It is shown that in general and for a given starting point, such trajectories can only be synthesized to a portion of the operating space of the manipulator. The method is developed based on the Trajectory Pattern Method (TPM). The application of the method to optimal trajectory synthesis for a plane 2R manipulator is presented.

Modeling of non-stationary vibration signals based on the modified Kronecker sequences

PAMM ◽  
2015 ◽  
Vol 15 (1) ◽  
pp. 195-196 ◽  
Author(s):  
Iulian Girip ◽  
Ligia Munteanu
Keyword(s):  
Vibration Signals ◽  
Stationary Vibration

The harmonic and transient electromagnetic response of a thin dipping dike

Geophysics ◽  
1983 ◽  
Vol 48 (7) ◽  
pp. 934-952 ◽  
Author(s):  
P. Weidelt
Keyword(s):  
Formal Solution ◽  
Harmonic Excitation ◽  
Electromagnetic Response ◽  
Approximate Determination ◽  
Finite Conductivity ◽  
Circular Loop ◽  
Decay Modes ◽  
Transient Electromagnetic ◽  
Free Decay

An exact solution is given for the electromagnetic induction in a dipping dike of finite conductivity, represented as a thin half‐sheet in a nonconducting surrounding. The problem is formulated for arbitrary dipole or circular loop [Formula: see text] configurations. The formal solution obtained by the Wiener‐Hopf technique is cast into a rapidly convergent triple integral suitable for an effective numerical treatment. A good agreement is found between numerical results and analog measurements available for harmonic excitation. The transient response is obtained as a superposition of the half‐sheet free‐decay modes and is illustrated by some numerical examples for coincident loops, including a diagram for the approximate determination of conductance and depth of a vertical dike.

Determination of Hurst exponent by optical signal processing applied on surface roughness measurements

2009 ◽  
Author(s):  
José Antonio Marbán Salgado ◽  
Oscar Sarmiento Martínez ◽  
Darwin Mayorga Cruz ◽  
Jorge Uruchurtu Chavarín
Keyword(s):  
Surface Roughness ◽  
Signal Processing ◽  
Hurst Exponent ◽  
Optical Signal Processing ◽  
Optical Signal ◽  
Roughness Measurements

A Harmonic Tracking Technique for Recovery of Gear Motion From Non-Stationary Vibration Signals

1995 ◽  
Author(s):  
Christopher A. Lerch ◽  
Richard H. Lyon
Keyword(s):  
Diagnostic System ◽  
Time Spectrum ◽  
Time Dependent ◽  
Invasive Technique ◽  
Signal Distortion ◽  
Vibration Signals ◽  
Tracking Method ◽  
Non Invasive ◽  
Stationary Vibration ◽  
Short Time

Abstract A method termed harmonic tracking is developed to recover time dependent gear motion from machine casing vibration. The harmonic tracking method uses short-time spectral generation and a subsequent set of algorithms to locate and track gear meshing frequencies as functions of time. The meshing frequencies are then integrated with respect to time to obtain the rotation of individual gears. More specifically, spectral generation is performed using the discrete Fourier transform, and the locating and tracking algorithms involve locating tones in each short-time spectrum and tracking them through successive spectra to recover gear meshing harmonics. The harmonic tracking method is found to be more robust than demodulation-based methods in the presence of measurement noise and signal distortion from the structural transfer function between gears and the casing. The harmonic tracking method is tested, both through simulation and experiments involving motor-operated valves (MOV’s) as part of the development of a diagnostic system for MOV’s. In all cases, the harmonic tracking method is found to recover gear motion with sufficient accuracy to perform diagnostics. The harmonic tracking method should be generally applicable to situations in which a non-invasive technique is required for determining the time-dependent angular speeds and displacements of gearbox input, intermediary, and output shafts.

Analysis of Stall Precursors and Determination of Optimum Signal Processing Methods for an LM-2500 Gas Turbine

2008 ◽  
Author(s):  
Knox T. Millsaps ◽  
Gustave C. Dahl ◽  
Daniel E. Caguiat ◽  
Jeffrey S. Patterson
Keyword(s):  
Time Series ◽  
Signal Processing ◽  
Gas Turbine ◽  
Rotor Speed ◽  
Processing Methods ◽  
Turbine Engine ◽  
Pressure Signal ◽  
Time Series Analyses ◽  
Signal Processing Methods

This paper presents an analysis of data taken from several stall initiation events on a GE LM-2500 gas turbine engine. Specifically, the time series of three separate pressure signals located at compressor stages 3, 6, and 15 were analyzed utilizing various signal processing methods to determine the most reliable indicator of incipient stall for this engine. The spectral analyses performed showed that rotating precursor waves traveling around the annulus at approximately half of the rotor speed were the best indicators. Non-linear chaotic time series analyses were also used to predict stall, but it was not as reliable an indicator. Several algorithms were used and it was determined that stall wave perturbations can be reliably identified about 900 revolutions prior to the stall. This work indicates that a single pressure signal located at stage 3 on an LM-2500 gas turbine is sufficient to provide advance warning of more than 2 seconds prior to the fully developed stall event.

Joint Wavelet Transform and Time Synchronous Averaging Source Separation Method and its Application on Aero-Engine

2020 ◽  
Author(s):  
Jiqing Cong ◽  
Jianping Jing ◽  
Changmin Chen ◽  
Zezeng Dai ◽  
Jianhua Cheng
Keyword(s):  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Fundamental Frequency ◽  
Averaging Method ◽  
Gaussian White Noise ◽  
Rotating Machinery ◽  
Vibration Source ◽  
Vibration Signals ◽  
Aero Engine ◽  
Harmonic Components

Abstract The reliability and safety of aero-engine are often the decisive factors for the safe and reliable flight of commercial aircraft. Hence, the vibration source location and fault diagnosis of aero-engine are of prime importance to detect faults and carry out fast and effective maintenance in time. However, the vibration signals collected by the sensors arranged on the casing of the aero-engine are generally the mixed signals of the main vibration sources inside the engine, and the components are extremely complicated. Therefore, the vibration source identification is a big challenge for a fault diagnosis and health management of the engine. In order to separate the key vibration sources of rotating machinery such as aero-engine, a Joint Wavelet Transform and Time Synchronous Averaging based algorithm (JWTS) is proposed in this paper. Based on the fact that the fundamental frequency and its harmonic and sub-harmonic components are generally included in the vibration spectrum of shaft fault signal of rotating machinery, wavelet transform and time synchronous averaging algorithm are combined to extract them. The algorithm completes separating the main vibration sources with three steps. First, the source number and fundamental frequency of each source are estimated using the wavelet transform. Second, every source is extracted from each observed signal by the time synchronous averaging method. Time synchronous averaging method can effectively extract a signal of cycle and harmonic rotor components and can suppress noise. Third, the optimal estimation of each source is determined according to signal’s 2-norm. Since the extracted source with a larger energy is closer to the real source, and signal’s 2-norm is a good indicator of the signal energy. Hence, the key vibration sources related to rotary speeds of the engine are obtained separately. The method is verified by synthetic mixed signals first. Three periodic signals of different frequencies are used to simulate the vibration sources of the aeroengine. The fundamental, harmonic and sub-harmonic components of them, as well as Gaussian white noise, are randomly mixed. The results show that the JWTS algorithm can estimate the number of the main sources and can extract each source effectively. Then the method is demonstrated using vibration signals of a real aero-engine. The results indicate that the proposed JWTS method has extracted and located the main sources within the aero-engine, including sources from the low-pressure rotor, high-pressure rotor, combustion chamber and accessory. Therefore, the proposed method provides a new fault diagnosis technology for rotating machinery, especially for a real aero-engine.

scholarly journals Nonlinear Blind Source Separation and Fault Feature Extraction Method for Mining Machine Diagnosis

2019 ◽  
Vol 9 (9) ◽  
pp. 1852 ◽  
Author(s):  
Hua Ding ◽  
Yiliang Wang ◽  
Zhaojian Yang ◽  
Olivia Pfeiffer
Keyword(s):  
Blind Source Separation ◽  
Additive Noise ◽  
Rbf Neural Network ◽  
Source Separation ◽  
Mining Machine ◽  
Vibration Source ◽  
Vibration Signals ◽  
Feature Extraction Method ◽  
Strongly Nonlinear ◽  
Nonlinear Mixtures

Mining machines are strongly nonlinear systems, and their transmission vibration signals are nonlinear mixtures of different kinds of vibration sources. In addition, vibration signals measured by the accelerometer are contaminated by noise. As a result, it is inefficient and ineffective for the blind source separation (BSS) algorithm to separate the critical independent sources associated with the transmission fault vibrations. For this reason, a new method based on wavelet de-noising and nonlinear independent component analysis (ICA) is presented in this paper to tackle the nonlinear BSS problem with additive noise. The wavelet de-noising approach was first employed to eliminate the influence of the additive noise in the BSS procedure. Then, the radial basis function (RBF) neural network combined with the linear ICA was applied to the de-noised vibration signals. Vibration sources involved with the machine faults were separated. Subsequently, wavelet package decomposition (WPD) was used to extract distinct fault features from the source signals. Lastly, an RBF classifier was used to recognize the fault patterns. Field data acquired from a mining machine was used to evaluate and validate the proposed diagnostic method. The experimental analysis results show that critical fault vibration source component can be separated by the proposed method, and the fault detection rate is superior to the linear ICA based approaches.

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