Vibration Signal Extraction of Rotating Machines Based on the Analysis of Degree of Cylcostationary

2012 ◽  
Vol 546-547 ◽  
pp. 188-193 ◽  
Author(s):  
Yu Rong Wang ◽  
Tian Xing Wu
Keyword(s):  
Background Noise ◽  
Vibration Signal ◽  
Sampling Frequency ◽  
Hilbert Transformation ◽  
New Method ◽  
Signal Extraction ◽  
Rotating Machines ◽  
End Effect ◽  
Research Results ◽  
Rotating Machine

When early faults occur in rotating machine, the vibration signal being extraction the often contain heavy background noise .In this paper the analysis of degree of cyclostationarity (DCS) was proposed to vibration signal extraction of rotating machine. This method can overcome the shortcoming of Hilbert transformation that will be influenced by many factors, such as end effect, sampling frequency and the added noise in signal. The research results show that the DCS can not only extract vibration signal but also suppress the added noise in the signal. Therefore more accurate fault signal will be extracted and detected through this new method.

MECHANICAL PULPING.Analysis method of high-consistency chip refiner forces

2012 ◽  
Vol 27 (4) ◽  
pp. 695-701 ◽  
Author(s):  
Antti Fredrikson ◽  
Lauri I. Salminen
Keyword(s):  
Energy Efficient ◽  
Background Noise ◽  
Spatial Information ◽  
New Method ◽  
Force Analysis ◽  
Rotating Machine ◽  
Irregular Data ◽  
High Consistency ◽  
Plate Geometry

Abstract We introduce a new method of processing irregular data rrom any rotating machine, in particular high-consistency pulp refiners. Refiner plate bar forces and rotor absolute angle are acquired synchronously, and a fuzzy gate is exploited to filter instantaneous force events from background noise. Fuzzy gate parameters are partially justified by plate geometry. The method enables the acquisition of quantified and detailed spatial information on refiner plate bar forces, thus supporting the design of more energy efficient refiner plates and bar patterns. The study identifie.s key areas of application for synchronized force analysis in high-consistency pulping research.

Research on Unbalance Signal Extraction under the Constraints of Low Operation Speed and Limited Acquisition Time

2012 ◽  
Vol 516 ◽  
pp. 618-623
Author(s):  
Ding Ding Zhao ◽  
Ping Cai ◽  
Wei Qi
Keyword(s):  
Frequency Domain ◽  
Background Noise ◽  
Field Experiments ◽  
Spectral Feature ◽  
Vibration Signal ◽  
Ar Model ◽  
Signal Extraction ◽  
Acquisition Time ◽  
Extension Method ◽  
Operation Speed

Aiming at extracting sinusoidal signals from strong background noise and disturbance under the constraint of limited cycles of samples, the spectral feature of the picked unbalance vibration signal of a hard bearing balancing machine was analyzed and an analogue filter was designed to eliminate the main disturbance components in the frequency domain, and a signal extension method based on the AR model was introduced and investigated. Simulation and field experiments demonstrated the feasibility of the presented extension method and an improvement in accuracy was achieved by extension of the AR model.

scholarly journals Performance Improvement of Classifier in Fault Diagnosis of Rotating Machines Using Sensor Fusion Techniques

2019 ◽  
Vol 8 (6) ◽  
pp. 1136-1141
Keyword(s):  
Machine Learning ◽  
Fault Diagnosis ◽  
Sensor Fusion ◽  
Feature Fusion ◽  
Learning Algorithm ◽  
Vibration Signal ◽  
Fault Classification ◽  
Rotating Machines ◽  
Rotating Machine ◽  
Vibration Signal Analysis

The shaft, rotor, bearing and gear are the important elements of the rotating machines. Most of the problems in rotating machines are caused due to bearings and shaft. The failure of rotating machine causes production downtime and economic & safety issues. Vibration signal analysis is highly accepted technique in fault diagnosis of rotating machine. For automation of fault diagnosis, machine learning approach has been followed. Machine learning classifies fault based on variation in signatures pattern of the machine. But its effectiveness gets reduced when it is used for multi fault class problem. So in the present work, sound signals are also used along with vibration signals for applying sensor fusion techniques. In sensor fusion, signals from various sensors are fused in three levels such as data fusion, feature fusion and decision level fusion and the fused data sets are used for fault classification using machine learning algorithm. The performance of each technique is studied in detail and compared using classification accuracy. A new method is proposed by combination of fusion techniques to enhance the performance

New Method of Optimal Design of Electrical Rotating Machines

2017 ◽  
Vol 5 (3) ◽  
pp. 479 ◽  
Author(s):  
Flur Ismagilov ◽  
Vyacheslav Vavilov ◽  
Valentina Ayguzina ◽  
Vladimir Bekuzin
Keyword(s):  
Genetic Algorithm ◽  
Optimal Design ◽  
Current Density ◽  
Mathematical Description ◽  
High Speed ◽  
Rotation Frequency ◽  
New Method ◽  
Rotating Machines ◽  
Active Length ◽  
Rotating Machine

The article presents a new method of the optimal design of the electrical rotating machine based on genetic algorithm. The mathematical description of the proposed algorithm is developed, and the optimal design of the high-speed electrical rotating machine by proposed method is performed. A new method for optimal design allows obtaining a new electrical rotating machine which mass is lower than mass of the initial electrical rotating machine by two times; the value of the rotor active length is lower by 2.37 times and the current density is higher by 1.7 times in comparison with the initial electrical rotating machine. The losses are increased by only 25 percent (power, rotation frequency and materials of both electrical rotating machine are the same).

scholarly journals Fault Diagnosis of a Helical Gearbox Based on an Adaptive Empirical Wavelet Transform in Combination with a Spectral Subtraction Method

2019 ◽  
Vol 9 (8) ◽  
pp. 1696 ◽  
Author(s):  
Wang ◽  
Lee
Keyword(s):  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Background Noise ◽  
Vibration Signal ◽  
Spectral Subtraction ◽  
Accurate Identification ◽  
Side Band ◽  
Empirical Wavelet Transform ◽  
Gear Fault ◽  
Fault Characteristic

Fault characteristic extraction is attracting a great deal of attention from researchers for the fault diagnosis of rotating machinery. Generally, when a gearbox is damaged, accurate identification of the side-band features can be used to detect the condition of the machinery equipment to reduce financial losses. However, the side-band feature of damaged gears that are constantly disturbed by strong jamming is embedded in the background noise. In this paper, a hybrid signal-processing method is proposed based on a spectral subtraction (SS) denoising algorithm combined with an empirical wavelet transform (EWT) to extract the side-band feature of gear faults. Firstly, SS is used to estimate the real-time noise information, which is used to enhance the fault signal of the helical gearbox from a vibration signal with strong noise disturbance. The empirical wavelet transform can extract amplitude-modulated/frequency-modulated (AM-FM) components of a signal using different filter bands that are designed in accordance with the signal properties. The fault signal is obtained by building a flexible gear for a helical gearbox with ADAMS software. The experiment shows the feasibility and availability of the multi-body dynamics model. The spectral subtraction-based adaptive empirical wavelet transform (SS-AEWT) method was applied to estimate the gear side-band feature for different tooth breakages and the strong background noise. The verification results show that the proposed method gives a clearer indication of gear fault characteristics with different tooth breakages and the different signal-noise ratio (SNR) than the conventional EMD and LMD methods. Finally, the fault characteristic frequency of a damaged gear suggests that the proposed SS-AEWT method can accurately and reliably diagnose faults of a gearbox.

Parametric Description of Vibration Using the Autocorrelation Function

2009 ◽  
Vol 147-149 ◽  
pp. 606-611
Author(s):  
Adam Kotowski
Keyword(s):  
Autocorrelation Function ◽  
Curve Fitting ◽  
Vibration Signal ◽  
Analytic Form ◽  
Parametric Representation ◽  
Rotating Machine ◽  
Fitting Procedure ◽  
Power Spectral ◽  
Parametric Description ◽  
Harmonic Components

The paper presents the use of the autocorrelation function for the description of vibrations and the problems connected with. The proposed method is based on the analysis of vibration signal recorded for machine during its operations using an analytic form of the autocorrelation function. The parameters are obtained using a curve fitting procedure. To keep a quality of parametric representation of considered vibration, only the curve fitting causes a determination coefficient over 0.90 is taken into consideration. Therefore, the autocorrelation functions are submitted for the fast Fourier transform to be helped, in determination of number of the dominant harmonic components. Also, the analytic form and parameters of power spectral density has been also calculated. Finally, the set of parameters has been collected to describe the selected fragment of vibration of the simple rotating machine. The influence of duration of analyzed vibration on the parameters values is also examined in this work.

Effects of the Shaft Normal Modes on the Model-Based Identification of Unbalances in Rotating Machines

2011 ◽  
Author(s):  
A. Vania ◽  
P. Pennacchi ◽  
S. Chatterton
Keyword(s):  
Normal Modes ◽  
Fault Identification ◽  
Diagnostic Strategy ◽  
Rotating Machines ◽  
Large Power ◽  
Identification Methods ◽  
Model Based ◽  
Rotating Machine ◽  
Experimental Response ◽  
Innovative Techniques

Model-based methods can be applied to identify the most likely faults that cause the experimental response of a rotating machine. Sometimes, the objective function, to be minimized in the fault identification method, shows multiple sufficiently low values that are associated with different sets of the equivalent excitations by means of which the fault can be modeled. In these cases, the knowledge of the contribution of each normal mode of interest to the vibration predicted at each measurement point can provide useful information to identify the actual fault. In this paper, the capabilities of an original diagnostic strategy that combines the use of common fault identification methods with innovative techniques based on a modal representation of the dynamic behavior of rotating machines is shown. This investigation approach has been successfully validated by means of the analysis of the abnormal vibrations of a large power unit.

scholarly journals Two De-Noising Methods Based on Gabor Transform

2011 ◽  
Vol 2-3 ◽  
pp. 176-181
Author(s):  
Yong Jun Shen ◽  
Guang Ming Zhang ◽  
Shao Pu Yang ◽  
Hai Jun Xing
Keyword(s):  
Wavelet Transform ◽  
Adaptive Filtering ◽  
Time Domain ◽  
Background Noise ◽  
Averaging Method ◽  
Vibration Signal ◽  
Gabor Transform ◽  
Transform Domain ◽  
Computation Scheme ◽  
Source Signals

Two de-noising methods, named as the averaging method in Gabor transform domain (AMGTD) and the adaptive filtering method in Gabor transform domain (AFMGTD), are presented in this paper. These two methods are established based on the correlativity of the source signals and the background noise in time domain and Gabor transform domain, that is to say, the uncorrelated source signals and background noise in time domain would still be uncorrelated in Gabor transform domain. The construction and computation scheme of these two methods are investigated. The de-noising performances are illustrated by some simulation signals, and the wavelet transform is used to compare with these two new de-noising methods. The results show that these two methods have better de-noising performance than the wavelet transform, and could reduce the background noise in the vibration signal more effectively.

scholarly journals An online headphone screening test based on dichotic pitch

2020 ◽  
Author(s):  
Alice E. Milne ◽  
Roberta Bianco ◽  
Katarina C. Poole ◽  
Sijia Zhao ◽  
Andrew J. Oxenham ◽  
...  
Keyword(s):  
False Positive ◽  
Background Noise ◽  
Screening Test ◽  
False Positive Rate ◽  
New Method ◽  
Additional Test ◽  
Positive Rate ◽  
Online Methods ◽  
Perceptual Phenomenon

AbstractOnline experimental platforms can be used as an alternative to, or complement, lab-based research. However, when conducting auditory experiments via online methods, the researcher has limited control over the participants’ listening environment. We offer a new method to probe one aspect of that environment, headphone use. Headphones not only provide better control of sound presentation but can also “shield” the listener from background noise. Here we present a rapid (< 3 min) headphone screening test based on Huggins Pitch (HP), a perceptual phenomenon that can only be detected when stimuli are presented dichotically. We validate this test using a cohort of “Trusted” online participants who completed the test using both headphones and loudspeakers. The same participants were also used to test an existing headphone test (AP test; Woods et al., 2017, Attention Perception Psychophysics). We demonstrate that compared to the AP test, the HP test has a higher selectivity for headphone users, rendering it as a compelling alternative to existing methods. Overall, the new HP test correctly detects 80% of headphone users and has a false-positive rate of 20%. Moreover, we demonstrate that combining the HP test with an additional test–either the AP test or an alternative based on a beat test (BT)–can lower the false-positive rate to ~ 7%. This should be useful in situations where headphone use is particularly critical (e.g., dichotic or spatial manipulations). Code for implementing the new tests is publicly available in JavaScript and through Gorilla (gorilla.sc).

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