Vibration Monitoring of Rotating Electrical Machines

2019 ◽  
pp. 163-188
Author(s):  
Achintya Choudhury
Keyword(s):  
Frequency Domain ◽  
Defect Detection ◽  
Electrical Machines ◽  
Vibration Monitoring ◽  
Vibration Measurement ◽  
Rotating Machines ◽  
Display Devices ◽  
Time Frequency ◽  
Measurement And Analysis ◽  
Detection And Diagnosis

Vibration monitoring is applicable to all rotating machines for defect detection and diagnosis. Measurement and analysis of vibration have also been applied to rotating electrical machines with the objective of fault detection and predictive maintenance. The sources of vibration generation in electrical rotating machines, both electrical and mechanical, have been identified in this chapter. The vibratory characteristics associated with these defects have also been discussed in detail. Analyses of vibratory signatures in time domain, frequency domain, and time frequency domain have been dealt with, and different features and indicators associated with each domain have been described. The details of vibration measurement schemes such as transducers, different signal conditioning elements, as well as characteristics of recording and display devices and their applicability to electrical machines have also been included in the chapter.

Wavelet Analysis of Vibration: Part 1—Theory

1994 ◽  
Vol 116 (4) ◽  
pp. 409-416 ◽  
Author(s):  
D. E. Newland
Keyword(s):  
Signal Analysis ◽  
Spectral Composition ◽  
Vibration Measurement ◽  
Harmonic Wavelets ◽  
Time Frequency ◽  
Orthogonal Wavelets ◽  
Vibration Data ◽  
Measurement And Analysis ◽  
Frequency Map ◽  
Selection Of

Wavelets provide a new tool for the analysis of vibration records. They allow the changing spectral composition of a nonstationary signal to be measured and presented in the form of a time-frequency map. The purpose of this paper, which is Part 1 of a pair, is to introduce and review the theory of orthogonal wavelets and their application to signal analysis. It includes the theory of dilation wavelets, which have been developed over a period of about ten years, and of harmonic wavelets which have been proposed recently by the author. Part II is about presenting the results on wavelet maps and gives a selection of examples. The papers will interest those who work in the field of vibration measurement and analysis and who are in positions where it is necessary to understand and interpret vibration data.

scholarly journals Application of Cyclo-Non-Stationary Indicators for Bearing Monitoring Under Varying Operating Conditions

2017 ◽  
Author(s):  
Konstantinos Gryllias ◽  
Simona Moschini ◽  
Jerome Antoni
Keyword(s):  
Frequency Domain ◽  
Condition Monitoring ◽  
Health Monitoring ◽  
Computational Cost ◽  
Rotating Machinery ◽  
Operating Conditions ◽  
Vibration Monitoring ◽  
Cyclic Behavior ◽  
Diagnostic Features ◽  
Time Frequency

Condition monitoring assesses the operational health of rotating machinery, in order to provide early and accurate warning of potential failures such that preventative maintenance actions may be taken. To achieve this target, manufacturers start taking on the responsibilities of engine condition monitoring, by embedding health monitoring systems within each engine unit and prompting maintenance actions when necessary. Several types of condition monitoring are used including oil debris monitoring, temperature monitoring and vibration monitoring. Among them, vibration monitoring is the most widely used technique. Machine vibro-acoustic signatures contain pivotal information about its state of health. The current work focuses on one part of the diagnosis stage of condition monitoring for engine bearing health monitoring as bearings are critical components in rotating machinery. A plethora of signal processing tools and methods applied at the time domain, the frequency domain, the time-frequency domain and the time-scale domain have been presented in order to extract valuable information by proposing different diagnostic features. Among others, an emerging interest has been reported on modeling rotating machinery signals as cyclostationary, which is a particular class of non-stationary stochastic processes. A process x(t) is said to be nth-order cyclostationary with period T if its nth-order moments exist and are periodic with period T. Several tools, such as the Spectral Correlation Density (SCD) and the Cyclic Modulation Spectrum (CMS) can be used in order to extract interesting information concerning the cyclic behavior of cyclostationary signals. In order to measure the cyclostationarity from order 1 to 4, concise and global indicators have been proposed. However, in a number of applications such as aircraft engines and wind turbines the characteristic vibroacoustic signatures of rotating machinery depend on the operating conditions of the rotational speed and/or the load. During the last decades fault diagnostics of rotating machinery under variable speed/load has attracted a lot of interest. The classical cyclostationary tools can be used under the assumption that the speed of machinery is constant or nearly constant, otherwise the vibroacoustic signal becomes cyclo-non-stationary. In order to overcome this limitation a generalization of both SCD and CMS functions have been proposed displaying cyclic Order versus Frequency. The goal of this paper is to propose a novel approach for the analysis of cyclo-nonstationary signals based on the generalization of indicators of cyclostationarity in order to cover the speed varying conditions. The proposed indicators of cyclo-non-stationarity (ICNS) are expected to summarize the information at various statistical orders and at lower computational cost compared to the Order-Frequency SCD or CMS. This generalization is realized by introducing a new speed-dependent angle averaging operator. The effectiveness of the approach is evaluated on an acceleration signal captured on the casing of an aircraft engine gearbox, provided by SAFRAN, in the frames of SAFRAN contest which took place at the Surveillance 8 International Conference.

Vibration Measurement and Fault Diagnosis System for Experimental Teaching

2013 ◽  
Vol 401-403 ◽  
pp. 1226-1229
Author(s):  
Xiao Yan Yang ◽  
You Gang Xiao ◽  
Jian Feng Huang
Keyword(s):  
Signal Processing ◽  
Frequency Domain ◽  
Virtual Instrument ◽  
Experimental System ◽  
Vibration Measurement ◽  
Diagnosis System ◽  
Time Frequency ◽  
Experimental Teaching ◽  
Equipment State ◽  
Signal Processing Methods

Based on LabVIEW, vibration measurement and diagnosis system of equipment was developed for experimental teaching. On the platform, vibration signals from running equipment can be sampled, displayed, stored, and analyzed in time domain, frequency domain and time-frequency domain. The advanced signal processing technology such as power spectrum analysis, cepstrum analysis, demodulation analysis can also be executed. Using pattern recognition technology, the processed signals can be integrated for intelligent diagnosis of equipment state. The experimental system is helpful for students to learn signal processing methods, and to design virtual instrument.

Study on the Defects Size of Ball Bearings Elements Using Vibration Analysis

2014 ◽  
Vol 658 ◽  
pp. 289-294 ◽  
Author(s):  
Carmen Bujoreanu ◽  
Razvan Monoranu ◽  
N. Dumitru Olaru
Keyword(s):  
Frequency Domain ◽  
Vibration Analysis ◽  
Ball Bearing ◽  
Fault Detection And Diagnosis ◽  
Response Measurement ◽  
Time Frequency ◽  
Bearing Life ◽  
Rolling Element ◽  
Detection And Diagnosis ◽  
The Time Domain

The vibration analysis aims to extract features from the measurements in order to be used for fault detection and diagnosis. Vibration response measurement is an important and effective technique for the detection of the defects in rolling element bearings. The corresponding analysis methods operate in the time domain, in the frequency domain and recently in the time-frequency domain. A quantitative determination of the defect severity and its development are useful to be determined in order to estimate the remaining useful ball bearing life. Experimental data from a bearing with a defect are collected by an accelerometer then processed to identify the passing time of a ball over a defect. The paper presents a computation model corroborated to an experimental investigation to establish the defect length of a ball bearing inner race.

Vibration Analysis on Rotating Machines using Fast Fourier Transform (FFT)

2019 ◽  
Vol 3 (1) ◽  
pp. 67-75
Author(s):  
Iip Muhlisin ◽  
Rusman Rusyadi
Keyword(s):  
System Performance ◽  
Vibration Analysis ◽  
Vibration Monitoring ◽  
Initial Condition ◽  
Rotating Machines ◽  
Experimental Frequency ◽  
Vibration Load ◽  
Time Frequency ◽  
Rotating Machine ◽  
Analytical Approaches

The history has record that heavy industries face major problems that causes by variant types of mechanical failures came from rotating machines. The Vibrations in rotating machine almost fond in everywhere, due to unbalances, misalignments and imperfect part, analytical approaches has shown that vibration monitoring has great capability in detecting and addressing the defect particular part in the machine line .The vibration velocities and vibration load will be measured at different speeds using The Time-frequency analysis at initial condition. The result of vibration readings spectrum analysis and phase analysis can be determining the figure of vibrations character, and the causes of height vibration will be found. By reading the spectrum unbalance will be identified. When the unbalanced part was balanced then we found that the vibration was decrease. The Vibration experimental frequency spectrum test will be conduct for both balanced and unbalanced condition and also in different speed conditions. To full fill the vibration analysis test, in this experimental research a prototype of vibration monitoring system was constructed. The vibration can be generated and the system performance can be monitored. In this prototype the signal from load cell and velocity sensor will be processed in microcontroller and send to computer where FFT will processed the signal to create spectrum in the computer display. The actual final result of Vibration analysis test will be provide after finish the vibrations analysis test that will be done latter, therefore the chart result on this paper is based on theoretical only.

Gear Vibration Monitoring System Based on Virtual Instruments

2012 ◽  
Vol 187 ◽  
pp. 161-164
Author(s):  
Chuan Hui Wu ◽  
Yu Guo ◽  
Ya Jun Fan
Keyword(s):  
Frequency Domain ◽  
Monitoring System ◽  
Virtual Instrument ◽  
Vibration Analysis ◽  
Vibration Monitoring ◽  
Test Results ◽  
Time Frequency ◽  
Machinery Condition Monitoring ◽  
Faults Diagnosis ◽  
Developing Platform

Vibration analysis is widely used for gear faults diagnosis. A gear vibration monitoring system based on virtual instrument developing platform LabVIEW and vibration analysis technology is developed and introduced in this paper. It satisfies the requirements of machinery condition monitoring and supports function expansion. Online and offline monitoring of gear running states can be realized though this system in both time domain, frequency domain and joint time –frequency domain. Experiments showed that this gear vibration monitoring system can be widely employed in gearbox. System not only guarantees the accuracy of the test results but also provides a friendly user interface for users’ easy operation.

scholarly journals Application of Cyclo-Nonstationary Indicators for Bearing Monitoring Under Varying Operating Conditions

2017 ◽  
Vol 140 (1) ◽  
Author(s):  
Konstantinos Gryllias ◽  
Simona Moschini ◽  
Jerome Antoni
Keyword(s):  
Frequency Domain ◽  
Condition Monitoring ◽  
Health Monitoring ◽  
Aircraft Engine ◽  
Rotating Machinery ◽  
Operating Conditions ◽  
Vibration Monitoring ◽  
Diagnostic Features ◽  
Time Frequency ◽  
Acceleration Signal

Condition monitoring assesses the operational health of rotating machinery, in order to provide early and accurate warning of potential failures such that preventative maintenance actions may be taken. To achieve this target, manufacturers start taking on the responsibilities of engine condition monitoring, by embedding health-monitoring systems within each engine unit and prompting maintenance actions when necessary. Several types of condition monitoring are used including oil debris monitoring, temperature monitoring, and vibration monitoring. Among them, vibration monitoring is the most widely used technique. Machine vibro-acoustic signatures contain pivotal information about its state of health. The current work focuses on one part of the diagnosis stage of condition monitoring for engine bearing health monitoring as bearings are critical components in rotating machinery. A plethora of signal processing tools and methods applied at the time domain, the frequency domain, the time–frequency domain, and the time-scale domain have been presented in order to extract valuable information by proposing different diagnostic features. Among others, an emerging interest has been reported on modeling rotating machinery signals as cyclo-stationary, which is a particular class of nonstationary stochastic processes. The goal of this paper is to propose a novel approach for the analysis of cyclo-nonstationary signals based on the generalization of indicators of cyclo-stationarity (ICNS) in order to cover the speed-varying conditions. The effectiveness of the approach is evaluated on an acceleration signal captured on the casing of an aircraft engine gearbox, provided by SAFRAN.

scholarly journals Time-Varying Spectral Kurtosis: Generalization of Spectral Kurtosis for Local Damage Detection in Rotating Machines under Time-Varying Operating Conditions

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3590
Author(s):  
Jacek Wodecki
Keyword(s):  
Damage Detection ◽  
Frequency Domain ◽  
Operating Conditions ◽  
Measured Signal ◽  
Time Varying ◽  
Local Damage ◽  
Rotating Machines ◽  
Frequency Bands ◽  
Time Frequency ◽  
Spectral Kurtosis

Vibration-based local damage detection in rotating machines (i.e., rolling element bearings) is typically a problem of detecting low-energy cyclic impulsive modulations in the measured signal. This can be challenging as both the amplitude of a single damage-related impulse and the distance between impulses might be changing in time. From the signal processing point of view, this means time varying regarding the the signal-to-noise ratio, location of information in the frequency domain, and loss of periodicity (this remains cyclic but not periodic). One of the many attempted approaches to this problem is filtration using custom filters derived in a data-driven fashion. One of the methods to obtain such filters is a selector approach, where the value of a certain statistic is calculated for individual frequency bands of a signal that results in the magnitude response of a filter. In this approach, each chosen statistic will yield different results, and the obtained filter will be focused on different frequency bands focusing on different behaviors. One of the most popular and powerful selectors is spectral kurtosis as popularized by Antoni, which uses kurtosis as an operational statistic. Unfortunately, after closer inspection, it is easy to notice that, although selectors can significantly enhance the signal, they accumulate a great deal of noise and other background content of signals, which occupies the space (or rather time) in between the impulses. Another disadvantage is that such filters are time-invariant, because, in the principle of their construction, they are not adaptive, and even slight changes in the signal yield suboptimal results either by missing relevant data when the conditions in the signal change (i.e., informative impulses widen in bandwidth), or by accumulating unnecessary noise when the relevant information is not present (in between impulses or in frequency bands that impulses no longer occupy). To address this issue, I propose generalization of the selector approach using the example of spectral kurtosis. This assumes creating a time-varying selector that can be seen as a spatial filter in the time-frequency domain. The time-varying SK (TVSK) is estimated for segments of the signal, and, instead of a vector of SK-based filter coefficients, one obtains a TVSK-based matrix of coefficients that takes into account the time-varying properties of the signal. The obtained structure is then binarized and used as a filter. The presented method is tested using a simulated signal as well as two real-life signals measured on heavy-duty bearings in two different types of machine.

Sign in / Sign up

Export Citation Format

Share Document