Decomposition of Gear Motion Signals and Its Application to Gearbox Diagnostics

1995 ◽  
Vol 117 (3A) ◽  
pp. 363-369 ◽  
Author(s):  
W. J. Wang ◽  
P. D. McFadden
Keyword(s):  
Time Domain ◽  
Vibration Signal ◽  
Error Signal ◽  
Dynamic Measurements ◽  
Motion Error ◽  
Gear Teeth ◽  
Fundamental Frequencies ◽  
The Time Domain ◽  
Fault Information ◽  
Early Damage

The decomposition of gear motion and the related dynamic measurements for the condition monitoring and fault diagnosis of gearboxes are described. The motion error signal is separated according to fundamental frequencies into the harmonic error and the residual error, which are used to quantify the gear condition. High-order accelerations, such as jerk, are considered and shown to be more sensitive to some classes of early damage to gear teeth. Analysis of the time domain average of a gearbox casing vibration signal enables early detection of gear damage. Several methods to represent and enhance the fault information in the signal are introduced, based on the representation of different forms of the motion errors.

scholarly journals Decoding Speech and Music Stimuli from the Frequency Following Response

2019 ◽  
Author(s):  
Steven Losorelli ◽  
Blair Kaneshiro ◽  
Gabriella A. Musacchia ◽  
Nikolas H. Blevins ◽  
Matthew B. Fitzgerald
Keyword(s):  
Time Domain ◽  
Auditory Perception ◽  
Objective Assessment ◽  
Perceptual Identification ◽  
Complex Sounds ◽  
Linear Discriminant ◽  
Speech Stimuli ◽  
Fundamental Frequencies ◽  
The Time Domain ◽  
Different Response

AbstractThe ability to differentiate complex sounds is essential for communication. Here, we propose using a machine-learning approach, called classification, to objectively evaluate auditory perception. In this study, we recorded frequency following responses (FFRs) from 13 normal-hearing adult participants to six short music and speech stimuli sharing similar fundamental frequencies but varying in overall spectral and temporal characteristics. Each participant completed a perceptual identification test using the same stimuli. We used linear discriminant analysis to classify FFRs. Results showed statistically significant FFR classification accuracies using both the full response epoch in the time domain (72.3% accuracy, p < 0.001) as well as real and imaginary Fourier coefficients up to 1 kHz (74.6%, p < 0.001). We classified decomposed versions of the responses in order to examine which response features contributed to successful decoding. Classifier accuracies using Fourier magnitude and phase alone in the same frequency range were lower but still significant (58.2% and 41.3% respectively, p < 0.001). Classification of overlapping 20-msec subsets of the FFR in the time domain similarly produced reduced but significant accuracies (42.3%–62.8%, p < 0.001). Participants’ mean perceptual responses were most accurate (90.6%, p < 0.001). Confusion matrices from FFR classifications and perceptual responses were converted to distance matrices and visualized as dendrograms. FFR classifications and perceptual responses demonstrate similar patterns of confusion across the stimuli. Our results demonstrate that classification can differentiate auditory stimuli from FFR responses with high accuracy. Moreover, the reduced accuracies obtained when the FFR is decomposed in the time and frequency domains suggest that different response features contribute complementary information, similar to how the human auditory system is thought to rely on both timing and frequency information to accurately process sound. Taken together, these results suggest that FFR classification is a promising approach for objective assessment of auditory perception.

Vibration Signal Time-Domain Characteristic Extraction for Coal and Rock Recognition at the Fully Mechanized Top-Coal Caving Face

2014 ◽  
Vol 556-562 ◽  
pp. 2862-2865 ◽  
Author(s):  
Shi Gang Zhu ◽  
Guang Hui Xue ◽  
Xin Ying Zhao ◽  
Er Meng Liu ◽  
Miao Wu
Keyword(s):  
Time Domain ◽  
Character Recognition ◽  
Vibration Signal ◽  
Working Environment ◽  
Characteristic Extraction ◽  
Coal Recovery ◽  
Working Face ◽  
Index Changes ◽  
The Time Domain ◽  
Signal Time

In order to avoid the over discharge and less discharge situations, improve the coal recovery rate and promote the working environment of workers at the fully mechanized top-coal caving face, experiments for the research of the coal and rock character recognition were carried on underground. Vibration signal at the hydraulic support tail beam and the rear scraper conveyor under different conditions were acquired at the fully mechanized top-coal caving face using the self-developed mine portable data recorder. The time-domain indexes of the vibration signal were analyzed. Results show that the variance, skewness index and kurtosis index changes most obviously under various stage of top coal caving and can be used as the time-domain characteristic indicator for the coal and rock character recognition in the fully mechanized working face.

scholarly journals Early Fault Detection in Bearing with Fault Seeded on Outer Raceway at Three Different Position: Orthogonal, Centered and Opposite

2019 ◽  
Vol 8 (4) ◽  
pp. 6448-6453
Keyword(s):  
Fault Detection ◽  
Time Domain ◽  
Low Voltage ◽  
Vibration Signal ◽  
Industrial Applications ◽  
Fault Detection And Diagnosis ◽  
Economic Losses ◽  
Detection And Diagnosis ◽  
Early Fault Detection ◽  
The Time Domain

Rotating machine such as a small low voltage motor or a power plant generator is an essential asset to the industrial applications. The execution and efficiency of these rotating machines are being reduced due to faulty rotating machinery parts. The faulty parts also generate various forces, thus increases the amplitude of vibration as well as energy consumption. Early fault detection and diagnosis have been widely used with various methods as they were able to reduce accidents and machine breakdowns along with economic losses. This study aims to present the faulty bearings which were seeded in the bearings. The fault size are ranging from 0.007 inches to 0.021 inches in diameter. Among the methods, vibration signal data is one of the champions. In this study, early fault detection was focused on bearing using the time domain technique and the data were analyzed. Particularly, the fault was introduced on the outer raceway at three different positions; orthogonal (3 o’clock), centered (6 o’clock) and opposite (12 o’clock). The MATLAB software was used to determine the time domain parameters, comprising of the standard deviation, Root Mean Square (RMS), skewness and shape factor as the representation of the best reflection of the failure. The time domain parameters for healthy and faulty bearing were plotted and compared in graphical presentation. The result shows all the four parameters have greater value in contrast with the healthy bearing value except for skewness data in the opposite (12 o’clock) position.

scholarly journals Wavelet-Prototypical Network Based on Fusion of Time and Frequency Domain for Fault Diagnosis

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1483
Author(s):  
Yu Wang ◽  
Lei Chen ◽  
Yang Liu ◽  
Lipeng Gao
Keyword(s):  
Fault Diagnosis ◽  
Frequency Domain ◽  
Time Domain ◽  
Vibration Signal ◽  
Training Phase ◽  
Practical Applications ◽  
The Time Domain ◽  
Domain Information ◽  
Machine Learning Models ◽  
Better Than

Neural networks for fault diagnosis need enough samples for training, but in practical applications, there are often insufficient samples. In order to solve this problem, we propose a wavelet-prototypical network based on fusion of time and frequency domain (WPNF). The time domain and frequency domain information of the vibration signal can be sent to the model simultaneously to expand the characteristics of the data, a parallel two-channel convolutional structure is proposed to process the information of the signal. After that, a wavelet layer is designed to further extract features. Finally, a prototypical layer is applied to train this network. Experimental results show that the proposed method can accurately identify new classes that have never been used during the training phase when the number of samples in each class is very small, and it is far better than other traditional machine learning models in few-shot scenarios.

Vibration Test and Fault Diagnosis for Main Hoist Gearbox of Heavy Casting Crane

2014 ◽  
Vol 532 ◽  
pp. 374-377
Author(s):  
Zhang Li ◽  
Xing Dong Wang ◽  
Chang Yi Hu ◽  
Chi Zhong Chen ◽  
Li Ming
Keyword(s):  
Fault Diagnosis ◽  
Time Domain ◽  
Planetary Gear ◽  
Vibration Signal ◽  
Vibration Test ◽  
Signal Characteristic ◽  
Engineering Software ◽  
Fault Type ◽  
The Time Domain ◽  
Main Hoist

In view of the structure and running characteristics of gearbox of large and special crane, we have respectively carried out vibration test of fault and free-fault gearbox containing planetary gear in the work. With the help of Matlab engineering software, we can read and process the collected vibration signal of gearbox and draw the time-domain and frequency-domain graph. Through the comparative analysis of vibration information of gearboxes, we can determine the link between fault type and signal characteristic value, effectively realize the fault diagnosis of gearbox.

scholarly journals Experimental Investigation and Fault Diagnosis for Buckled Wet Clutch Based on Multi-Speed Hilbert Spectrum Entropy

Entropy ◽  
2021 ◽  
Vol 23 (12) ◽  
pp. 1704
Author(s):  
Jiaqi Xue ◽  
Biao Ma ◽  
Man Chen ◽  
Qianqian Zhang ◽  
Liangjie Zheng
Keyword(s):  
Fault Diagnosis ◽  
Time Domain ◽  
Vibration Signal ◽  
Bench Test ◽  
Vehicle Performance ◽  
Vibration Signals ◽  
Hilbert Spectrum ◽  
Wet Clutch ◽  
Diagnosis Method ◽  
The Time Domain

The multi-disc wet clutch is widely used in transmission systems as it transfers the torque and power between the gearbox and the driving engine. During service, the buckling of the friction components in the wet clutch is inevitable, which can shorten the lifetime of the wet clutch and decrease the vehicle performance. Therefore, fault diagnosis and online monitoring are required to identify the buckling state of the friction components. However, unlike in other rotating machinery, the time-domain features of the vibration signal lack efficiency in fault diagnosis for the wet clutch. This paper aims to present a new fault diagnosis method based on multi-speed Hilbert spectrum entropy to classify the buckling state of the wet clutch. Firstly, the wet clutch is classified depending on the buckling degree of the disks, and then a bench test is conducted to obtain vibration signals of each class at varying speeds. By comparing the accuracy of different classifiers with and without entropy, Hilbert spectrum entropy shows higher efficiency than time-domain features for the wet clutch diagnosis. Thus, the classification results based on multi-speed entropy achieve even better accuracy.

Window Functions for the Calculation of the Time Domain Averages of the Vibration of the Individual Planet Gears and Sun Gear in an Epicyclic Gearbox

1994 ◽  
Vol 116 (2) ◽  
pp. 179-187 ◽  
Author(s):  
P. D. McFadden
Keyword(s):  
Detailed Analysis ◽  
Time Domain ◽  
Vibration Signal ◽  
Window Function ◽  
Vibration Signals ◽  
Numerical Examples ◽  
Rectangular Window ◽  
Window Functions ◽  
The Time Domain ◽  
The Individual

An existing technique which enables the estimation of the time domain averages of the tooth meshing vibration of the individual planet and sun gears in an epicyclic gearbox from measured vibration signals has been revised. A key feature of the existing technique is the sampling of the vibration signal within a rectangular window in the time domain when one of the planet gears is close to the vibration transducer. The revised technique permits the use of other window functions, and a detailed analysis shows that the errors in the estimate of the time domain average can be expressed in terms of the window function. Several suitable window functions which enable a reduction in the level of the errors are demonstrated by numerical examples and by the analysis of data from a test on a helicopter gearbox with deliberate damage to one of the planet gears.

Vibration Fault Diagnosis on Gearbox of Belt Conveyor in Coal Mine

2014 ◽  
Vol 543-547 ◽  
pp. 1145-1148 ◽  
Author(s):  
Shi Gang Zhu ◽  
Wei Jian Ding ◽  
Guang Hui Xue
Keyword(s):  
Fault Diagnosis ◽  
Power Spectrum ◽  
Coal Mine ◽  
Time Domain ◽  
Vibration Signal ◽  
Vibration Monitoring ◽  
Belt Conveyor ◽  
Underground Coal Mine ◽  
Input Shaft ◽  
The Time Domain

Belt conveyor gearbox is one of key equipments in coal mine which makes sure that the coal mine runs continuously and smoothly. Once it has faults, it will greatly influence the production and the benefits of coalmine. What is more, the raw coal cut by shearer could not be transported to the ground and serious accidents may occur. The authors carry out the vibration monitoring trials on belt conveyor gearbox in an underground coal mine using self-developed mining portable vibration recorder and obtain amount of on-site vibration signal. After analyzing the variation trend of the time domain indexes and power spectrum, we find that the gear of the input shaft of the reducer has severe wear and broken gears. Overhaul results verifies the correctness of the above analysis and the validity of the data sampled by the recorder.

Application of Mathematical Morphological Filter in Vibration Signal Processing

2011 ◽  
Vol 71-78 ◽  
pp. 4564-4567
Author(s):  
Ai Jun Hu ◽  
Jing Jing Sun ◽  
Wan Li Ma
Keyword(s):  
Signal Processing ◽  
Frequency Domain ◽  
Time Domain ◽  
Nonlinear Filtering ◽  
Vibration Signal ◽  
Morphological Operations ◽  
Morphological Filter ◽  
Original Signal ◽  
Low Pass ◽  
The Time Domain

The morphological filter as a nonlinear filtering method has been widely used for image (or signal) processing. Unlike the traditional digital filters, mathematical morphological operations are shape-based computing. Feature extraction of signals is entirely in the time domain without the transforming of the signal from the time domain to frequency domain. The vibration signal contaminated with noise is processed using morphological filter and Butterworth filter respectively. To compare the outputs of the two filters, we find that morphological filter shows better performance. It is effective in suppressing noise while maintaining the original signal both in the time and frequency domain. In addition, an outstanding advantage of morphological filter is its ability to keep the phase of the original signal. Its computing speed is faster. In the end, its low-pass characteristic is verified by processing vibration signal.

An adaptive multiple time domain synchronous averaging method and its application in vibration signal feature enhancement

2021 ◽  
Author(s):  
Na Yin ◽  
Zong Meng ◽  
Yang Guan ◽  
Fengjie Fan
Keyword(s):  
Time Domain ◽  
Feature Detection ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Difficult Problem ◽  
Multiple Time ◽  
Denoising Method ◽  
Time Domain Signal ◽  
Signal Features ◽  
The Time Domain

Abstract The time domain synchronous averaging (TSA) method is a typical time domain signal denoising method, which is widely used in the state detection of rotating machinery. In order to solve the difficult problem of extracting vibration signal features from strong interference, an adaptive multiple time domain synchronous averaging(aMTSA) method based on signal period is proposed in this paper. In view of the blindness and randomness of period selection in TSA method, a new evaluation index of periodic impulse characteristics is proposed. In this method, the signal is resampled then the iteration stop threshold is set, and then the calculation period of interest is determined by two cycle screening. Finally, reconstructed signals with enhanced features are obtained by copying and stitching. Experimental results show that the proposed method is robust and superior in the feature detection of rolling bearing vibration signals.

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