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.

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.

scholarly journals Confirmation of Thermal Images and Vibration Signals for Intelligent Machine Fault Diagnostics

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Achmad Widodo ◽  
Djoeli Satrijo ◽  
Toni Prahasto ◽  
Gang-Min Lim ◽  
Byeong-Keun Choi
Keyword(s):  
Vibration Signal ◽  
Rolling Element Bearing ◽  
Self Organizing Map ◽  
Fault Diagnostics ◽  
Vibration Signals ◽  
Thermal Images ◽  
Outer Race ◽  
Intelligent Machine ◽  
Machine Fault ◽  
The Time Domain

This paper deals with the maintenance technique for industrial machinery using the artificial neural network so-called self-organizing map (SOM). The aim of this work is to develop intelligent maintenance system for machinery based on an alternative way, namely, thermal images instead of vibration signals. SOM is selected due to its simplicity and is categorized as an unsupervised algorithm. Following the SOM training, machine fault diagnostics is performed by using the pattern recognition technique of machine conditions. The data used in this work are thermal images and vibration signals, which were acquired from machine fault simulator (MFS). It is a reliable tool and is able to simulate several conditions of faulty machine such as unbalance, misalignment, looseness, and rolling element bearing faults (outer race, inner race, ball, and cage defects). Data acquisition were conducted simultaneously by infrared thermography camera and vibration sensors installed in the MFS. The experimental data are presented as thermal image and vibration signal in the time domain. Feature extraction was carried out to obtain salient features sensitive to machine conditions from thermal images and vibration signals. These features are then used to train the SOM for intelligent machine diagnostics process. The results show that SOM can perform intelligent fault diagnostics with plausible accuracies.

scholarly journals The Comparison of the Effect of Haimming Window and Blackman Window in the Time-Scaling and Pitch-Shifting Algorithms

2011 ◽  
Vol 1 ◽  
pp. 221-225
Author(s):  
Zhi Wei Lin ◽  
Li Da ◽  
Hao Wang ◽  
Wei Han ◽  
Fan Lin
Keyword(s):  
Fourier Transform ◽  
Frequency Domain ◽  
Time Domain ◽  
Frequency Domain Method ◽  
Domain Type ◽  
Domain Method ◽  
Time Scaling ◽  
Window Functions ◽  
Inherent Frequency ◽  
The Time Domain

The real-time pitch shifting process is widely used in various types of music production. The pitch shifting technology can be divided into two major types, the time domain type and the frequency domain type. Compared with the time domain method, the frequency domain method has the advantage of large shifting scale, low total cost of computing and the more flexibility of the algorithm. However, the use of Fourier Transform in frequency domain processing leads to the inevitable inherent frequency leakage effects which decrease the accuracy of the pitch shifting effect. In order to restrain the side effect of Fourier Transform, window functions are used to fall down the spectrum-aliasing. In practical processing, Haimming Window and Blackman Window are frequently used. In this paper, we compare both the effect of the two window functions in the restraint of frequency leakage and the performance and accuracy in subjective based on the traditional phase vocoder[1]. Experiment shows that Haimming Window is generally better than Blackman Window in pitch shifting process.

scholarly journals Vibrations in CDFW

Entropy ◽  
2020 ◽  
Vol 22 (6) ◽  
pp. 704
Author(s):  
Daniel Soares de Alcantara ◽  
Pedro Paulo Balestrassi ◽  
José Henrique Freitas Gomes ◽  
Carlos Alberto Carvalho Castro
Keyword(s):  
Time Domain ◽  
Low Frequency ◽  
Welding Process ◽  
Statistical Characteristics ◽  
Crest Factor ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
Complex Process ◽  
The Time Domain ◽  
Peak Value

Continuous drive friction welding is a solid-state welding process that has been experimentally proven to be a fast and reliable method. This is a complex process; deformations in the viscosity of a material alter the friction between the surfaces of the pieces. All these dynamics cause changes in the vibration signals; the interpretation of these signals can reveal important information. The vibration signals generated during the friction and forging stages are measured on the stationary part of the structure to determine the influence of the manipulated variables on the time domain statistical characteristics (root mean square, peak value, crest factor, and kurtosis). In the frequency domain, empirical mode decomposition is used to characterize frequencies. It was observed that it is possible to identify the effects of the manipulated variables on the calculated statistical characteristics. The results also indicate that the effect of manipulated variables is stronger on low-frequency signals.

scholarly journals Transmission compensated primary reflection retrieval in the data domain and consequences for imaging

Geophysics ◽  
2019 ◽  
Vol 84 (4) ◽  
pp. Q27-Q36 ◽  
Author(s):  
Lele Zhang ◽  
Jan Thorbecke ◽  
Kees Wapenaar ◽  
Evert Slob
Keyword(s):  
Thin Layer ◽  
Time Domain ◽  
Velocity Model ◽  
Original Data ◽  
Transmission Losses ◽  
Multiple Reflections ◽  
Data Set ◽  
Numerical Examples ◽  
The One ◽  
The Time Domain

We have developed a scheme that retrieves primary reflections in the two-way traveltime domain by filtering the data. The data have their own filter that removes internal multiple reflections, whereas the amplitudes of the retrieved primary reflections are compensated for two-way transmission losses. Application of the filter does not require any model information. It consists of convolutions and correlations of the data with itself. A truncation in the time domain is applied after each convolution or correlation. The retrieved data set can be used as the input to construct a better velocity model than the one that would be obtained by working directly with the original data and to construct an enhanced subsurface image. Two 2D numerical examples indicate the effectiveness of the method. We have studied bandwidth limitations by analyzing the effects of a thin layer. The presence of refracted and scattered waves is a known limitation of the method, and we studied it as well. Our analysis indicates that a thin layer is treated as a more complicated reflector, and internal multiple reflections related to the thin layer are properly removed. We found that the presence of refracted and scattered waves generates artifacts in the retrieved data.

Wavelets base Automatic Extraction Technology for Vibration Signal Characteristics of Hybrid Electric Vehicle Engine

2019 ◽  
Vol 11 (5) ◽  
Author(s):  
Dongfang Song ◽  
Guanfei Yin
Keyword(s):  
Frequency Domain ◽  
Time Domain ◽  
Energy Analysis ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Domain Analysis ◽  
Extraction Technology ◽  
Vibration Signals ◽  
Engine Vibration ◽  
Signal Characteristics

Traditional automatic characteristic extraction technology of engine vibration signals for hybrid electric vehicles (HEV) only focuses on the analysis of engine vibration signals in time domain and frequency domain. Single time domain analysis or single frequency domain analysis cannot accurately analyse the vibration signals, while both time domain analysis and frequency domain analysis have cross-analysis. As a result, the analysis results are repetitive and conflicting, which makes it difficult to extract the characteristics of engine vibration signals. The final extraction accuracy is not high and the extraction efficiency is low. For this reason, an automatic characteristic extraction technology of HEV engine vibration signal based on wavelet packet energy analysis is proposed. Firstly, the mechanical vibration of engine is converted into corresponding voltage and current signals by various sensors and then converted into digital signals by A/D (analog/digital) conditioner. The data of vibration signals are often mixed with various noises, which have a great impact on the final analysis of vibration signals. Data interception and pre-filtering are adopted. Wave, zero-mean, elimination of trend term and elimination of staggered points are used to pre-treat the vibration signals with mixed noise. Short-Time Fourier Transform (STFT) algorithm is introduced to analyse the pre-processed engine vibration signals and the fundamental properties of the non-stationary vibration signals in actual operation of the engine are obtained. The energy distribution of the analysed engine vibration signal is calculated by the wavelet packet energy analysis method. The calculated parameters of the energy distribution of the wavelet packet are taken as the characteristic parameters of the vibration signal. The vibration signal characteristics of the engine are automatically extracted. The experiment is carried out in the form of comparison with the traditional method. The experimental results show that the time-frequency joint analysis applied in the proposed technology can accurately analyse the essential characteristics of the engine vibration signal of HEV. The wavelet packet energy analysis method can ensure the extraction accuracy of the engine vibration signal characteristics.

Fault features analysis of a compound planetary gear set with damaged planet gears

2017 ◽  
Vol 232 (9) ◽  
pp. 1586-1604 ◽  
Author(s):  
Guoyan Li ◽  
Fangyi Li ◽  
Haohua Liu ◽  
Dehao Dong
Keyword(s):  
Time Domain ◽  
Planetary Gear ◽  
Significant Feature ◽  
Time Interval ◽  
Time Varying ◽  
Mesh Stiffness ◽  
Vibration Signals ◽  
Fault Features ◽  
Fault Properties ◽  
The Time Domain

The fault properties of compound planetary gear set are much more complicated than the simple planetary gear set. A damaged planet will induce two periodic transient impulses in the raw signals and generates modulation sidebands around the mesh harmonics. This paper aims to investigate the fault properties of a compound planetary gear set in damaged planet conditions. A dynamic model is proposed to simulate the vibration signals. The time interval between the fault-induced close impulses in the time domain is used as a significant feature to locate the faulty planet. Considering the phase relations, the time-varying mesh stiffness is obtained. Then, the fault properties are demonstrated in the simulation, and the theoretical derivations are experimentally verified.

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.

Fault Diagnosis of Gear Wearing Based on Order Cepstrum Analysis

2014 ◽  
Vol 543-547 ◽  
pp. 922-925 ◽  
Author(s):  
Hao Tian ◽  
Xiao Yong Kang ◽  
Yong Jian Li ◽  
Jun Nuo Zhang
Keyword(s):  
Time Domain ◽  
Processing Method ◽  
Vibration Signals ◽  
Cepstrum Analysis ◽  
Signal Processing Method ◽  
Start Up ◽  
Rotary Mechanism ◽  
Speed Up ◽  
Stationary Vibration ◽  
The Time Domain

The article explored a new signal processing method called order cepstrum analysis, it can analyze the instantaneous signals of the rotary mechanism, and can process the non-stationary vibration signals such as speed up or speed down signals effectively. Firstly, the start-up vibration signals of the gearbox are sampled at constant time increments in time-domain, then the data are resampled with software at constant angle increments in angle-domain. Therefore, the time domain instantaneously signal is changed into angle domain stationary signal. Then, the stationary signal is analyzed by order cepstrum. From the result we can find that it can avoid the frequency fuzzy phenomenon effectively, which cannot be solved with the traditional frequency spectrum analysis.

Time-Frequency Characteristics of Vibration Signals Analysis for Large Instantaneous Impact Mill Rolling Based on Wavelet Packet

2013 ◽  
Vol 834-836 ◽  
pp. 1061-1064
Author(s):  
Qi Jun Xiao ◽  
Zhong Hui Luo
Keyword(s):  
Time Domain ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Signal Wave ◽  
Reconstruction Technique ◽  
Wavelet Packet Decomposition ◽  
Vibration Signals ◽  
Time Frequency ◽  
Impact Vibration ◽  
Signal Time

The wavelet packet decomposition and reconstruction technique is applied to time-frequency analysis of bite steel impact vibration signal by big rolling machine, it is obtained the bite steel impact signal wave packet. According to the size of the wavelet packet energy, it is reconstructed the signal of No.1 and No.2 wavelet packet. According to reconstruction of the signal time domain waveform and FFT spectrum chart, some meaningful conclusions are obtained.

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