2B24 Application of Vibration Signal Processing for Gear Fault Detection(The 12th International Conference on Motion and Vibration Control)

2014 ◽  
Vol 2014.12 (0) ◽  
pp. _2B24-1_-_2B24-9_
Author(s):  
Woong-Yong Lee ◽  
Hae-Young Ji ◽  
Dong-Hyong Lee ◽  
Jae-Chul Kim
Keyword(s):  
Signal Processing ◽  
Fault Detection ◽  
Vibration Control ◽  
Vibration Signal ◽  
International Conference ◽  
Gear Fault

Recent Advances in Vibration Signal Processing Techniques for Gear Fault Detection-A Review

2013 ◽  
Vol 430 ◽  
pp. 78-83 ◽  
Author(s):  
Rusmir Bajrić ◽  
Ninoslav Zuber ◽  
Safet Isić
Keyword(s):  
Signal Processing ◽  
Fault Detection ◽  
Vibration Signal ◽  
Analysis Time ◽  
Time Frequency ◽  
Detection Techniques ◽  
Advantages And Disadvantages ◽  
Gear Fault ◽  
Processing Techniques ◽  
Signal Processing Techniques

This paper provides a review of the literature, progress and changes over the years on fault detection of gears using vibration signal processing techniques. Analysis of vibration signals generated by gear in mesh has shown its usefulness in industrial gearbox condition monitoring. Vibration measurement provides a very efficient way of monitoring the dynamic conditions of a machine such as gearbox. Various vibration analysis methods have been proposed and applied to gear fault detection. Most of the traditional signal analysis techniques are based on the stationary assumption. Such techniques can only provide analyses in terms of the statistical average in the time or frequency domain, but can not reveal the local features in both time and frequency domains simultaneously. Frequency/quefrency analysis, time/statistical analysis, time-frequency analysis and cyclostationarity analysis are reviewed in regard for stationary and nonstationary operation. The use of vibration signal processing detection techniques is classified and discussed. The capability of each technique, fundamental principles, advantages and disadvantages and practical application for gear faults detection have been examined by literature review.

Fault Detection of Broken Rotor Bar Using an Improved form of Hilbert–Huang Transform

2018 ◽  
Vol 17 (02) ◽  
pp. 1850012 ◽  
Author(s):  
F. Sabbaghian-Bidgoli ◽  
J. Poshtan
Keyword(s):  
Signal Processing ◽  
Fault Detection ◽  
Hilbert Transform ◽  
Wavelet Packet ◽  
Amplitude Spectrum ◽  
Vibration Signal ◽  
External Noise ◽  
Vibration Signals ◽  
Broken Rotor Bar ◽  
Hilbert Huang Transform

Signal processing is an integral part in signal-based fault diagnosis of rotary machinery. Signal processing converts the raw data into useful features to make the diagnostic operations. These features should be independent from the normal working conditions of the machine and the external noise. The extracted features should be sensitive only to faults in the machine. Therefore, applying more efficient processing techniques in order to achieve more useful features that bring faster and more accurate fault detection procedure has attracted the attention of researchers. This paper attempts to improve Hilbert–Huang transform (HHT) using wavelet packet transform (WPT) as a preprocessor instead of ensemble empirical mode decomposition (EEMD) to decompose the signal into narrow frequency bands and extract instantaneous frequency and compares the efficiency of the proposed method named “wavelet packet-based Hilbert transform (WPHT)” with the HHT in the extraction of broken rotor bar frequency components from vibration signals. These methods are tested on vibration signals of an electro-pump experimental setup. Moreover, this project applies wavelet packet de-noising to remove the noise of vibration signal before applying both methods mentioned and thereby achieves more useful features from vibration signals for the next stages of diagnosis procedure. The comparison of Hilbert transform amplitude spectrum and the values and numbers of detected instantaneous frequencies using HHT and WPHT techniques indicates the superiority of the WPHT technique to detect fault-related frequencies as an improved form of HHT.

Gear fault detection using dynamic transmission variance

2018 ◽  
Vol 24 (1) ◽  
pp. 101-118 ◽  
Author(s):  
Chris K. Mechefske ◽  
David Benjamin Rapos ◽  
Markus Timusk
Keyword(s):  
Signal Processing ◽  
Fault Detection ◽  
Gear Tooth ◽  
Operational Mode ◽  
Content Type ◽  
Input Shaft ◽  
Gear Mesh ◽  
Rotational Position ◽  
Gear Fault ◽  
Plastic Gears

Purpose The purpose of this paper is to report the findings of a study that used measurements of shaft relative rotational position, made using inexpensive Hall Effect sensors and magnets mounted at the ends of a gearbox input and output shafts, to determine gear “transmission variance.” The transmission variance signals, as a function of gear/shaft rotational position, were then used to detect and diagnose gear faults. Design/methodology/approach Two sets of spur gears (one plastic and one steel) were used to experimentally determine the relative shaft rotational position between the input and the output gearbox shafts. Fault-free and faulted (damaged tooth faces and cracked tooth bases) gears were used to collect representative dynamic signals. Signal processing was used to extract transmission variance values as a function of shaft rotational position and then used to detect and diagnose gear faults. Findings The results show that variations in the relative rotational position of the output shaft relative to that of the input shaft (the transmission variance) can be used to reveal gear mesh characteristics, including faults, such as cracked or missing gear teeth and flattened gear tooth faces, in both plastic gears and steel gears under appropriate (realistic) loads and speeds. Research limitations/implications The operational mode of the non-contact rotational position sensors and the dynamic accuracy limitations are explained along with the basic signal processing required to extract transmission variance values. Practical implications The results show that shaft rotational position measurements can be made accurately and precisely using relatively inexpensive sensors and can subsequently reveal gear faults. Social implications The inexpensive and yet trustworthy fault detection methodology developed in this work should help to improve the efficiency of maintenance actions on gearboxes and, therefore, improve the overall industrial efficiency of society. Originality/value The method described has distinct advantages over traditional analysis methods based on gearbox vibration and/or oil analysis.

scholarly journals NEURAL NET USING TO DETERMINE DEPTH AND FREQUENCY OF SIGNALS’ MODULATION FOR ELECTRICAL EQUIPMENT ULTRASONIC VIBROCONTROL

2021 ◽  
pp. 21-30
Author(s):  
Anatoly V. Bychkov ◽  
Irina Yu. Bychkova ◽  
Nadezhda N. Suslova ◽  
Kurbangali K. Alimov
Keyword(s):  
Neural Network ◽  
Signal Processing ◽  
Vibration Control ◽  
Sampling Rate ◽  
Vibration Signal ◽  
Electrical Equipment ◽  
Neural Net ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Mean Square Errors

The apparatus of artificial neural networks (ANN) is proposed to be used for signal processing in active ultrasonic (US) vibration control of electrical equipment. A feature of the applied neural network algorithm is that the required information about vibration parameters is embedded in the ultrasound signal’s phase change at its constant amplitude. Under these conditions, traditional spectral analysis of signals requires a high sampling rate and a significant recording duration. When using the direct propagation’s ANN with three hidden layers, it was shown that it is sufficient to use a sampling frequency of 5-6 points for the period of an ultrasonic wave and a recording duration of 4-5 periods to estimate the nonstationary frequency and amplitude of the vibration signal. Estimates of the error in determining the amplitude, frequency and phase of vibrations are obtained. The root-mean-square errors of the neural network algorithm do not exceed units of percent. The possibilities of using a trained neural network for signal processing in a «sliding window» are demonstrated. The accuracy characteristics of the proposed neural network algorithm of signal processing and the possibility of its optimization for electrical equipment’s vibration control are discussed.

A Gear Chain Fault Detection Method Using an Adaptive Interference Canceling

2007 ◽  
Vol 345-346 ◽  
pp. 1303-1306
Author(s):  
Bum Won Bae ◽  
In Pil Kang ◽  
Yeon Sun Choi
Keyword(s):  
Fault Detection ◽  
Signal Analysis ◽  
Detection Method ◽  
Gear Tooth ◽  
Vibration Signal ◽  
Gear Fault ◽  
Diagnosis Method ◽  
Vibration Signal Analysis ◽  
Processing Techniques ◽  
Signal Processing Techniques

A fault diagnosis method based on wavelet and adaptive interference canceling is presented for the identification of a damaged gear tooth. A damaged tooth of a certain gear chain generates impulsive signals that could be informative to fault detections. Many publications are available not only for the impulsive vibration signal analysis but the application of signal processing techniques to the impulsive signal detections. However, most of the studies about the gear fault detection using the impulsive vibration signals of a driving gear chain are limited to the verification of damage existence on a gear pair. Requirements for more advanced method locating damaged tooth in a driving gear chain should be a motivation of further studies. In this work an adaptive interference canceling combined with wavelet method is used for a successful identification of the damaged tooth location. An application of the wavelet technique provides a superior resolution for the damage detection to the traditional frequency spectrum based methods. An analysis and experiment with three pair gear chain show the feasibility of this study yielding a precise location of the damaged gear tooth.

scholarly journals Gear Fault Detection Based on Teager-Huang Transform

2010 ◽  
Vol 2010 ◽  
pp. 1-9 ◽  
Author(s):  
Hui Li ◽  
Haiqi Zheng ◽  
Liwei Tang
Keyword(s):  
Fault Detection ◽  
Energy Operator ◽  
Vibration Signal ◽  
Intrinsic Mode Functions ◽  
Instantaneous Amplitude ◽  
Hilbert Huang Transform ◽  
Mode Decomposition ◽  
Gear Fault ◽  
Novel Method ◽  
Detection And Diagnosis

Gear fault detection based on Empirical Mode Decomposition (EMD) and Teager Kaiser Energy Operator (TKEO) technique is presented. This novel method is named as Teager-Huang transform (THT). EMD can adaptively decompose the vibration signal into a series of zero mean Intrinsic Mode Functions (IMFs). TKEO can track the instantaneous amplitude and instantaneous frequency of the Intrinsic Mode Functions at any instant. The experimental results provide effective evidence that Teager-Huang transform has better resolution than that of Hilbert-Huang transform. The Teager-Huang transform can effectively diagnose the fault of the gear, thus providing a viable processing tool for gearbox defect detection and diagnosis.

Application of Envelop Analysis and Wavelet Transform for Fault Detection in Local Gearboxes Failure

2008 ◽  
Author(s):  
Dong Sik Gu ◽  
Byeong Keun Choi ◽  
Byeong Su Kim ◽  
Jeong Hwan Lee ◽  
Jong Duk Son ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Fault Detection ◽  
Vibration Signal ◽  
Rolling Element Bearing ◽  
Rolling Element ◽  
Gear Fault ◽  
Ae Signal ◽  
Element Bearing ◽  
General Statistical ◽  
New Feature

Vibration analysis is widely used in machinery diagnosis and the wavelet transform has also been implemented in many applications in the condition monitoring of machinery. In contrast to previous applications, this paper examines whether acoustic signal can be used effectively along vibration signal to detect the various local fault, in local fault of gearboxes using the wavelet transform. Moreover, envelop analysis is well known as useful tool for the detection of rolling element bearing fault. In this paper, acoustic emission (AE) sensor is employed to detect gearbox damage by installing them around bearing housing at driven-end side. Signal processing is conducted by wavelet transform and enveloping to detect the fault all at once gearbox and bearing using AE signal. Result of fault detection is presented using some general statistical features and a proposed new feature (RGF: Ratio of Gear Frequency) for gear fault calculated from AE signal with different condition.

Gearbox fault diagnosis using RMS based probability density function and entropy measures for fluctuating speed conditions

2016 ◽  
Vol 16 (6) ◽  
pp. 682-695 ◽  
Author(s):  
Vikas Sharma ◽  
Anand Parey
Keyword(s):  
Fault Diagnosis ◽  
Probability Density Function ◽  
Fault Detection ◽  
Probability Density ◽  
Density Function ◽  
Approximate Entropy ◽  
Vibration Signal ◽  
Rotating Speed ◽  
Entropy Measures ◽  
Gear Fault

Fault diagnosis of gearbox which operates on low rotating speed with high fluctuations is highly important because its ignorance can led to a catastrophe. The uncertainty within the vibration signal of the gearbox can be identified by the entropy measures, on the basis of probability density function of a signal. But, under fluctuating speeds, entropies may show insignificant results, hence making them non-reliable. The aim of this article is to develop a reliable and stable technique for gear fault detection under such fluctuating speeds. Therefore, a root mean square–based probability density function is proposed to improve the efficiency of entropy measures. The fault detection capabilities of proposed technique were demonstrated experimentally. Various entropy measures, namely, Shannon entropy, Rényi entropy, approximate entropy, and sample entropy, were compared as well as evaluated for both Gaussian and proposed probability density function. The proposed technique was further validated using two condition indicators based on amplitude of probability density function. Results suggest the effective fault diagnosis using proposed method.

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