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.

Fault diagnosis of internal combustion engine gearbox using vibration signals based on signal processing techniques

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ravikumar KN ◽  
Hemantha Kumar ◽  
Kumar GN ◽  
Gangadharan KV
Keyword(s):  
Signal Processing ◽  
Fault Diagnosis ◽  
Classification Accuracy ◽  
Internal Combustion ◽  
Frequency Characteristics ◽  
Ic Engine ◽  
Vibration Signals ◽  
Content Type ◽  
Time Frequency ◽  
Gear Fault

PurposeThe purpose of this paper is to study the fault diagnosis of internal combustion (IC) engine gearbox using vibration signals with signal processing and machine learning (ML) techniques.Design/methodology/approachVibration signals from the gearbox are acquired for healthy and induced faulty conditions of the gear. In this study, 50% tooth fault and 100% tooth fault are chosen as gear faults in the driver gear. The acquired signals are processed and analyzed using signal processing and ML techniques.FindingsThe obtained results show that variation in the amplitude of the crankshaft rotational frequency (CRF) and gear mesh frequency (GMF) for different conditions of the gearbox with various load conditions. ML techniques were also employed in developing the fault diagnosis system using statistical features. J48 decision tree provides better classification accuracy about 85.1852% in identifying gearbox conditions.Practical implicationsThe proposed approach can be used effectively for fault diagnosis of IC engine gearbox. Spectrum and continuous wavelet transform (CWT) provide better information about gear fault conditions using time–frequency characteristics.Originality/valueIn this paper, experiments are conducted on real-time running condition of IC engine gearbox while considering combustion. Eddy current dynamometer is attached to output shaft of the engine for applying load. Spectrum, cepstrum, short-time Fourier transform (STFT) and wavelet analysis are performed. Spectrum, cepstrum and CWT provide better information about gear fault conditions using time–frequency characteristics. ML techniques were used in analyzing classification accuracy of the experimental data to detect the gearbox conditions using various classifiers. Hence, these techniques can be used for detection of faults in the IC engine gearbox and other reciprocating/rotating machineries.

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.

Performance Assessment of Gear Condition Indicators in Detecting Progressive Gear Tooth Crack

2017 ◽  
Author(s):  
Yang Luo ◽  
Natalie Baddour ◽  
Ming Liang
Keyword(s):  
Fault Detection ◽  
Performance Assessment ◽  
Early Stage ◽  
Gear Tooth ◽  
Relative Effectiveness ◽  
Constant Load ◽  
Fault Detection And Diagnosis ◽  
Dynamic Simulations ◽  
Condition Indicators ◽  
Gear Fault

Gear condition indicators are one of the most important gear transmission fault detection and diagnosis techniques. Various kinds of condition indicators for different kinds of gear fault types (e.g. tooth crack, wear, eccentricity, etc.) have been proposed in the past several decades. However, their relative effectiveness, especially in light of some newly proposed indicators, on gear fault detection or diagnosis has not been fully evaluated. Performance assessment of gear fault condition indicators is not only helpful in designing new advanced indicators but also important for the development of a reliable Condition Based Maintenance (CBM) system. The objective of this paper is to verify and compare the relative performances of twenty-one selected gear condition indicators as applied to a progressive gear tooth crack under constant load and speed working conditions. The main goals are to identify which indicators are sensitive to the fault or have the capability to detect the initial tooth crack and therefore to recommend the most effective gear condition indicators. Dynamic simulations were used to generate the vibration signals which reflect the real underlying vibration behavior of the transmission system. Based on the simulated results, the performances of the selected indicators under noise-free as well as various signal to noise ratio conditions were evaluated and compared. Results indicate that many of the selected indicators are effective for the detection of the progressive tooth crack only under noise-free conditions, and the indicators that only consider time or frequency domain features, such as RMS, Kurtosis, energy ratio, sideband index, are generally less able to detect a tooth crack at an early stage compared to the methods based on reconstructed signals, such as the NA4, FM4, M6A, M8A.

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.

scholarly journals A New Acoustic Emission Sensor Based Gear Fault Detection Approach

2020 ◽  
Vol 4 (3) ◽  
Author(s):  
Yongzhi Qu ◽  
Eric Bechhoefer ◽  
David He ◽  
Junda Zhu
Keyword(s):  
Acoustic Emission ◽  
Fault Detection ◽  
Wind Turbine ◽  
Gear Tooth ◽  
Sampling Rate ◽  
Signal Frequency ◽  
Maintenance Cost ◽  
Detection Approach ◽  
Gear Fault ◽  
Ae Signal

In order to reduce wind energy costs, prognostics and health management (PHM) of wind turbine is needed to reduce operations and maintenance cost of wind turbines. The major cost on wind turbine repairs is due to gearbox failure. Therefore, developing effective gearbox fault detection tools is important in the PHM of wind turbine. PHM system allows less costly maintenance because it can inform operators of needed repairs before a fault causes collateral damage happens to the gearbox. In this paper, a new acoustic emission (AE) sensor based gear fault detection approach is presented. This approach combines a heterodyne based frequency reduction technique with time synchronous average (TSA) and spectral kurtosis (SK) toprocess AE sensor signals and extract features as condition indictors for gear fault detection. Heterodyne techniques commonly used in communication are used to preprocess the AE signals before sampling. By heterodyning, the AE signal frequency is down shifted from MHz to below 50 kHz. This reduced AE signal sampling rate is comparable to that of vibration signals. The presented approach is validated using seeded gear tooth crack fault tests on a notational split torque gearbox. The approach presented in this paper is physics based and the validation results have showed that it could effectively detect the gear faults.

Sign in / Sign up

Export Citation Format

Share Document