Detection of Gear Failures via Vibration and Acoustic Signals Using Wavelet Transform

Baydar, Naim; Ball, Andrew

doi:10.1006/mssp.2001.1435

Cited by 182 publications

(73 citation statements)

References 7 publications

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“…One of the main advantage of using CWT is its ability to provide high frequency resolution low frequency region and high time resolution in high frequency region which is very useful for gearbox fault diagnosis. The CWT of a signal x(t) is defined as a convolution integral of x(t) with dilated and scaled versions of a mother wavelet function Ψ(t) [5]:…”

Section: The Continuous Wavelet Transformmentioning

confidence: 99%

Comparative Study of Gearbox Fault Diagnosis by Vibration Measurements

Hartono

Halim

Widodo

et al. 2016

MATEC Web of Conferences

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Abstract.Vibration analysis has been demonstrated to be one of the best tools to detect faults in a gearbox by providing abundant information about the operating condition of a gearbox. However, a gearbox generates complex vibration signals, which makes it difficult to diagnose when a fault occurs. There are several fault diagnosis methods that can be utilized to analyze the underlying signals. The time-frequency method has been used and showed some promising results. On the other hand, it also has its drawback when it is applied to a complex mechanical system such as gearboxes. This paper thus attempts to examine the effectiveness of several diagnosis methods to detect faults in a gearbox from vibration measurements. The results show that the cepstrum method can provide a more accurate indication of a faulty gearbox compared to other diagnosis methods.

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Section: The Continuous Wavelet Transformmentioning

confidence: 99%

Comparative Study of Gearbox Fault Diagnosis by Vibration Measurements

Hartono

Halim

Widodo

et al. 2016

MATEC Web of Conferences

View full text Add to dashboard Cite

show abstract

“…Similarly, the acoustic signal is easily contaminated in a normal industrial environment due to the fact that airborne sound from machine is noisy and complex. That is a reason why the acoustic signal has been received slight attention for machinery condition monitoring and fault diagnosis [8]. It could state that alternative signals being more accurate are necessary.…”

Section: Introductionmentioning

confidence: 99%

An Approach to Fault Diagnosis of Rotating Machinery Using the Second-Order Statistical Features of Thermal Images and Simplified Fuzzy ARTMAP

Thobiani¹,

Tran²,

Tinga³

2017

ENG

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Thermal image, or thermogram, becomes a new type of signal for machine condition monitoring and fault diagnosis due to the capability to display real-time temperature distribution and possibility to indicate the machine's operating condition through its temperature. In this paper, an investigation of using the second-order statistical features of thermogram in association with minimum redundancy maximum relevance (mRMR) feature selection and simplified fuzzy ARTMAP (SFAM) classification is conducted for rotating machinery fault diagnosis. The thermograms of different machine conditions are firstly preprocessed for improving the image contrast, removing noise, and cropping to obtain the regions of interest (ROIs). Then, an enhanced algorithm based on bi-dimensional empirical mode decomposition is implemented to further increase the quality of ROIs before the second-order statistical features are extracted from their gray-level co-occurrence matrix (GLCM). The highly relevant features to the machine condition are selected from the total feature set by mRMR and are fed into SFAM to accomplish the fault diagnosis. In order to verify this investigation, the thermograms acquired from different conditions of a fault simulator including normal, misalignment, faulty bearing, and mass unbalance are used. This investigation also provides a comparative study of SFAM and other traditional methods such as back-propagation and probabilistic neural networks. The results show that the second-order statistical features used in this framework can provide a plausible accuracy in fault diagnosis of rotating machinery.

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“…The early detection and isolation are very important to avoid costly breakdowns and improve equipment reliability as reported in [1] and [2]. In the past decades, various analytical, semi-analytical and numerical methods with different accuracies have been attracted much attention from research teams to investigate the rail vibrations then the diagnosis of this system and isolation of the defect [23], [24], [25] and [26]. The finite elements method [29] is a very effective method for the railway model calculates.…”

Section: Introductionmentioning

confidence: 99%

Model Study and Fault Detection for the Railway System

Rawia¹,

Tlijani²,

Knani³

2017

ijacsa

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Abstract-The wheel-rail-sleepers system is simulated as a series of moving point loads on an Euler-Bernoulli beam resting on a visco-elastic half space. This paper concentrates on the railsleepers interaction system (railway system) and the fault detection. The main objective is to mathematically develop and implement a dynamic model of a railway system then the diagnosis of system defects using a Luenberger observer (LO). The simulation results are based on a physical description, mathematical equations and simulations with MATLAB simulation program.

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Detection of Gear Failures via Vibration and Acoustic Signals Using Wavelet Transform

Cited by 182 publications

References 7 publications

Comparative Study of Gearbox Fault Diagnosis by Vibration Measurements

Comparative Study of Gearbox Fault Diagnosis by Vibration Measurements

An Approach to Fault Diagnosis of Rotating Machinery Using the Second-Order Statistical Features of Thermal Images and Simplified Fuzzy ARTMAP

Model Study and Fault Detection for the Railway System

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