Vibration signal analysis for gear fault diagnosis with various crack progression scenarios

Mohammed, Omar D.; Rantatalo, Matti; Aidanpää, Jan-Olov; Kumar, Uday

doi:10.1016/j.ymssp.2013.06.040

Cited by 116 publications

(74 citation statements)

References 29 publications

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“…8), Chen and Shao [36] developed an analytical model for TVMS calculation. Based on Chen's model [36], Mohammed et al [37] discussed the validity of the RMS and kurtosis for diagnosing the crack levels under different crack propagation forms. Assuming the propagation path of the spatial crack as a quasi-parabolic form, Yu et al [38] presented a TVMS calculation model for a gear pair with spatial tooth crack.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Review on dynamics of cracked gear systems

Zeng

Feng

et al. 2015

Engineering Failure Analysis

274

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Section: Methodsmentioning

confidence: 99%

“…[36] (see Fig. 24b), Mohammed et al [37] investigated the influence of tooth root crack propagation on dynamic response of a gear system. Ma et al [55] established a 6 DOF model of a perforated gear system, which is similar to the model in Ref.…”

Section: References Methods Descriptionsmentioning

confidence: 99%

Review on dynamics of cracked gear systems

Zeng

Feng

et al. 2015

Engineering Failure Analysis

274

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“…In other previous studies, see for example [16], various crack propagation scenarios in gears were investigated using vibration signal analysis. In this study, the authors take into consideration such studies by examining three levels of damage to simplify the design to select the most suitable sensors.…”

Section: The Experimental Methodology and Conditionsmentioning

confidence: 99%

Condition monitoring of helical gears using automated selection of features and sensors

2016

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The selection of most sensitive sensors and signal processing methods is essential process for the design of condition monitoring and intelligent fault diagnosis and prognostic systems. Normally, sensory data includes high level of noise and irrelevant or redundant information which makes the selection of the most sensitive sensor and signal processing method a difficult task. This paper introduces a new application of the Automated Sensor and Signal Processing Approach (ASPS), for the design of condition monitoring systems for developing an effective monitoring system for gearbox fault diagnosis. The approach is based on using Taguchi's orthogonal arrays, combined with automated selection of sensory characteristic features, to provide economically effective and optimal selection of sensors and signal processing methods with reduced experimental work. Multi-sensory signals such as acoustic emission, vibration, speed and torque are collected from the gearbox test rig under different health and operating conditions. Time and frequency domain signal processing methods are utilised to assess the suggested approach. The experiments investigate a single stage gearbox system with three level of damage in a helical gear to evaluate the proposed approach. Two different classification models are employed using neural networks to evaluate the methodology. The results have shown that the suggested approach can be applied to the design of condition monitoring systems of gearbox monitoring without the need for implementing pattern recognition tools during the design phase; where the pattern recognition can be implemented as part of decision making for diagnostics. The suggested system has a wide range of applications including industrial machinery as well as wind turbines for renewable energy applications.

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“…Mohammeda et al [22] investigated tree different types of teeth cracks. First, crack is over all teeth and has constant value on every teeth width, second, crack depth is crack depth is distributed according to a parabolic function; third, crack is growing in all directions.…”

Section: Science and Technologymentioning

confidence: 99%