The role of surface irregularities in the production of noise in rolling and sliding contacts was investigated. Noise spectra of rolling and sliding contacts were recorded in a small free-field chamber which was specially designed and the results obtained are discussed. © 1982.

T. Ananthapadmanaban

Spectrum Analysis

Surfaces

SURFACE IRREGULARITIES

Bearings

IIT Madras is a public technical and research university located in Chennai, Tamil Nadu. Founded in 1959, it is recognised as an Institute of National Importance.

IIT Madras has been ranked as the top engineering institute in India for four years in a row by the National Institutional Ranking Framework of the MHRD

It currently offers undergraduate, postgraduate and research degrees across 16 disciplines in Engineering, Sciences, Humanities and Management. About 596 faculty belonging to science and engineering departments and centres of the Institute are engaged in teaching, research and industrial consultancy.

IIT Madras

An investigation of the role of surface irregularities in the noise spectrum of rolling and sliding contacts

Wear

International Journal of Decision Support Systems

A comparative study of naive Bayes classifier and Bayes net classifier for fault diagnosis of roller bearing using sound signal

Bearing is one of vital transmission elements, finding numerous applications in small, medium and large machineries. Sound and vibration signals of a rotating machine contain the dynamic information about its operating condition. There are many articles in the literature reporting suitability of vibration signals for fault diagnosis applications; however, the transducer used for vibration signals (accelerometers) is costly. This prevents small scale industries and low cost equipments from using diagnostic tools on affordability ground. On the other hand, transducers used for acquiring sound signals are relatively low cost or/and affordable. Hence, there is a need for studying the use of sound signal for fault diagnosis applications. This paper uses acoustic signals (sound) acquired from near field area of bearings in good and simulated faulty conditions for the purpose of fault diagnosis through machine learning approach. The descriptive statistical features were extracted from sound signals and the important ones were selected using decision tree (dimensionality reduction). The selected features were then used for classification using C4.5 decision tree algorithm. The paper also discusses the effect of features, effect of various classifier parameters on classification accuracy. © 2012 Elsevier Ltd. All rights reserved.

Journal	Wear
ISSN	00431648
Open Access	No