Fault diagnosis of rolling bearings using a genetic algorithm optimized neural network

Ünal M., Onat M., Demetgul M., Küçük H.

MEASUREMENT, vol.58, pp.187-196, 2014 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 58
  • Publication Date: 2014
  • Doi Number: 10.1016/j.measurement.2014.08.041
  • Journal Name: MEASUREMENT
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.187-196
  • Keywords: Rolling bearing, Fault diagnostics, Envelope detector, Hilbert Transform, Fast Fourier Transform artificial neural network, Genetic algorithm, Feed-forward algorithm, SUPPORT VECTOR MACHINES, VIBRATION, TRANSFORM
  • Marmara University Affiliated: Yes


In rotary complex machines, collapse of a component may inexplicably occur usually accompanied by a noise or a disturbance emanating from other sources. Rolling bearings constitute a vital part in many rotational machines and the vibration generated by a faulty bearing easily affects the neighboring components. Continuous monitoring, fault diagnosis and predictive maintenance, is a crucial task to reduce the degree of damage and stopping time for a rotating machine. Analysis of fault-related vibration signal is a usual method for accurate diagnosis. Among the resonant demodulation techniques, a well-known resolution often used for fault diagnosis is envelope analysis. But, usually this method may not be adequate enough to indicate satisfactory results. It may require some auxiliary additional techniques. This study suggests some methods to extract features using envelope analysis accompanied by Hilbert Transform and Fast Fourier Transform. The proposed artificial neural network (ANN) based fault estimation algorithm was verified with experimental tests and promising results. Every test was initiated with a reference ANN architecture to avoid inappropriate classification during the evaluation of fitness value. Later, ANN model was modified using a genetic algorithm providing, an optimal skillful fast-reacting network architecture with improved classification results. (C) 2014 Elsevier Ltd. All rights reserved.