Evaluation of the use of envelope analysis and DWT on AE signals generated from degrading shafts

Abstract Vibration analysis is widely used in machinery diagnosis. Wavelet transforms and envelope analysis, which have been implemented in many applications in the condition monitoring of machinery, are applied in the development of a condition monitoring system for early detection of faults generated in several key components of machinery. Early fault detection is a very important factor in condition monitoring and a basic component for the application of condition-based maintenance (CBM) and predictive maintenance (PM). In addition, acoustic emission (AE) sensors have specific characteristics that are highly sensitive to high-frequency and low-energy signals. Therefore, the AE technique has been applied recently in studies on the early detection of failure. In this paper, AE signals caused by crack growth on a rotating shaft were captured through an AE sensor. The AE signatures were pre-processed using the proposed signal processing method, after which power spectrums were generated from the FFT results. In the power spectrum, some peaks from fault frequencies were presented. According to the results, crack growth in rotating machinery can be considered and detected using an AE sensor and the signal processing method.

[1]  Charles T. Hatch,et al.  Fundamentals of Rotating Machinery Diagnostics , 2003 .

[2]  Thomas R. Kurfess,et al.  Vibration analysis for Bearing outer race condition diagnostics , 1999 .

[3]  Susumu Sato,et al.  Applications of nematic liquid crystals to electric-field sensors , 1990 .

[4]  M. Elforjani,et al.  Condition Monitoring of Slow‐Speed Shafts and Bearings with Acoustic Emission , 2011 .

[5]  M. Elforjani,et al.  Detecting natural crack initiation and growth in slow speed shafts with the Acoustic Emission technology , 2009 .

[6]  M. Elforjani,et al.  Observations of a naturally degrading slow-speed shaft , 2010 .

[7]  M. Elforjani,et al.  Monitoring the Onset and Propagation of Natural Degradation Process in a Slow Speed Rolling Element Bearing With Acoustic Emission , 2008 .

[8]  DongSik Gu,et al.  Detection of faults in gearboxes using acoustic emission signal , 2011 .

[9]  Athanasios Papoulis,et al.  Probability, Random Variables and Stochastic Processes , 1965 .

[10]  L. D. Hall.  Diagnosis of continuous rotor–stator rubbing in large scale turbine units using acoustic emissions , 2004 .

[11]  M. Elforjani and.  Condition Monitoring of Slow-Speed Shafts and Bearings with Acoustic Emission , 2010 .

[12]  Ichiya Sato.  Rotating machinery diagnosis with acoustic emission techniques , 1990 .

[13]  M. Elforjani,et al.  Accelerated natural fault diagnosis in slow speed bearings with Acoustic Emission , 2010 .

[14]  D Mba,et al.  Condition monitoring of low-speed rotating machinery using stress waves Part 1 , 1999 .