Flow Measurement by Wavelet Packet Analysis of Sound Emissions

Creative Commons License

GÖKSU H.

MEASUREMENT & CONTROL, cilt.51, sa.3-4, ss.104-112, 2018 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 51 Sayı: 3-4
  • Basım Tarihi: 2018
  • Doi Numarası: 10.1177/0020294018768340
  • Dergi Adı: MEASUREMENT & CONTROL
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.104-112
  • Anahtar Kelimeler: Acoustic emissions, flow measurement, fluids, multilayer perceptron, norm entropy, wavelet packet analysis, FEATURE-EXTRACTION, NEURAL-NETWORKS, CLASSIFICATION, TRANSFORM, SIGNAL, RECOGNITION, ENERGY, FAULT, ALGORITHM, DIAGNOSIS
  • Akdeniz Üniversitesi Adresli: Evet

Özet

Fluid, when running through pipes, makes a complex sound emission whose parameters change nonlinearly with respect to flow speed. Especially, in household pipe systems, there may be spraying effects and resonance effects which make the emission more complex. We present a novel approach for predicting flow speed based on wavelet packet analysis of sound emissions rather than traditional time and frequency domain methods. Wavelet packet analysis, by providing arbitrary time-frequency resolution, enables analyzing signals of stationary and non-stationary nature. It has better time representation than Fourier analysis and better high-frequency resolution than wavelet analysis. Wavelet packet analysis subimages are further analyzed to obtain feature vectors of norm entropy. These feature vectors are fed into a multilayer perceptron for prediction. Prediction accuracy of 98.62%, with 3.99E-04 Us mean absolute error and its corresponding 1.85% relative error is achieved. Time sensitivity is +/- 0.453 s and is open to improvement by varying window width. The result indicates that the proposed method is a good candidate for flow measurement by acoustic analysis.