16 research outputs found
A hygrothermo-mechanical model for wood: part A. Poroelastic formulation and validation with neutron imaging COST Action FP0904 2010-2014: Thermo-hydro-mechanical wood behavior and processing
The correct prediction of the behavior of wood components undergoing environmental loading or industrial process requires that the hygrothermal and mechanical (HTM) behavior of wood is considered in a coupled manner. A fully coupled poromechanical approach is proposed and validated with neutron imaging measurements of moist wood specimens exposed to high temperature. This paper demonstrates that a coupled HTM approach adequately captures the variations of temperature, moisture content, and dimensions that result in a moist wood sample exposed to one-side heating.ISSN:0018-3830ISSN:1437-434
Decision Fusion Method for Bearing Faults Classification Based on Wavelet Denoising and Dempster–Shafer Theory
Feature Extraction, Risky Classifications and Fault Diagnosis on Rolling Bearings of EEG Signals Denoised using Stationary Wavelet Transform of Patient Monitoring and IoT
Intelligent predictive maintenance for fault diagnosis and prognosis in machine centers: Industry 4.0 scenario
Rolling element bearing faults diagnosis based on autocorrelation of optimized: wavelet de-noising technique
Machinery failure diagnosis is an important component of the condition based maintenance (CBM) activities for most engineering systems. Rolling element bearings are the most common cause of rotating machinery failure. The existence of the amplitude modulation and noises in the faulty bearing vibration signal present challenges to effective fault detection method. The wavelet transform has been widely used in signal de-noising, due to its extraordinary time-frequency representation capability. In this paper, a new technique for rolling element bearing fault diagnosis based on the autocorrelation of wavelet de-noised vibration signal is applied. The wavelet base function has been derived from the bearing impulse response. To enhance the fault detection process the wavelet shape parameters (damping factor and center frequency) are optimized based on kurtosis maximization criteria. The results show the effectiveness of the proposed technique in revealing the bearing fault impulses and its periodicity for both simulated and real rolling bearing vibration signals.Khalid F. Al-Raheem, Asok Roy, K. P. Ramachandran, D. K. Harrison, Steven Grainge