WEBINAR: Data pre-processing and denoising for damage detection

Summary

An effective Structural Health Monitoring (SHM) system relies on good quality measurement data. All measured data are susceptible to noise or errors which are introduced at various stages along the signal path. Some errors gradually develop as the system ages, while others are apparent only under certain operating conditions. In this presentation we introduce the most common types of measurement errors and discuss methods for detecting and, where possible, removing them to recover the original measured signal. The presentation begins with an overview describing stages along the signal path and the types of error commonly introduced. We describe the most common configuration errors associated with SHM data acquisition and show how errors can be identified, remedied or avoided through good system design. The term ‘noise’ is used to describe an undesirable component within the signal. The presentation continues by categorizing the types of noise, identifying the cause of noise and presenting methods of denoising measured data. We conclude by describing techniques that are particularly suited to the analysis of rotating machinery.

Andrew Halfpenny

Andrew heads technology and innovation for HBK's nCode product brand. He holds a PhD in Mechanical Engineering and a Masters’ degree in Civil and Structural Engineering. Over the years he has introduced many new technologies to the automotive, aerospace and power generation sectors. These include: customer usage monitoring, target customer analysis, proving ground correlation, accelerated laboratory testing and mathematical simulation. His most recent work has been developing methods to measure and improve the performance, durability and reliability of electric vehicles. Andrew holds a European patent for the ‘Damage Monitoring Tag’, and developed the new vibration methods used for qualifying UK military helicopters. He has worked in consultancy with “blue chip” customers across the UK, Europe, Americas and the Far East, and has written publications on Fatigue, Structural Health Monitoring and Digital Signal Processing. Andrew is a founding member of the NAFEMS PSE (Professional Simulation Engineer) Certification scheme, and sits on the NAFEMS committee for Dynamic Testing. He is also a visiting lecturer on structural dynamics and structural health monitoring with the University of Sheffield.

WEBINAR: Data pre-processing and denoising for damage detection