Download Structural Health Monitoring: A Machine Learning Perspective by Charles R. Farrar, Keith Worden(auth.) PDF

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By Charles R. Farrar, Keith Worden(auth.)

Written by means of international leaders and pioneers within the box, this publication is a must have learn for researchers,  training engineers and collage college operating in SHM.

Structural future health tracking: A desktop studying Perspective is the 1st accomplished booklet at the common challenge of structural future health tracking. The authors, popular specialists within the box, think of structural health and wellbeing tracking in a brand new demeanour by means of casting the matter within the context of a computing device learning/statistical trend reputation paradigm, first explaining the paradigm more often than not phrases then explaining the method intimately with additional perception supplied through numerical and experimental reports of laboratory attempt specimens and in-situ buildings. This paradigm offers a accomplished framework for constructing SHM recommendations.

Structural health and wellbeing tracking: A computer studying Perspective makes large use of the authors’ designated surveys of the technical literature, the event they've got won from instructing quite a few classes in this topic, and the result of appearing various analytical and experimental structural wellbeing and fitness tracking reports.

  • Considers structural healthiness tracking in a brand new demeanour by way of casting the matter within the context of a desktop learning/statistical development popularity paradigm
  • Emphasises an built-in method of the improvement of structural health and wellbeing tracking ideas via coupling the size part of the matter at once with the information interrogation algorithms
  • Benefits from huge use of the authors’ special surveys of 800 papers within the technical literature and the event they've got received from educating a variety of brief classes in this subject. 

Content:
Chapter 1 creation (pages 1–16):
Chapter 2 old review (pages 17–43):
Chapter three Operational assessment (pages 45–52):
Chapter four Sensing and information Acquisition (pages 53–85):
Chapter five Case reports (pages 87–117):
Chapter 6 advent to chance and information (pages 119–160):
Chapter 7 Damage?Sensitive beneficial properties (pages 161–243):
Chapter eight beneficial properties in accordance with Deviations from Linear reaction (pages 245–294):
Chapter nine desktop studying and Statistical trend popularity (pages 295–320):
Chapter 10 Unsupervised studying – Novelty Detection (pages 321–360):
Chapter eleven Supervised studying – type and Regression (pages 361–401):
Chapter 12 info Normalisation (pages 403–438):
Chapter thirteen basic Axioms of Structural healthiness tracking (pages 439–460):
Chapter 14 harm diagnosis (pages 461–477):

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Extra resources for Structural Health Monitoring: A Machine Learning Perspective

Example text

These sensors are monitored with standard commercial data acquisition systems. Vibration-based SHM studies for offshore platforms have primarily used ambient inputs from wave motion and platform operations as the excitation source (Martinez and Quijada, 1991). There are a limited number of studies where the researchers have developed data acquisition systems designed specifically for offshore platform applications. As an example, engineers have developed a vibration excitation and data acquisition system that can be mounted on a truss element below the water surface.

Perhaps the most refined forms of SHM performed in the aerospace industry are the HUMS used by the rotorcraft industry. These systems evolved from condition monitoring of rotating machinery and were developed for commercial rotorcraft in response to the significant number of crashes experienced by helicopters servicing North Sea oil platforms. In parallel, these systems were developed for military rotorcraft. 3 shows US Army personnel checking the output of a HUMS deployed on a UH-60M Blackhawk helicopter.

Numerous summaries and reviews of these approaches are available in textbook form, including detailed charts relating various machine failures to specific characteristics of the measured dynamic response (Braun, 1986; Hewlett-Packard, 1991; Wouk, 1991; Crawford, 1992; Mitchell, 1993; Taylor, 1994; Eisenmann and Eisenmann, 1997; Bently and Hatch, 2003; Randall, 2011). As an example, one such chart (Mitchell, 1993) shows that gear misalignment will result in higher harmonics of the gear meshing frequency and that these harmonics will have sidebands associated with the shaft rotation frequencies.

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