Damage detection of structural elements based on active sensing and machine learning approaches

Authors

  • Christabel C Ngetich Pan African University Institute for Basic Sciences, Technology and Innovation, JKUAT, 62000, Nairobi, Kenya.
  • James K Kimotho Department of Mechanical Engineering, JKUAT P.O. Box 62000-00100 Nairobi.
  • John M Kihiu Department of Mechanical Engineering, JKUAT P.O. Box 62000-00100 Nairobi.

Keywords:

SHM, Machine Learning algorithms, PZT, Ultrasonic guided wave, lamb wave, linear sine wave sweep

Abstract

Monitoring the health of structural elements is very crucial in civil, mechanical and aerospace industries where
accrual of small defects such as cracks may result in catastrophic failure. Nondestructive evaluation (NDE) techniques such
as visual inspection, X-ray, ultrasound, eddy currents evaluation, magnetic particle testing, etc., are mostly used for damage
detection and monitoring of structural components. Although these techniques are efficient in damage detection, they are time
consuming, difficult, and expensive to apply regularly on structures. Structural health monitoring (SHM) techniques for damage
detection by employing signals acquired directly from piezoelectric transducers attached on the structure have been developed
to overcome this challenge. The main goal of SHM is geared towards the development of efficient approaches for processing
and analyzing this data to obtain relevant information on the condition of the structure therefore providing real time monitoring.
The main challenge of most SHM applications lies in establishing the right kind of signal that can be employed for effective
detection of damage. As a contribution, this paper presents an approach for damage detection and damage classification in
structural elements. This work incorporates signal processing, feature extraction, damage detection and application of machine
learning algorithms for damage classification. A structural health monitoring test rig is set up consisting of a stainless steel
cantilever bar instrumented with two Lead Zirconate Titanate (PZT) transducers for generation and detection of the ultrasonic
guided waves for damage detection. Lamb waves and linear sine wave sweep are evaluated on their effectiveness in damage
detection. The obtained sensor data is preprocessed and the extracted features used to train four machine learning algorithms
with the obtained lamb wave sensor data achieving an average classification accuracy of 92.2% while the linear sine wave
sweep sensor data achieved an average classification accuracy of 81.3%.

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Published

2020-09-16

How to Cite

Ngetich, C. C., Kimotho, . J. K., & Kihiu, . J. M. (2020). Damage detection of structural elements based on active sensing and machine learning approaches. JOURNAL OF SUSTAINABLE RESEARCH IN ENGINEERING, 5(2), 62–77. Retrieved from https://jsre.jkuat.ac.ke/index.php/jsre/article/view/87

Issue

Vol. 5 No. 2 (2019): JOURNAL OF SUSTAINABLE RESEARCH IN ENGINEERING

Section

Articles

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