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Advanced Signal/Data Processing for Structural Health Monitoring
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Advanced Signal/Data Processing for Structural Health Monitoring

A special issue of Signals (ISSN 2624-6120).

Deadline for manuscript submissions: closed (31 July 2021) | Viewed by 10101

Special Issue Editor

Dr. Phong B. Dao

E-Mail Website
Guest Editor
Department of Robotics and Mechatronics, Faculty of Mechanical Engineering and Robotics, AGH University of Krakow, Al. Mickiewicza 30, 30-059 Krakow, Poland
Interests: structural health monitoring; non-destructive testing; condition monitoring; fault diagnosis; advanced signal/data processing; intelligent control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear colleagues,

Structural health monitoring (SHM) has gained significant importance for aerospace, civil, and mechanical engineering infrastructures as well as energy supply systems and numerous other industrial installations. Structural damage detection is a key element in SHM systems and the practical implementation of damage detection strategies to real-world structures outside of laboratory conditions is one of the most challenging tasks for engineering community.

Recently, the majority of studies in SHM have been focused on developing cost-effective, automatic, and reliable damage detection technologies for SHM applications. It is generally agreed that signal/data processing will play an important role in the implementation of these technologies. Moreover, processing and interpreting the massive amount of data (big data) generated through long-term monitoring of huge and complex civil infrastructure (e.g., bridges, wind turbines, etc.) is an emerging challenge that needs to be urgently addressed by the SHM community.

Therefore, this Special Issue is dedicated to recent research and advances in SHM data interpretation, damage detection techniques, machine learning algorithms, and algorithms developed to process and interpret large amounts of SHM data, with a focus is on newly developed signal processing techniques and their applications to various engineering systems.

Prospective authors are invited to submit high-quality original contributions and reviews for this Special Issue. Suitable topics include, but are not limited to:

- Pattern recognition and machine learning approaches for SHM

- Damage detection algorithms

- Advanced signal processing methods for SHM

- Time series analysis for SHM

- Parametric and non-parametric methods 

Dr. Phong B. Dao
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Signals is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • structural health monitoring (SHM)
  • non-destructive testing (NDT)
  • advanced signal processing for SHM and NDT
  • smart materials and structures
  • damage detection
  • condition monitoring
  • modeling and simulation

Published Papers (4 papers)

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Research

26 pages, 7713 KiB  
Article
Bearing Prognostics: An Instance-Based Learning Approach with Feature Engineering, Data Augmentation, and Similarity Evaluation
by Jun Sun and Qiao Sun
Signals 2021, 2(4), 662-687; https://doi.org/10.3390/signals2040040 - 10 Oct 2021
Viewed by 2488
Abstract
We propose an instance-based learning approach with data augmentation and similarity evaluation to estimate the remaining useful life (RUL) of a mechanical component for health management. The publicly available PRONOSTIA datasets, which provide accelerated degradation test data for bearings, are used in our [...] Read more.
We propose an instance-based learning approach with data augmentation and similarity evaluation to estimate the remaining useful life (RUL) of a mechanical component for health management. The publicly available PRONOSTIA datasets, which provide accelerated degradation test data for bearings, are used in our study. The challenges with the datasets include a very limited number of run-to-failure examples, no failure mode information, and a wide range of bearing life spans. Without a large number of training samples, feature engineering is necessary. Principal component analysis is applied to the spectrogram of vibration signals to obtain prognostic feature sequences. A data augmentation strategy is developed to generate synthetic prognostic feature sequences using learning instances. Subsequently, similarities between the test and learning instances can be assessed using a root mean squared (RMS) difference measure. Finally, an ensemble method is developed to aggregate the RUL estimates based on multiple similar prognostic feature sequences. The proposed approach demonstrates comparable performance with published solutions in the literature. It serves as an alternative method for solving the RUL estimation problem. Full article
(This article belongs to the Special Issue Advanced Signal/Data Processing for Structural Health Monitoring)
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15 pages, 1703 KiB  
Article
Adaptive Probabilistic Optimization Approach for Vibration-Based Structural Health Monitoring
by Hussain Altammar and Sudhir Kaul
Signals 2021, 2(3), 475-489; https://doi.org/10.3390/signals2030029 - 17 Jul 2021
Viewed by 1988
Abstract
This paper presents a novel adaptive probabilistic algorithm to identify damage characteristics by integrating the use of the frequency response function with an optimization approach. The proposed algorithm evaluates the probability of damage existence and determines salient details such as damage location and [...] Read more.
This paper presents a novel adaptive probabilistic algorithm to identify damage characteristics by integrating the use of the frequency response function with an optimization approach. The proposed algorithm evaluates the probability of damage existence and determines salient details such as damage location and damage severity in a probabilistic manner. A multistage sequence is used to determine the probability of damage parameters including crack depth and crack location while minimizing uncertainties. A simply supported beam with an open edge crack was used to demonstrate the application of the algorithm for damage detection. The robustness of the algorithm was tested by incorporating varying levels of noise into the frequency response. All simulation results show successful detection of damage with a relatively high probability even in the presence of noise. Results indicate that the probabilistic algorithm could have significant advantages over conventional deterministic methods since it has the ability to avoid yielding false negatives that are quite common among deterministic damage detection techniques. Full article
(This article belongs to the Special Issue Advanced Signal/Data Processing for Structural Health Monitoring)
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21 pages, 4415 KiB  
Article
A Comparison between Ultrasonic Guided Wave Leakage and Half-Cell Potential Methods in Detection of Corrosion in Reinforced Concrete Decks
by Ahmad Shoaib Amiri, Ece Erdogmus and Dana Richter-Egger
Signals 2021, 2(3), 413-433; https://doi.org/10.3390/signals2030026 - 30 Jun 2021
Cited by 7 | Viewed by 1970
Abstract
This article presents the advantages and limitations of a recently developed Ultrasonic Guided Wave Leakage (UGWL) method in comparison to the well-known Half-Cell Potential (HCP) method in their ability to detect corrosion in reinforced concrete (RC) bridge decks. This research also establishes a [...] Read more.
This article presents the advantages and limitations of a recently developed Ultrasonic Guided Wave Leakage (UGWL) method in comparison to the well-known Half-Cell Potential (HCP) method in their ability to detect corrosion in reinforced concrete (RC) bridge decks. This research also establishes a correlation between UGWL data and chloride content in concrete RC slabs. Concrete slabs submerged in a 10% NaCl solution were monitored using both methods over a period of six months. The chloride content from the three cores (0.84, 0.55, and 0.18%) extracted from the slab after the 6-month long process all exceeded the chloride threshold values suggested in ACI 318, which is 0.05 to 0.1% by weight of concrete. Further, the UGWL method detected changes due to corrosion approximately 21 days earlier than the HCP method. Full article
(This article belongs to the Special Issue Advanced Signal/Data Processing for Structural Health Monitoring)
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20 pages, 2016 KiB  
Article
A Numerical Study on Computational Time Reversal for Structural Health Monitoring
by Christos G. Panagiotopoulos and Georgios E. Stavroulakis
Signals 2021, 2(2), 225-244; https://doi.org/10.3390/signals2020017 - 22 Apr 2021
Cited by 2 | Viewed by 2512
Abstract
Structural health monitoring problems are studied numerically with the time reversal method (TR). The dynamic output of the structure is applied, time reversed, as an external loading and its propagation within the deformable medium is followed backwards in time. Unknown loading sources or [...] Read more.
Structural health monitoring problems are studied numerically with the time reversal method (TR). The dynamic output of the structure is applied, time reversed, as an external loading and its propagation within the deformable medium is followed backwards in time. Unknown loading sources or damages can be discovered by means of this method, focused by the reversed signal. The method is theoretically justified by the time-reversibility of the wave equation. Damage identification problems relevant to structural health monitoring for truss and frame structures are studied here. Beam structures are used for the demonstration of the concept, by means of numerical experiments. The influence of the signal-to-noise ratio (SNR) on the results was investigated, since this quantity influences the applicability of the method in real-life cases. The method is promising, in view of the increasing availability of distributed intelligent sensors and actuators. Full article
(This article belongs to the Special Issue Advanced Signal/Data Processing for Structural Health Monitoring)
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