Probabilistic acousto-ultrasonic active-sensing structural health monitoring based on gaussian process and stochastic time series models

Amer, Ahmad
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Gandhi, Farhan
Koratkar, Nikhil
Julius, Anak Agung
Kopsaftopoulos, Fotis
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Aeronautical engineering
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Attribution-NonCommercial-NoDerivs 3.0 United States
This electronic version is a licensed copy owned by Rensselaer Polytechnic Institute (RPI), Troy, NY. Copyright of original work retained by author.
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In the context of engineering structures, structural safety, maintenance and life-cycle management processes are a major factor in sustainability. In particular, the aerospace industry is one that depends heavily on schedule-based procedures in order to sustain proper life-cycle management, ensure safety, and improve performance. Most of such procedures include some type of Non-destructive Evaluation (NDE) techniques, in which aircraft need to be inspected on a regular basis on the ground before operations can be resumed regardless of structural state. This framework, although very effective in the sustainability efforts of the aerospace industry, suffers from a number of drawbacks; the most economically-prominent of which are cost, increased downtime, less-than-optimal safety management paradigm (damage can occur and grow between scheduled procedures) and the limited applicability of fully-autonomous operations. As such, research endeavors in the past 40 years have been directed towards developing sustainability efforts that can be applied online (limiting downtime and increasing safety) and in an automated fashion (limiting the need for costly man hours, and also allowing for autonomous operation). Aside from the implementation of such frameworks in the industry of rotating machinery, the collection of these online frameworks falls under the field of Structural Health Monitoring (SHM). Because of the complexity of aircraft operations, manifested in multiple operational cycles, and, within each cycle, the varying operational and environmental conditions, the aerospace industry poses as a very rich arena for development of SHM techniques \cite{Dong-Kim18}. When it comes to active-sensing guided-wave SHM in particular, where piezoelectric sensors communicate with each other, owing to the fact that most of the currently-employed approaches are of a deterministic nature, i.e. they do not account for operational, environmental and modelling uncertainties, the complexity of aerospace SHM creates a number of challenges in the face of researchers in the active-sensing SHM field today. Namely, emerging SHM technologies need to be accurate and robust in the face of stochastic time-varying and non-linear structural responses, as well as incipient damage types and complex failure modes that can be easily masked by the effects of varying operational and environmental conditions. In addition, with the advancement in on-board data acquisition technologies, SHM frameworks need to be data-intelligent i.e. they need to be capable of using data efficiently. Thus, there lies a need for the development of active-sensing SHM frameworks, where proper understanding, modeling, and analysis of stochastic structural responses under varying states and damage characteristics is achieved for clearing the road towards achieving the aforementioned ultimate goal of SHM systems. This is where \textit{probabilistic SHM} comes in. This thesis attempts to pave the way towards fully-probabilistic frameworks for active-sensing, guided-wave SHM. With focus on damage detection and quantification, statistical and probabilistic techniques are put forward that not only properly model uncertainties in the data coming from the system being interrogated, but also surpass currently-used methods in accuracy. In addition, this thesis addresses the issue of data-intelligence of probabilistic models through a number of approaches. The first problem tackled in this thesis is statistical damage detection, where statistics based on non-parametric time series representations are proposed and applied to test cases to compare their detection performance with standard state-of-the-art damage indicators. Then, once damage is detected, the problem of data-intelligence is addressed through proposing a statistical signal path selection algorithm, again based on non-parametric time series models, which classifies signal paths into damage-intersecting and non-intersecting, where only the former is used in training damage quantification models. After that, probabilistic damage quantification is addressed next through proposing three frameworks based on the probabilistic machine learning techniques within the family of Gaussian Process (GP) models. The first probabilistic damage quantification framework uses industry-standard damage indicators to build the GP models. The second GP framework uses one of the damage detection statistics mentioned above. The third quantification framework uses GP models that are trained using time-varying parametric time series representations. Other work don in this thesis include the integration of physics-based load-compensation models with GP models for situations where critical data is missing in the training space. Also multi-output GP models are proposed to leverage information from across a sensor network for better damage quantification. All in all, the methods presented in this thesis are intended to bring the active-sensing, guided-wave SHM community one step closer to a full probabilistic treatment of SHM problems; the research herein should be considered as a stepping stone towards that goal. This being said, from the studies conducted in this thesis, a plethora of open research questions that need to be answered emerge, with some of them mentioned at the end of the thesis.
August 2021
School of Engineering
Dept. of Mechanical, Aerospace, and Nuclear Engineering
Rensselaer Polytechnic Institute, Troy, NY
Rensselaer Theses and Dissertations Online Collection
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