Automated extraction of damage features through genetic programming

Harvey, Dustin; Todd, Michael D.

doi:10.1117/12.2009739

Cited by 5 publications

(3 citation statements)

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“…Based on the analysis presented in section II, the presented review of related literature, and the authors' experiences with various approaches [26], [27], this study proposes a novel method called Autofead for automated feature design. The overall goal of this work is to develop a system that can infer from numeric sequence inputs an optimal, minimum-basis feature set for a given decision making process.…”

Section: Autofead Design Processmentioning

confidence: 99%

Automated Feature Design for Numeric Sequence Classification by Genetic Programming

Harvey

Todd

2015

IEEE Trans. Evol. Computat.

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Abstract-Pattern recognition methods rely on maximuminformation, minimum-dimension feature sets to reliably perform classification and regression tasks. Many methods exist to reduce feature set dimensionality and construct improved features from an initial set; however, there are few general approaches for design of features from numeric sequences. Any information lost in pre-processing or feature measurement cannot be recreated during pattern recognition. General approaches are needed to extend pattern recognition to include feature design and selection for numeric sequences, such as time series, within the learning process itself. This study proposes a novel genetic programming approach to automated feature design called Autofead. In this method, a genetic programming variant evolves a population of candidate features built from a library of sequence-handling functions. Numerical optimization methods, included through a hybrid approach, ensure that the fitness of candidate algorithms is measured using optimal parameter values. Autofead represents the first automated feature design system for numeric sequences to leverage the power and efficiency of both numerical optimization and standard pattern recognition algorithms. Potential applications include the monitoring of electrocardiogram signals for indications of heart failure, network traffic analysis for intrusion detection systems, vibration measurement for bearing condition determination in rotating machinery, and credit card activity for fraud detection.

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Section: Autofead Design Processmentioning

confidence: 99%

Automated Feature Design for Numeric Sequence Classification by Genetic Programming

Harvey

Todd

2015

IEEE Trans. Evol. Computat.

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“…However, the distance-based approach fails when transformation of the input space is required to identify features relevant for a given task. The authors previously proposed a genetic programming based system, Autofead, for data-based, automated feature extraction algorithm development 4,5 . Autofead provides a feature-based time series classification solution with no required knowledge of the data-generating system.…”

Section: Structural Health Monitoring and Time Series Classificationmentioning

confidence: 99%

Robust evaluation of time series classification algorithms for structural health monitoring

2014

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Structural health monitoring (SHM) systems provide real-time damage and performance information for civil, aerospace, and mechanical infrastructure through analysis of structural response measurements. The supervised learning methodology for data-driven SHM involves computation of low-dimensional, damage-sensitive features from raw measurement data that are then used in conjunction with machine learning algorithms to detect, classify, and quantify damage states. However, these systems often suffer from performance degradation in real-world applications due to varying operational and environmental conditions. Probabilistic approaches to robust SHM system design suffer from incomplete knowledge of all conditions a system will experience over its lifetime. Info-gap decision theory enables nonprobabilistic evaluation of the robustness of competing models and systems in a variety of decision making applications. Previous work employed info-gap models to handle feature uncertainty when selecting various components of a supervised learning system, namely features from a pre-selected family and classifiers. In this work, the info-gap framework is extended to robust feature design and classifier selection for general time series classification through an efficient, interval arithmetic implementation of an info-gap data model. Experimental results are presented for a damage type classification problem on a ball bearing in a rotating machine. The info-gap framework in conjunction with an evolutionary feature design system allows for fully automated design of a time series classifier to meet performance requirements under maximum allowable uncertainty.

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“…Thus, the fundamental assumption in this approach is that data are available that are known to span the desired classification or regression spaces. The adopted approach to automated feature extraction algorithm design, called Autofead, uses a custom genetic programming variant to search for the optimal processing path from measured responses to features relevant to the SHM task [5]. Genetic programming is an evolutionary, heuristic search method in the program space which has been successfully adapted to a wide range of data mining and engineering design problems [6,7].…”

Section: Introductionmentioning

confidence: 99%

Structural health monitoring feature design by genetic programming

Harvey¹,

Todd²

2014

Smart Mater. Struct.

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Structural health monitoring (SHM) systems provide real-time damage and performance information for civil, aerospace, and other high-capital or life-safety critical structures. Conventional data processing involves pre-processing and extraction of low-dimensional features from in-situ time series measurements. The features are then input to a statistical pattern recognition algorithm to perform the relevant classification or regression task necessary to facilitate decisions by the SHM system. Traditional design of signal processing and feature extraction algorithms can be an expensive and time-consuming process requiring extensive system knowledge and domain expertise.Genetic programming, a heuristic program search method from evolutionary computation, was recently adapted by the authors to perform automated, data-driven design of signal processing and feature extraction algorithms for statistical pattern recognition applications. The proposed method, called Autofead, is particularly suitable to handle the challenges inherent in algorithm design for structural health monitoring problems where the manifestation of damage in structural response measurements is often unclear or unknown. Autofead mines a training database of response measurements to discover information-rich features specific to the problem at hand. This study provides experimental validation on three structural health monitoring applications including ultrasonic damage detection, bearing damage classification for rotating machinery, and vibration-based structural health monitoring. Performance comparisons with common feature choices for each problem area are provided demonstrating the versatility of Autofead to produce significant algorithm improvements on a wide range of problems.

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Automated extraction of damage features through genetic programming

Cited by 5 publications

References 0 publications

Automated Feature Design for Numeric Sequence Classification by Genetic Programming

Automated Feature Design for Numeric Sequence Classification by Genetic Programming

Robust evaluation of time series classification algorithms for structural health monitoring

Structural health monitoring feature design by genetic programming

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