Application of Neural Networks for Detection of Changes in Nonlinear Systems

Masri, Sami F.; Smyth, Andrew W.; Chassiakos, Anastasios; Caughey, T. K.; Hunter, Norman F.

doi:10.1061/(asce)0733-9399(2000)126:7(666)

Cited by 186 publications

(79 citation statements)

References 10 publications

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“…This study provides some important clues regarding the fault tolerance capacity and generalization capabilities of a neural network in the context of identification of a nonlinear single degree-of-freedom mechanical system. ANN based schemes have also been proposed for detecting damage in structures that exhibit a nonlinear behavior (232) .…”

Section: Journal Of System Design and Dynamicsmentioning

confidence: 99%

A Review of Nonlinear Dynamics of Mechanical Systems in Year 2008

Shaw

Balachandran

2008

JSDD

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In this article, we provide an overview of a selection of topics of current interest in nonlinear dynamics and vibrations of mechanical systems. Specifically, we cover the traditional topics of structural dynamics, rotating systems, vibration control, vehicle dynamics, and machining, as well as some topics that have emerged more recently, namely micro-and nano-electromechanical systems, piecewise smooth systems, and structural health monitoring and system identification. In the introductory and closing sections, we offer our perspective on the state of affairs in nonlinear dynamics and its utility in the study of mechanical systems.

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Section: Journal Of System Design and Dynamicsmentioning

confidence: 99%

A Review of Nonlinear Dynamics of Mechanical Systems in Year 2008

Shaw

Balachandran

2008

JSDD

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“…The head loses in each loop are obtained from equation (6): (6) in which i and j are node numbers at the ends of pipes.…”

Section: Direct Analysismentioning

confidence: 99%

“…The ANN can be useful for complicated engineering problems. The ANN consists primarily of three basic elements: neurons, the architecture of the network and a learning rule [3,4,6].…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Earthquake damage detection in Tehran’s water distribution system

Gholikandi¹,

Ardakani²,

Pourjafari

et al. 2009

Safety and Security Engineering III

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Iran is located in the one of world's large seismicity zones called ALPA (AlpHimalaya) and the mega city Tehran has is in a particularly dangerous situation. In a destructive earthquake, considerable damage occurs in buried structures such as the urban water supply system. In this study, the following objects are investigated: review of the damage analysis and seismic design of the water networks, review of the seismic information for water distribution systems, earthquake damage detection in water distribution systems, using an artificial neural network (ANN) and a general back error propagating perception to detect pipelines damaged due to earthquakes. The failure points are obtained by using the amount of water discharged from the tanks. The states probable failures have to be computed, the mathematical model for different states of pipe failure by which the damaged pipes could be detected through a back analysis. Since there are various types of failure, therefore, the ANN has been applied. Through a parametric study, different geometry, shape diameters and pressure of the water network are surveyed and the best network architecture for each case is obtained. The peak responses and phase delays are assumed to be network outputs. The network is trained in a supervised manner. The study shows the efficiency and capability of the ANN for modelling the observed nonlinear behaviour.

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“…With particular reference to SHM applications, Ampeliotis et al (2012) proposed a spatio-temporal protocol, based on the adaptive filtering technique, which tracks the time-varying prediction model at each sensor and consequently reduces the power consumption by reducing transmissions. On the other hand, SHM research led to the development of several strategies for damage detection and localization which fulfill the requirements of effectiveness, simplicity, reliability and low amount of data storage, as shown in the works by Masri et al (2000), Casciati andCasciati (2006), andCasciati (2008). For instance, damage detection can be performed using time series analysis of vibration signals measured from a structure before and after damage.…”

Section: Introductionmentioning

confidence: 99%

A prediction-error-based method for data transmission and damage detection in wireless sensor networks for structural health monitoring

Yıldırım

Oguz

Bogdanovic

2013

Journal of Vibration and Control

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In this work, a prediction-error-based method that aims to reduce the amount of data transmission and storage in structural health monitoring wireless sensor networks (WSN) while keeping the damage detection capabilities intact is presented. WSNs offer tremendous promise for accurate and continuous structural monitoring using a dense array of inexpensive sensors and possess many advantages over conventional wired systems, particularly for large civil infrastructures. This paper validates the studied approach with the data collected during a test on a three-story steel frame mounted at the ELSA laboratory of the EU Joint Research Center in Ispra, Italy. The frame is tested with and without steel bracing, which results in non-sway behavior and sway frame responses. The excitation is provided by a shaker mounted on top of the frame. Accelerometer signals are collected by a multi-channel data acquisition system. The proposed method has been tested extensively via recorded experimental data and it offers considerable saving in transmitted energy while at the same time keeping the ability to detect changes or damage in the structure.

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Application of Neural Networks for Detection of Changes in Nonlinear Systems

Cited by 186 publications

References 10 publications

A Review of Nonlinear Dynamics of Mechanical Systems in Year 2008

A Review of Nonlinear Dynamics of Mechanical Systems in Year 2008

Earthquake damage detection in Tehran’s water distribution system

A prediction-error-based method for data transmission and damage detection in wireless sensor networks for structural health monitoring

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