Multiagent-Based Collaborative Framework for a Self-Managing Structural Health Monitoring System

Smarsly, Kay; Law, Kincho H.; Hartmann, Dietrich

doi:10.1061/(asce)cp.1943-5487.0000107

Cited by 28 publications

(11 citation statements)

References 19 publications

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“…The data conversion is done at the main server using the open-source tool "Pentaho Data Integration" (Roldan, 2009;Castors, 2008). Performance tests validating the automated data conversion process are documented in Smarsly and Hartmann (2009a, 2009b and Smarsly et al (2012).…”

Section: Decentralized Software Systemmentioning

confidence: 99%

“…To detect such an anomaly, the data interrogator agent extracts and analyzes the measurements stored in the monitoring database. For that purpose, a set of configuration files specifying interrogation parameters, database URL, database driver, sensor specification, interrogation intervals, etc., is predefined and stored in the multiagent system (Smarsly et al, 2012). The agent-based connection to the monitoring database is realized through Java Database Connectivity (JDBC), an industry standard for database-independent connectivity between Java applications, such as software agents, and various database systems.…”

Section: Multiagent-based Self-diagnostic Systemmentioning

confidence: 99%

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An integrated monitoring system for life-cycle management of wind turbines

Smarsly¹,

Hartmann²,

Law³

2013

Smart Structures and Systems

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Abstract.With an annual growth rate of about 30%, wind energy systems, such as wind turbines, represent one of the fastest growing renewable energy technologies. Continuous structural health monitoring of wind turbines can help improving structural reliability and facilitating optimal decisions with respect to maintenance and operation at minimum associated life-cycle costs. This paper presents an integrated monitoring system that is designed to support structural assessment and life-cycle management of wind turbines. The monitoring system systematically integrates a wide variety of hardware and software modules, including sensors and computer systems for automated data acquisition, data analysis and data archival, a multiagent-based system for selfdiagnosis of sensor malfunctions, a model updating and damage detection framework for structural assessment, and a management module for monitoring the structural condition and the operational efficiency of the wind turbine. The monitoring system has been installed on a 500 kW wind turbine located in Germany. Since its initial deployment in 2009, the system automatically collects and processes structural, environmental, and operational wind turbine data. The results demonstrate the potential of the proposed approach not only to ensure continuous safety of the structures, but also to enable cost-efficient maintenance and operation of wind turbines.

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Section: Decentralized Software Systemmentioning

confidence: 99%

Section: Multiagent-based Self-diagnostic Systemmentioning

confidence: 99%

An integrated monitoring system for life-cycle management of wind turbines

Smarsly¹,

Hartmann²,

Law³

2013

Smart Structures and Systems

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“…The LCM framework provides valuable information that can be used not only to monitor the integrity of the wind turbine structure, but also to support new research and to test algorithmic developments. The LCM framework -details can be found in Smarsly et al (2012aSmarsly et al ( ,b,c, 2013a and Hartmann et al (2011) -consists of several interconnected components that are installed at geographically distributed locations:…”

Section: Wind Turbine Life-cycle Management Frameworkmentioning

confidence: 99%

Analyzing the Temporal Variation of Wind Turbine Responses Using Gaussian Mixture Model and Gaussian Discriminant Analysis

Park

Smarsly

Law

et al. 2015

J. Comput. Civ. Eng.

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Site-and time-specific wind field characteristics have a significant impact on the structural response and the lifespan of wind turbines. This paper presents a machine learning approach towards analyzing and predicting the response of a wind turbine structure to diurnal and nocturnal wind fields. Machine learning algorithms are applied (i) to better understand the changes of wind field characteristics due to atmospheric conditions, and (ii) to gain insights into the wind turbine loads being affected by the wind field. Using Gaussian Mixture Model, the variations in wind field characteristics are 2 investigated by comparing the joint probability density functions of selected wind field features. The wind field features are constructed from long-term monitoring data taken from a 500 kW wind turbine in Germany that is used as a reference system. Furthermore, employing Gaussian Discriminant Analysis, representative daytime and nocturnal wind turbine loads are compared and analyzed.

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“…Insignificant differences are observed that are possibly attributable to the higher turbulence at daytime. Finally, it is worth mentioning that when comparing the representative daytime and nocturnal data of September, 2012, with data sets 22/41 recorded in previous years [26,27], no significant changes in the structural response are detected.…”

Section: Operational and Structural Wind Turbine Responsementioning

confidence: 94%

A computational framework for life-cycle management of wind turbines incorporating structural health monitoring

Smarsly

Hartmann

Law

2013

Structural Health Monitoring

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The integration of structural health monitoring into life-cycle management strategies can help facilitating a reliable operation of wind turbines and reducing the life-cycle costs significantly. This paper presents a life-cycle management (LCM) framework for online monitoring and performance assessment of wind turbines, enabling optimum maintenance and inspection planning at minimum associated life-cycle costs. Incorporating continuously updated monitoring data (i.e. structural, environmental and operational data), the framework allows capturing and understanding the actual wind turbine condition and, hence, reduces uncertainty in structural responses as well as load effects acting on the structure. As will be shown in this paper, the framework integrates a variety of heterogeneous hardware and software components, including sensors and data acquisition units, server systems, Internetenabled user interfaces as well as finite element models for system identification and a multiagent system for self-detecting sensor malfunctions. To validate its capabilities and to Smarsly et al. 2 demonstrate its practicability, the framework is deployed for continuous monitoring and lifecycle management of a 500 kW wind turbine. Remote life-cycle analyses of the monitored wind turbine are conducted and case studies are presented investigating both the structural performance and the operational efficiency of the wind turbine.

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Multiagent-Based Collaborative Framework for a Self-Managing Structural Health Monitoring System

Cited by 28 publications

References 19 publications

An integrated monitoring system for life-cycle management of wind turbines

An integrated monitoring system for life-cycle management of wind turbines

Analyzing the Temporal Variation of Wind Turbine Responses Using Gaussian Mixture Model and Gaussian Discriminant Analysis

A computational framework for life-cycle management of wind turbines incorporating structural health monitoring

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