A Multi-Agent Fault Detection System for Wind Turbine Defect Recognition and Diagnosis

Zaher, Ammar; McArthur, S.D.J.

doi:10.1109/pct.2007.4538286

Cited by 48 publications

(31 citation statements)

References 8 publications

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“…Anomaly detection consists of finding patterns in data that do not follow the expected behavior (Chandola et al, 2009). Any deviation from the normal profile of the model is considered as an anomaly (Zaher and McArthur, 2007); its most common applications are related to cyber security. However, the concept of anomaly detection is applied in different fields, among which is the industrial damage detection, which refers to detection of different faults and failures in complex industrial systems (Chandola et al, 2009).…”

Section: Discussionmentioning

confidence: 99%

Towards Effective Automatic Feedback for Simulator Training

Marcano

Yazidi

Ferati

et al. 2017

Linköping Electronic Conference Proceedings

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The presence of an expert instructor during simulator training is of great importance for the trainees, if not indispensable. The instructor's role is to give feedback and guide the trainees to help them make the right decisions on time as effectively as possible. The instructor starts or pauses the training scenarios when needed and facilitates reflection during and after the scenarios. However, the fact that simulator-training sessions are very dependent on the participation of a guiding instructor can be a drawback, since there are not always sufficient expert instructors to fulfil the training demands. In this work, an intelligent tutoring system (ITS) is proposed as an automatic feedback solution for simulator training. It is based on effective assessment of the system conditions using a clustering based anomaly detection technique as a core component. Furthermore, we provide insights into the design of a proper interface for our ITS. The article presents the methodology for developing such a system which consists of three stages: data collection, data analysis and delivering feedback.

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Section: Discussionmentioning

confidence: 99%

Towards Effective Automatic Feedback for Simulator Training

Marcano

Yazidi

Ferati

et al. 2017

Linköping Electronic Conference Proceedings

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“…Transient and oscillatory stabilities were analysed with different wind scenarios for electricity generation process [44]. Zaher and McArthur [45] give an explanation of the use of signals and trending for fault detection based on parameter estimation. Performance monitoring analysis the relationship between parameters such as power, wind speed, blade angle and rotor speed for an assessment of wind turbine condition and for the early detection of faults [46].…”

Section: Degree Of Importance Is Further Normalized By Divid-mentioning

confidence: 99%

Strategic maintenance technique selection using combined quality function deployment, the analytic hierarchy process and the benefit of doubt approach

Baidya

Dey

Ghosh

et al. 2016

Int J Adv Manuf Technol

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The business performance of manufacturing organizations depends on the reliability and productivity of equipment, machineries and entire manufacturing system. Therefore, the main role of maintenance and production managers is to keep manufacturing system always up by adopting most appropriate maintenance methods. There are alternative maintenance techniques for each machine, the selection of which depend on multiple factors. The contemporary approaches to maintenance technique selection emphasize on operational needs and economic factors only. As the reliability of production systems is the strategic intent of manufacturing organizations, maintenance technique selection must consider strategic factors of the concerned organization along with operational and economic criteria. The main aim of this research is to develop a method for selecting the most appropriate maintenance technique for manufacturing industry with the consideration of strategic, planning and operational criteria through involvement of relevant stakeholders. The proposed method combines quality function deployment (QFD), the analytic hierarchy process (AHP) and the benefit of doubt (BoD) approach. QFD links strategic intents of the organizations with the planning and operational needs, the AHP helps in prioritizing the criteria for selection and ranking the alternative maintenance techniques, and the BoD approach facilitates analysing robustness of the method through sensitivity analysis through setting the realistic limits for decision making. The proposed method has been applied to maintenance technique selection problems of three productive systems of a gear manufacturing organization in India to demonstrate its effectiveness.

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“…2). A typical SCADA data is 10 min averaged data [19]. Thus the collected and stored SCADA data must then be examined in order to deduce the overall health of the turbine as well as its internal components.…”

Section: Scada Data Based Cms For Wtmentioning

confidence: 99%

“…They developed, applied, and tested several data analysis methods and measurement configurations successfully and concluded that for all types of measurement SCADA data, time series, vibration monitoring could be used for CM [18]. Zaher and McArthur also proposed an idea to use the combination of abnormal detection and data-trending techniques encapsulated in a multi-agent framework for the development of a fault detection system for WTs [19]. Pre-processing of SCADA is a must for extraction of useful information and patterns from huge data.…”

Section: Scada Data Based Cms For Wtmentioning

confidence: 99%

SCADA data based condition monitoring of wind turbines

2014

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Wind turbines (WTs) are quite expensive pieces of equipment in power industry. Maintenance and repair is a critical activity which also consumes lots of time and effort, hence making it a costly affair. Carefully planning the maintenance based upon condition of the equipment would make the process reasonable. Mostly the WTs are equipped with some kind of condition monitoring device/system, which provides the information about the device to the central data base i.e., supervisory control and data acquisition (SCADA) data base. These devices/systems make use of data processing techniques/methods in order to detect and predict faults. The information provided by condition monitoring equipments keeps on recoding in the SCADA data base. This paper dwells upon the techniques/methods/algorithms developed, to carry out diagnosis and prognosis of the faults, based upon SCADA data. Subsequently data driven approaching for SCADA data interpretation has been reviewed and an artificial intelligence (AI) based framework for fault diagnosis and prognosis of WTs using SCADA data is proposed.

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A Multi-Agent Fault Detection System for Wind Turbine Defect Recognition and Diagnosis

Cited by 48 publications

References 8 publications

Towards Effective Automatic Feedback for Simulator Training

Towards Effective Automatic Feedback for Simulator Training

Strategic maintenance technique selection using combined quality function deployment, the analytic hierarchy process and the benefit of doubt approach

SCADA data based condition monitoring of wind turbines

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