Investigation of feasibility of deterministic, theoretical predictive models underpinning visual condition monitoring outcomes

Aging reinforced concrete (R/C) bridge girders, currently in use, exhibit cracks wider than predicted at the design stage. Variation in material properties over time and increase in imposed live loads during service life can be the possible reasons for this behavioural difference. Visual condition monitoring reports of bridge assets do not seem to provide a more quantifiable explanation to this phenomenon. Therefore, authors propose a theoretical time dependent methodology, to predict the crack widths with age. The proposed method takes into consideration flexural stresses, shrinkage and creep. A bridge girder, currently in use, is analysed using proposed theoretical approach and the outcomes are compared with condition monitoring records available.

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Using Synthetic Data to Train a Visual Condition Monitoring System for Leak Detection

10.1109/bigdataservice52369.2021.00031 ◽

2021 ◽

Author(s):

Ralf Gitzel ◽

Arzam Kotriwala ◽

Tobias Fechner ◽

Martin Schiefer

Keyword(s):

Monitoring System ◽

Condition Monitoring ◽

Synthetic Data ◽

Leak Detection ◽

Visual Condition ◽

Condition Monitoring System

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Performance analysis of different predictive models for condition monitoring of direct drive wind turbine generator

Measurement and Control ◽

10.1177/00202940211003930 ◽

2021 ◽

pp. 002029402110039

Author(s):

Jui-Hung Liu ◽

Nelson T Corbita

Keyword(s):

Performance Analysis ◽

Monitoring System ◽

Condition Monitoring ◽

Wind Turbines ◽

Predictive Models ◽

Wind Farm ◽

Direct Drive ◽

The Neural Network ◽

Neural Network Algorithm ◽

Condition Monitoring System

This paper presents a performance analysis of predictive models for the generator module which can be used as a reference for improvement in the condition monitoring system using wind turbines in a wind farm in Taiwan. With the generator being a critical component prone to failures, it is important to perform data analysis on its parameters that could be used for condition monitoring. The main innovative feature in this framework is the conduct of performance analysis before the development of the condition monitoring system. Also, the consistency of the performance between the different wind turbines in the wind farm is evaluated. The predictive models are generated using the neural network algorithm with a different combination of parameters from the SCADA system. The correlation of the parameters as well as the mean square error of the predictive models were then computed for analysis. Results showed that pairing of input parameters with a higher correlation to the output parameter would give better performance for the predictive model. Furthermore, the performance of the different models was consistent throughout the different wind turbines in the wind farm which indicates that the same model can be developed and used for wind turbines belonging to the same wind farm. Employing a preliminary performance analysis of different combinations of component parameters could help in optimizing predictive models for condition monitoring.

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