Application of dynamic neural networks with exogenous input to industrial conditional monitoring

Yusuf, Syed Adnan; Brown, David; Mackinnon, Alan; Papanicolaou, Richard

doi:10.1109/ijcnn.2013.6706762

Cited by 7 publications

(8 citation statements)

References 33 publications

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“…Xu et al (2009), meanwhile, used continuous WT to train fuzzy logic system (FLS) under variable speed conditions. Yusuf et al (2013) utilised GA to optimised statistical time features that used to train DNN. In Camarena et al (2014), EMD of current signals employed to tran NN under nonstationary operating conditions.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

A Robust Bearing Fault Detection and Diagnosis Technique for Brushless DC Motors Under Non-stationary Operating Conditions

Abed

Sharma

Sutton

et al. 2015

J Control Autom Electr Syst

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Section: Resultsmentioning

confidence: 99%

“…This model is a type of RNN that has a delay line on the input, and the output is fed back to the input by another delay line (Yusuf et al 2013). Such a network with one hidden layer and four output units is shown in Fig.…”

Section: Recurrent Neural Network (Rnn) For Real-time Condition Monitmentioning

confidence: 99%

A Robust Bearing Fault Detection and Diagnosis Technique for Brushless DC Motors Under Non-stationary Operating Conditions

Abed

Sharma

Sutton

et al. 2015

J Control Autom Electr Syst

View full text Add to dashboard Cite

“…In this case, the anomalies are usually caused by equipment malfunction but as already mentioned, there is little difference between the effects faulty equipment and an attack. An architecture for industrial condition monitoring that is based on a dynamic neural network was proposed by the authors in [ 52 ]. The proposed scheme was designed to work in noisy, dynamic non-linear systems and was tested on the cooling system of a poly-phase induction motor.…”

Section: Non-parametric Methodsmentioning

confidence: 99%

“…However, like the one in [ 41 ] , the scheme was not specifically evaluated for a resource constrained environment as the practical feasibility was not backed up by any experimental data. Of the three ANN schemes [ 50 , 51 , 52 ] discussed two of them considered the practical implications and one of them required a substantial amount prior knowledge. Most of the remaining schemes required prior knowledge with the exception being the only GA scheme that considered the practical implications [ 54 ].…”

Section: Observations and Recommendationsmentioning

confidence: 99%

A Survey of Anomaly Detection in Industrial Wireless Sensor Networks with Critical Water System Infrastructure as a Case Study

Ramotsoela

Abu‐Mahfouz

Hancke

2018

Sensors

116

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The increased use of Industrial Wireless Sensor Networks (IWSN) in a variety of different applications, including those that involve critical infrastructure, has meant that adequately protecting these systems has become a necessity. These cyber-physical systems improve the monitoring and control features of these systems but also introduce several security challenges. Intrusion detection is a convenient second line of defence in case of the failure of normal network security protocols. Anomaly detection is a branch of intrusion detection that is resource friendly and provides broader detection generality making it ideal for IWSN applications. These schemes can be used to detect abnormal changes in the environment where IWSNs are deployed. This paper presents a literature survey of the work done in the field in recent years focusing primarily on machine learning techniques. Major research gaps regarding the practical feasibility of these schemes are also identified from surveyed work and critical water infrastructure is discussed as a use case.

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“…Outside of wind energy NARX has been used for condition monitoring. In [17], a comparison between dynamic neural networks and NARX was made. This was for detecting faults within the cooling system fan of a motor.…”

Section: Narxmentioning

confidence: 99%

Effect of Time History on Normal Behaviour Modelling Using SCADA Data to Predict Wind Turbine Failures

et al. 2020

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Operations and Maintenance (O&M) can make up a significant proportion of lifetime costs associated with any wind farm, with up to 30% reported for some offshore developments. It is increasingly important for wind farm owners and operators to optimise their assets in order to reduce the levelised cost of energy (LCoE). Reducing downtime through condition-based maintenance is a promising strategy of realising these goals. This is made possible through increased monitoring and gathering of operational data. SCADA data are useful in terms of wind turbine condition monitoring. This paper aims to perform a comprehensive comparison between two types of normal behaviour modelling: full signal reconstruction (FSRC) and autoregressive models with exogenous inputs (ARX). At the same time, the effects of the training time period on model performance are explored by considering models trained with both 12 and 6 months of data. Finally, the effects of time resolution are analysed for each algorithm by considering models trained and tested with both 10 and 60 min averaged data. Two different cases of wind turbine faults are examined. In both cases, the NARX model trained with 12 months of 10 min average Supervisory Control And Data Acquisition (SCADA) data had the best training performance.

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Application of dynamic neural networks with exogenous input to industrial conditional monitoring

Cited by 7 publications

References 33 publications

A Robust Bearing Fault Detection and Diagnosis Technique for Brushless DC Motors Under Non-stationary Operating Conditions

A Robust Bearing Fault Detection and Diagnosis Technique for Brushless DC Motors Under Non-stationary Operating Conditions

A Survey of Anomaly Detection in Industrial Wireless Sensor Networks with Critical Water System Infrastructure as a Case Study

Effect of Time History on Normal Behaviour Modelling Using SCADA Data to Predict Wind Turbine Failures

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