Artificial neural network approach for detection and diagnosis of valve stiction

Venceslau, Allan; Guedes, Luiz Affonso; Silva, Diego R. C.

doi:10.1109/etfa.2012.6489768

Cited by 10 publications

(10 citation statements)

References 5 publications

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“…Rossi and Scali, 2005;Srinivasan et al, 2005a). In recent years, a number of stiction detection methods based on neural network/machine learning have been presented in the literature (Amiruddin et al, 2019;Kamaruddin, 2020;Napoli et al, 2019;Venceslau et al, 2012). So, it is required to add one more category of stiction detection method to the above classification, i.e.…”

Section: Detection Methods For Stictionmentioning

confidence: 99%

“…The time-series data segments are classified as increasing, decreasing or steady. Then, a stiction index is Horch and Isaksson (2001) Histogram All type Kano et al (2004) Limit cycle pattern All type Yamashita (2006a) Limit cycle pattern Only FC Scali and Ghelardoni (2008) Limit cycle pattern Only FC Daneshwar and Noh (2015) Limit cycle pattern Only FC Bra ´sio et al (2015) Limit cycle pattern Only LC Yamashita (2006b) Limit cycle pattern + Statistics Only LC Rengaswamy et al (2001) Waveform shape + ANN All type Rossi and Scali (2005) Waveform shape All type Srinivasan et al (2005a) Waveform shape All type Singhal and Salsbury (2005) Waveform shape All type except LC He et al (2007) Waveform shape All type Zabiri and Ramasamy (2009) Waveform shape + Nonlinearity detection All type Teh et al (2018) Waveform shape + Nonlinearity detection All type Ahmed et al (2009) Waveform shape All type Stockmann et al (2009) Waveform shape All type except LC Ha ¨gglund (2011) Waveform shape All type Farenzena and Trierweiler (2012b) Waveform shape Only LC Dambros et al (2016) Waveform shape All type Thornhill (2005) Nonlinearity detection All type Choudhury et al (2006) Nonlinearity detection All type Aftab et al (2016Aftab et al ( , 2017 Nonlinearity detection All type Farenzena and Trierweiler (2009) Machine learning (ANN) All type Venceslau et al (2012) Machine learning (ANN) All type Sharma et al (2017) Machine learning (ANN) Only FC Amiruddin et al (2019) Machine learning (ANN) All type Dambros et al (2019) Machine learning (ANN) All type Napoli et al (2019) Machine learning (CNN) Only FC Kamaruddin et al (2020) Machine learning (CNN) All type Henry et al (2020) Machine learning (CNN) All type Choudhury et al (2007) Controller gain change method All type…”

Section: Limit Cycle Pattern-based Methodsmentioning

confidence: 99%

“…But, the prediction accuracy is slightly poorer compared with previous methods. Venceslau et al (2012) proposed another ANN model to detect stiction. The ANN model used multilayer perceptron network trained with the algorithm of back-propagation.…”

Section: Machine Learning-based Methodsmentioning

confidence: 99%

“…Romano and Garcia, 2011;Wang and Wang, 2009), neural network-based (e.g. Farenzena and Trierweiler, 2009;Kamaruddin et al, 2020;Venceslau et al, 2012).…”

Section: Quantification Methods For Stictionmentioning

confidence: 99%

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Comparison of current state of control valve stiction detection and quantification techniques

Akavalappil

Radhakrishnan

2021

Transactions of the Institute of Measurement and Control

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The process control and automation system is one of the significant features in any process plant. The control valve is considered a fundamental part of the process control system. It regulates the fluid flow by changing the size of the flow passage as directed by the controller. If properly maintained, the control valves can help to reduce process variability and improve product quality, which in turn increases the overall efficiency of the plant. But control valve stiction is an enduring concern within process industries which eventually affects the efficiency of plant operation. Hence, it is extremely essential to detect and quantify stiction at the earliest opportunity, to determine the correct maintenance measures. Numerous methods related to stiction detection and estimation have been carried out so far by researchers. A literature study of the latest publications in stiction detection and quantification discloses that research in the conventional methods of stiction detection and quantification such as limit cycle pattern-based, waveform shape-based, nonlinearity-based, and model-based methods is gradually declining. At the same time, most stiction detection/quantification methods reported in the recent literature are based on artificial neural network and convolutional neural network. This implies that future stiction detection and quantification research will be focused towards machine learning (ML) algorithms. Taking this into consideration, this paper aims to provide a literature review of stiction detection and quantification methods by focusing on the latest research ideas and innovative approaches. This review also aims to compare recent techniques based on ML algorithms with conventional methods by pointing out the relationship among different methods, differences, and possible points of weakness.

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Section: Detection Methods For Stictionmentioning

confidence: 99%

Section: Limit Cycle Pattern-based Methodsmentioning

confidence: 99%

Section: Machine Learning-based Methodsmentioning

confidence: 99%

“…Romano and Garcia, 2011;Wang and Wang, 2009), neural network-based (e.g. Farenzena and Trierweiler, 2009;Kamaruddin et al, 2020;Venceslau et al, 2012).…”

Section: Quantification Methods For Stictionmentioning

confidence: 99%

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Comparison of current state of control valve stiction detection and quantification techniques

Akavalappil

Radhakrishnan

2021

Transactions of the Institute of Measurement and Control

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“…Most of the detection methods proposed are based on the relationship between process variables (PV) and controller outputs (OP) due to the difficulties in observing manipulated variable (MV). Various detection methods based on neural networks have been proposed, such as NLPCA [5], ANN [6], NLPCA-AC [7], and SDN [8]. These methods consider the time series input directly as 1D signal.…”

Section: Introductionmentioning

confidence: 99%

Control valve stiction detection by use of AlexNet and transfer learning

2021

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Control valve stiction is a common problem faced by the process industries, which can have a strong adverse effect on the profitable operation of plants. Although various stiction detection methods based on neural networks have been proposed, few of these studies have considered the performance of stiction detection based on the use of 2D representations of the process signals. In this paper, such an approach is proposed, based on the use of a pretrained convolutional neural network, AlexNet. The proposed convolutional neural network stiction detection (CNN-SD) method showed highly satisfactory performance, which can be further applied on real industrial data.

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Control valve stiction detection using Markov transition field and deep convolutional neural network

Memarian,

Damarla,

Huang

2023

Can J Chem Eng

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Control valve stiction is an industrial problem that often causes oscillations in process control loops. Oscillating control loops are not capable of maintaining key process variables near or at their desired values, thus yielding low‐quality products, inducing economic loss, and increasing environmental impacts. Therefore, it is of vital importance to detect stiction in industrial control valves. In this regard, the present work proposes a new method based on the Markov transition field and convolutional neural network (CNN) to identify sticky control valves in industrial control loops. The Markov transition field is employed to convert process variable (PV) and controller output (OP) into two‐dimensional images, which are then utilized by CNN to learn to distinguish stiction induced oscillations from oscillations brought out by a non‐stiction condition. A transfer learning strategy is adopted to improve the stiction detection capability of the proposed method. Its performance is evaluated via its application to benchmark control loops taken from the chemical, paper, mining, and metal industries. Results demonstrate that the proposed method obtains the correct verdict for the majority of the control loops studied.

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Artificial neural network approach for detection and diagnosis of valve stiction

Cited by 10 publications

References 5 publications

Comparison of current state of control valve stiction detection and quantification techniques

Comparison of current state of control valve stiction detection and quantification techniques

Control valve stiction detection by use of AlexNet and transfer learning

Control valve stiction detection using Markov transition field and deep convolutional neural network

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