Control Loop Performance Assessment. 1. A Qualitative Approach for Stiction Diagnosis

and, Ranganathan Srinivasan†; Rengaswamy, Raghunathan; Miller, Randy

doi:10.1021/ie0490280

Cited by 64 publications

(61 citation statements)

References 45 publications

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“…Type Loop Applicability Horch [40] cross-correlation no LC Horch [48] statistics all type Kano et al [29] MV(OP) patterns all (better FC) Yamashita [41] MV(OP) patterns only FC Scali and Ghelardoni [42] MV(OP) patterns only FC Daneshwar and Noh [49] MV(OP) patterns only FC Yamashita [50] statistics only LC Farenzena and Trierweiler [51] waveform shape only LC Choudhury et al [43] NL detection all Thornhill [44] NL detection all Mohammad and Huang [52] NL detection all Rengaswamy et al [53] waveform shape all Srinivasan et al [45] waveform shape all Rossi & Scali [46] waveform shape all He et al [30] waveform shape all Singhal and Salsbury [47] waveform shape no LC Hägglund [54] waveform shape all Zabiri and Ramasamy [55] waveform shape all Ahmed et al [56] waveform shape all Ahammad and Choudhury [57] harmonics based all Zakharov et al [58] algorithms combination all Afterwards, following this line, Cuadros et al [60] proposed an improved algorithm that fits an ellipse just using the most significant points of OP and PV signals. Despite this method being applicable only to flow control loops, the procedure seems to have more precision than the previous approaches.…”

Section: Methods Featuresmentioning

confidence: 99%

“…Following Jelali and Huang [11], they can be broadly classified into four categories: cross-correlation function-based [40], limit cycle patterns-based (e.g., [29,41,42]), nonlinearity detection based (e.g., [43,44]), and waveform shape-based (e.g., [30,[45][46][47]). …”

Section: Detection Techniquesmentioning

confidence: 99%

“…The approach of Srinivasan et al [45] consisted of pattern recognition using the dynamic time warping technique. An optimal alignment between measured data and a stiction template pattern for each oscillating cycle and a global pattern for the whole dataset is performed.…”

Section: Farenzena and Trierweilermentioning

confidence: 99%

See 2 more Smart Citations

RETRACTED: Review on Valve Stiction. Part I: From Modeling to Smart Diagnosis

Capaci

Scali

2015

Processes

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Abstract:Valve stiction is indicated as one of the main problems affecting control loop performance and then product quality. Therefore, it is important to detect this phenomenon as early as possible, distinguish it from other causes, and suggest the correct action to the operator in order to fix it. It is also very desirable to give an estimate of stiction amount, in order to be able to follow its evolution in time to allow the scheduling of valve maintenance or different operations, if necessary. This paper, in two parts, is a review of the state of the art about the phenomenon of stiction from its basic characterization to smart diagnosis, including modeling, detection techniques, quantification, compensation and a description of commercial software packages. In particular, Part I of the study analyzes the most significant works appearing in the recent literature, pointing out analogies and differences among various techniques, showing more appealing features and possible points of weakness. The review also includes an illustration of the main features of performance monitoring systems proposed by major software houses. Finally, the paper gives indications on future research trends and potential advantages for loop diagnosis when additional measurements are available, as in newly designed plants with valve positioners and smart instrumentation. In Part II, performance of some well-established methods for stiction quantification are compared by applications to different industrial datasets. OPEN ACCESSProcesses 2015, 3 423

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Section: Methods Featuresmentioning

confidence: 99%

Section: Detection Techniquesmentioning

confidence: 99%

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RETRACTED: Review on Valve Stiction. Part I: From Modeling to Smart Diagnosis

Capaci

Scali

2015

Processes

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“…Horch (1999) proposed that an odd correlation between manipulated and process variable indicates the presence of valve non-linearities in nonintegrating plants. Other methods based on shape analysis formalisms for 50 detecting valve non-linearities are put forth by Srinivasan et al (2005), Hägglund (2011) and Yamashita (2005).…”

Section: Introductionmentioning

confidence: 99%

Detecting non-linearity induced oscillations via the dyadic filter bank property of multivariate empirical mode decomposition

Aftab¹,

Hovd²,

Sivalingam

2017

Journal of Process Control

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Non-linearity induced oscillations in control loops are characterized by the presence of higher order harmonics. In this paper the dyadic filter bank property of the Multivariate Empirical Mode Decomposition (MEMD) is 10 exploited to reveal the harmonic content of the oscillatory signal to indicate the presence of non-linearity. Once the harmonics are identified the extent of non-linearity is evaluated automatically using Degree of non-linearity measure (DNL) introduced in our previous work (Aftab et al., 2016). Although detection of non-linearity via harmonics is an old concept; any automatic 15 method has still not been reported. Moreover, the existing methods suffer from the restrictive assumption of signal stationarity. The proposed method is more robust in identifying the non-linearity induced oscillations and is adaptive and data driven in nature and thus requires no a priori assumption about the underlying process dynamics. The proposed method 20 can also differentiate among the different sources of multiple oscillations, for example combinations of nonlinearity and linear sources or two nonlinear sources. Apart from detecting the non-linearities the proposed method can also contribute in locating the source of non-linearity, thereby reducing the maintenance time to a considerable extent. The robustness and effective-25 ness of the proposed method is established using industrial case studies and results are compared with existing methods based on higher order statistics (Choudhury et al., 2008) and surrogate based methods (Thornhill, 2005).

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“…proposed a correlation based method to detect valve stiction in non-integrating plants. Srinivasan et al (2005a), have proposed methods based on shape formalisms to detect stiction by comparing the waveform of the data with predefined trends. Non-linear time series methods have been investigated by to detect presence of non-linearities in control loops.…”

Section: Non-linearity Analysismentioning

confidence: 99%

A Delay Vector Variance based Approach for Detecting and Isolating the Non-linearity Induced Oscillations in Control Loops

Aftab¹,

Hovd²,

Sivalingam

2017

IFAC-PapersOnLine

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SummaryThe profitability of any industrial process is closely related to its ability to maintain near optimal operating conditions; therefore robust methods for root cause detection of any control performance degradation is crucial not only to maintain the desired operation of any industrial process but can result in marked improvement in productivity and over all economics.In a large and complex industrial control system, disturbances/oscillations originating at one point tend to propagate both up-and downstream due to underlying interactions, process flows and recycle streams, thus leading to plant-wide effects. Effective controller performance monitoring therefore requires detection and diagnosis of such plant-wide disturbances so that targeted maintenance can be achieved within the shortest possible time.Oscillations are considered to be the most important indicator of control performance degradation. Oscillations not only cause product variability and effect quality but also lead to adverse economical consequences owing to loss of precious resources like raw material and energy. Therefore, automated detection and diagnosis of oscillations needs to be reliable and effective as the large scale of the control system in industrial process plants makes manual observation and diagnosis practically impossible.Detection and diagnosis of oscillatory control loops is not trivial due to the causes such as presence of multiple oscillations, unknown process dynamics, non stationary effects and noise corruption, to name a few. The work presented in this thesis is aimed at addressing these challenges and at the development of improved tools for both detection and diagnosis of oscillations and plant-wide disturbances. Moreover, data driven methods are preferred over model based diagnostics as they are more general and are independent of a specific plant model. Dynamic proiii iv Summary cess models can be costly to develop and maintain, and for many process plants dynamic models are not available.This thesis has focussed on using the multivariate empirical mode decomposition (MEMD) and associated characteristics for the detection and diagnosis of oscillations in control loops. The methods proposed in thesis are aimed at addressing the shortcomings of the existing approaches and are fully data driven that require no a priori knowledge about the data or process itself. An improved approach for the oscillation detection has been presented that caters for non-stationary effects and reduces the mode mixing problems associated with the univariate empirical mode decomposition. Moreover, MEMD along with the proposed grouping algorithm provides a robust method to detect the plant-wide oscillations where different control variables, having common signatures of oscillations, are oscillating due to the same root cause. This helps in searching for the root cause only in the affected variables.Another important contribution of the thesis is an automated detection of harmonics in an oscillating signal. Non-linearity induced oscillations g...

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Control Loop Performance Assessment. 1. A Qualitative Approach for Stiction Diagnosis

Cited by 64 publications

References 45 publications

RETRACTED: Review on Valve Stiction. Part I: From Modeling to Smart Diagnosis

RETRACTED: Review on Valve Stiction. Part I: From Modeling to Smart Diagnosis

Detecting non-linearity induced oscillations via the dyadic filter bank property of multivariate empirical mode decomposition

A Delay Vector Variance based Approach for Detecting and Isolating the Non-linearity Induced Oscillations in Control Loops

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