A framework for on-line trend extraction and fault diagnosis

Maurya, Mano Ram; Paritosh, Praveen; Rengaswamy, Raghunathan; Venkatasubramanian, Venkat

doi:10.1016/j.engappai.2010.01.027

Cited by 60 publications

(32 citation statements)

References 52 publications

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“…The window is iteratively halved in two and the fit process re-iterated until the fit error decreases below a threshold fixed in function of the noise estimate. An on-line version was proposed in [8] by processing the method on data stored on line in a FIFO (first in first out) buffer.…”

Section: Review Of Methods For Trend Extractionmentioning

confidence: 99%

“…The residual variability is calculated in a similar way as in Section 4.1: The median of the residual is calculated on a time window of size N r preceding the segmentation time t seg (8).…”

Section: Presentation Of the Methodsmentioning

confidence: 99%

“…Then, the trends are used as inputs to a decision system whose outputs correspond to the faults to be diagnosed on line. The decision system can be either a classifier learned from a training data-base [1][2][3], a knowledge-based system built using expert's rules [4][5][6][7] or a pattern-matching system that compares the trend extracting on-line with fault trend patterns using a similarity measure [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

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A self-tuning adaptive trend extraction method for process monitoring and diagnosis

Charbonnier

Portet

2012

Journal of Process Control

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Section: Review Of Methods For Trend Extractionmentioning

confidence: 99%

“…The residual variability is calculated in a similar way as in Section 4.1: The median of the residual is calculated on a time window of size N r preceding the segmentation time t seg (8).…”

Section: Presentation Of the Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A self-tuning adaptive trend extraction method for process monitoring and diagnosis

Charbonnier

Portet

2012

Journal of Process Control

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“…Recent contributions have extended the approach to adapt to disturbances at different time scales [16], to work on-line [17], and to find the optimal start and end points of a shape in the time series [18]. However, this approach requires the creation of a complete database with the possible shapes of a transient disturbance.…”

Section: A Analysis Of Transient Disturbancesmentioning

confidence: 99%

“…Figure 23 shows the frequency distribution of ratio (17) for terms j leading to positive detections. Positive detections are determined based on the threshold proposed in section IV-B.…”

mentioning

confidence: 99%

Multivariate Detection of Transient Disturbances for Uni- and Multirate Systems

Cecı́lio

Ottewill

Fretheim

et al. 2015

IEEE Trans. Contr. Syst. Technol.

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This paper presents a method to detect transient disturbances in a multivariate context, and an extension of that method to handle multi-rate systems. Both methods are based on a time series analysis technique known as nearest neighbors, and on multivariate statistics implemented as a singular value decomposition. The motivation for these developments is that there is an increasing industrial requirement for the analysis of data sets comprising measurements from industrial processes together with their associated electrical and mechanical equipment. These systems are increasingly affected by transient disturbances, and their measurements are commonly sampled at different rates. The paper demonstrates superior results with the multivariate method in comparison to the univariate approach, and with the multi-rate method in comparison to a uni-rate method, for which the fast-sampled measurements had to be downsampled. The method is demonstrated on experimental and industrial case studies.

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A novel framework for integrating data mining with control loop performance assessment

2015

Self Cite

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Data driven control loop performance assessment techniques assume that the data being analyzed correspond to single plant-controller configuration. However, in an industrial setting where processes are affected due to the presence of feedstock variability and drifts, the plant-controller configuration changes with time. Also, user-defined benchmarking of control loops (common in industrial plants) requires that the data corresponding to optimal operation of the controller be known. However, such information might not be available beforehand in which case it is necessary to extract the same from routine plant operating data. A technique that addresses these fundamental requirements for ensuring reliable performance assessment is proposed. The proposed technique performs a recursive binary segmentation of the data and makes use of the fact that changes in controller settings translate to variations in plant output for identifying regions corresponding to single plant-controller configurations. The statistical properties of the data in each such window are then compared with the theoretically expected behavior to extract the data corresponding to optimal configuration. This approach has been applied on: (1) raw plant output, (2) Hurst exponent, and (3) minimum variance index of the process data. Simulation examples demonstrate the applicability of proposed approach in industrial settings. A comparison of the three routes is provided with regard to the amount of data needed and the efficacy achieved. Key results are emphasized and a framework for applying this technique is described. This tool is of significance to industries interested in an automated analysis of large scale control loop data for multiple process variables that is otherwise left unutilized.

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A framework for on-line trend extraction and fault diagnosis

Cited by 60 publications

References 52 publications

A self-tuning adaptive trend extraction method for process monitoring and diagnosis

A self-tuning adaptive trend extraction method for process monitoring and diagnosis

Multivariate Detection of Transient Disturbances for Uni- and Multirate Systems

A novel framework for integrating data mining with control loop performance assessment

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