On reducing false alarms in multivariate statistical process control

Chen, Tao

doi:10.1016/j.cherd.2009.09.003

Cited by 37 publications

(16 citation statements)

References 19 publications

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“…Zhu et al (Zhu et al, 2014) developed a dynamic alarm limit, which was especially suitable for process transitions such as feedstock, throughput, or product grade changes and maintenance operations. For selecting the alarm signals, some approaches, such as fuzzy clustering Geng et al, 2005), multivariate statistics (Chen, 2010), pattern matching (Cheng et al, 2013b) and Bayesian analysis (Pariyani et al, 2012), were proposed to improve process safety and product quality. Furthermore, two alarm data visualization tools, high density alarm plot (HDAP) and alarm similarity color map (ASCM), were presented to evaluate the integrated performance of alarm system (Kondaveeti et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Integrating probabilistic signed digraph and reliability analysis for alarm signal optimization in chemical plant

Peng¹,

Gu²,

Xu³

et al. 2015

Journal of Loss Prevention in the Process Industries

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Section: Introductionmentioning

confidence: 99%

Integrating probabilistic signed digraph and reliability analysis for alarm signal optimization in chemical plant

Peng¹,

Gu²,

Xu³

et al. 2015

Journal of Loss Prevention in the Process Industries

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“…Determine the elements of matrices A, Q as the preliminarily defined. As the previous definitions for equation (41) TT respectively, and set the searching step size as h then begin searching 5 T , 6 T in the range (49). For each searching step size, calculate p c (equation (37)) and flow rate of each pipe (equation (49), (50)), and test whether 1 Aq Q ξ +≤  is satisfied.…”

Section: Hydraulic and Thermal Calculation Based On Searchingmentioning

confidence: 99%

“…For each searching step size, calculate p c (equation (37)) and flow rate of each pipe (equation (49), (50)), and test whether 1 Aq Q ξ +≤  is satisfied. If it is not satisfied, update 5 T , 6 T with one step to continue calculation, or retain the temperature value and the corresponding flow rates.…”

Section: Hydraulic and Thermal Calculation Based On Searchingmentioning

confidence: 99%

See 1 more Smart Citation

Data Processing Approaches for the Measurements of Steam Pipe Networks in Iron and Steel Enterprises

XX¹,

MZ²,

H(王宏)³

et al. 2012

Energy Efficiency - The Innovative Ways for Smart Energy, the Future Towards Modern Utilities

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There are many reasons for the poor accuracy of steam measurement data. The most important reasons can be concluded as follows:1. The working conditions of the instrument deviating from the designed conditions Data Processing Approaches for the Measurements of Steam Pipe Networks in Iron and Steel Enterprises 245At present, the mass flow rates are mostly deduced by the volume flow rates and the density. However, the changes of temperature and pressure in the transmission process would lead to the density of steam deviate from the original designed value [3]. The measurement errors would be very large [1]. Any more, some superheated steam would change into a vapor-liquid two-phase medium, which making precision worse. The Complexity of Steam CharacteristicsWith the ambient temperature changing, the total amount of the condensate water in the transmission process will be different. That makes difference between the amount of the production and the consumption of steam. In addition, the steam pipe leakage will add to the difference. Therefore, the accumulated readings are always doubtful.3. The Occurrence of wearing or damage to the Key Components As the orifice differential pressure flow rate meter being in use for long time, the size of aperture would differ from the original size because of the adhered foreign bodies, or erosion by the durative high temperature steam flow. As the parameters can not be adjusted on time, and it is hard to calibrate the instrument, the measurement errors will accumulate.

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“…Hotelling's T2 statistic is typically employed in the determination of abnormalities by monitoring the variability of principal components (QIN, 2012;MACGREGOR and CINAR, 2012;VENKATASUBRAMANIAN et al, 2003b). Chen (2010), Villegas et al (2010) and Silva (2008) studies are examples of works that applied PCA and Hotelling's T2 statistic in process monitoring. The identification of abnormalities was performed through the construction of control charts.…”

Section: Introductionmentioning

confidence: 99%

Algorithm for Detection and Diagnosis of Industrial Furnace Faults Applying Singular Value Decomposition and Graphic Visualization

Fonseca

Teófilo

Soares

et al. 2017

JCEC

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ABSTRACT:The development of an algorithm based on singular value decomposition with graphic visualization to aid in the fault detection and diagnosis was the aim of this work. In order to test the algorithm, real temperature data from an industrial furnace of a natural gas processing unit were used. Nine temperature detectors were monitored over time. In addition to a real fault that caused the shutdown of the equipment, five simulated faults in the furnace detectors were also tested. Results showed that the algorithm was effective and has great potential to assist in the monitoring of industrial processes. RESUMO: O desenvolvimento de um algoritmo baseado na decomposição dos valores singulares com visualização gráfica para auxiliar na detecção e no diagnóstico de falhas foi o objetivo deste trabalho. Para testar o algoritmo foram utilizados dados de temperatura reais de um forno industrial

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On reducing false alarms in multivariate statistical process control

Cited by 37 publications

References 19 publications

Integrating probabilistic signed digraph and reliability analysis for alarm signal optimization in chemical plant

Integrating probabilistic signed digraph and reliability analysis for alarm signal optimization in chemical plant

Data Processing Approaches for the Measurements of Steam Pipe Networks in Iron and Steel Enterprises

Algorithm for Detection and Diagnosis of Industrial Furnace Faults Applying Singular Value Decomposition and Graphic Visualization

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