Performance management via adaptive thresholds with separate control of false positive and false negative errors

Breitgand, David; Goldstein, Maayan; Henis, Ealan; Shehory, Onn

doi:10.1109/inm.2009.5188810

Cited by 4 publications

(1 citation statement)

References 9 publications

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“…Many works in literature face this issue and provide with many solutions [23,24,25]. The complete elimination is not achievable because in the real case it is always possible dealing with a peculiar scenario.…”

Section: False Positive Reductionmentioning

confidence: 99%

Power plant condition monitoring by means of coal powder granulometry classification

et al. 2018

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In this work, a condition monitoring approach suitable for coal fired power plant is proposed. This approach is based on classification techniques and it is applied for the monitoring of the Particle Size Distribution (PSD) of coal powder. For coal fired power plant, the PSD of coal can affect the combustion performance, therefore it is a meaningful parameter of the operating condition of the plant.Three tests have been carried out aimed to study the effect of the class numbers, the dataset size, and the reduction of the number of false positives on the effectiveness of the approach. For each designed test, three standard classification algorithms, i.e. Artificial Neural Network, Extreme Learning Machine and Support Vector Machine, have been employed and compared. Experimental data taken from 13 measuring point on 13 burners of two different industrial power plants have been used. Obtained results showed that, using two classes give the most accurate results, using only the 90% of the available data can still provide comparable classification results, and the level of false positive can be effectively reduced.

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Section: False Positive Reductionmentioning

confidence: 99%