@InProceedings{6952112,
  author = {A. Youssef and C. Delpha and D. Diallo},
  booktitle = {2014 22nd European Signal Processing Conference (EUSIPCO)},
  title = {Performances theoretical model-based optimization for incipient fault detection with KL Divergence},
  year = {2014},
  pages = {466-470},
  abstract = {Sensible and reliable incipient fault detection methods are major concerns in industrial processes. The Kullback Leibler Divergence (KLD) has proven to be particularly efficient. However, the performance of the technique is highly dependent on the detection threshold and the Signal to Noise Ratio (SNR). In this paper, we develop an analytical model of the fault detection performances (False Alarm Probability and Miss Detection Probability) based on the KLD including the noisy environment characteristics. Thanks to this model, an optimization procedure is applied to set the optimal fault detection threshold depending on the SNR and the fault severity.},
  keywords = {fault diagnosis;optimisation;principal component analysis;signal detection;optimization;performance modeling;miss detection probability;false alarm probability;detection threshold;Kullback Leibler Divergence;KL divergence;incipient fault detection;Fault detection;Monitoring;Cost function;Signal to noise ratio;Noise measurement;Fault detection;performance modeling;Optimization;Kullback-Leibler Divergence;Principal Component Analysis},
  issn = {2076-1465},
  month = {Sep.},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2014/html/papers/1569924767.pdf},
}

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However, the performance of the technique is highly dependent on the detection threshold and the Signal to Noise Ratio (SNR). In this paper, we develop an analytical model of the fault detection performances (False Alarm Probability and Miss Detection Probability) based on the KLD including the noisy environment characteristics. Thanks to this model, an optimization procedure is applied to set the optimal fault detection threshold depending on the SNR and the fault severity.","keywords":"fault diagnosis;optimisation;principal component analysis;signal detection;optimization;performance modeling;miss detection probability;false alarm probability;detection threshold;Kullback Leibler Divergence;KL divergence;incipient fault detection;Fault detection;Monitoring;Cost function;Signal to noise ratio;Noise measurement;Fault detection;performance modeling;Optimization;Kullback-Leibler Divergence;Principal Component Analysis","issn":"2076-1465","month":"Sep.","url":"https://www.eurasip.org/proceedings/eusipco/eusipco2014/html/papers/1569924767.pdf","bibtex":"@InProceedings{6952112,\n author = {A. Youssef and C. Delpha and D. Diallo},\n booktitle = {2014 22nd European Signal Processing Conference (EUSIPCO)},\n title = {Performances theoretical model-based optimization for incipient fault detection with KL Divergence},\n year = {2014},\n pages = {466-470},\n abstract = {Sensible and reliable incipient fault detection methods are major concerns in industrial processes. The Kullback Leibler Divergence (KLD) has proven to be particularly efficient. However, the performance of the technique is highly dependent on the detection threshold and the Signal to Noise Ratio (SNR). In this paper, we develop an analytical model of the fault detection performances (False Alarm Probability and Miss Detection Probability) based on the KLD including the noisy environment characteristics. 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