On-line loose parts monitoring false alarm discrimination

Zigler, G.L.; Morris, Gilbert; Roberts, R.C.

doi:10.1016/0149-1970(85)90085-x

Cited by 3 publications

(1 citation statement)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The main objective of the second stage is to subsequently label all the clusters produced in the first-stage classification into groups of true and false alarms. Based on the existence of time difference of detecting the vibration signals generated by the loose parts by sensors amounted on different positions, Zigler [7] proposed judging whether the suspect signal is impact signal or not according to the compatibility of signal detecting time among the related sensors.…”

Section: Introductionmentioning

confidence: 99%

An Alarm Method for a Loose Parts Monitoring System

Cao

Zheng

et al. 2012

Shock and Vibration

View full text Add to dashboard Cite

Abstract. In order to reduce the false alarm rate and missed detection rate of a Loose Parts Monitoring System (LPMS) for Nuclear Power Plants, a new hybrid method combining Linear Predictive Coding (LPC) and Support Vector Machine (SVM) together to discriminate the loose part signal is proposed. The alarm process is divided into two stages. The first stage is to detect the weak burst signal for reducing the missed detection rate. Signal is whitened to improve the SNR, and then the weak burst signal can be detected by checking the short-term Root Mean Square (RMS) of the whitened signal. The second stage is to identify the detected burst signal for reducing the false alarm rate. Taking the signal's LPC coefficients as its characteristics, SVM is then utilized to determine whether the signal is generated by the impact of a loose part. The experiment shows that whitening the signal in the first stage can detect a loose part burst signal even at very low SNR and thusly can significantly reduce the rate of missed detection. In the second alarm stage, the loose parts' burst signal can be distinguished from pulse disturbance by using SVM. Even when the SNR is −15 dB, the system can still achieve a 100% recognition rate.

show abstract

Section: Introductionmentioning

confidence: 99%