• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.9B
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• pp.793-805
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• 2003

In OBS (Optical Burst Switching) networks which decouple the burst from its header, the fault of a fiber link can lead to the failure of all the light-path that traverses the fiber. Because each light-path is expected to operate at a rate of a few Gbps by using WDM (Wavelength Division Multiplexing) technology, any failure may lead to large data loss. Therefore, an efficient recovery scheme must be provided. In this paper, we analyze network utilization and BCP (Burst Control Packet) loss rate according to each link failure by applying the conventional restoration schemes in OBS networks. And through these simulation results, an ASPR scheme is proposed improve the fault management scheme in terms of recovery time and throughput. Finally, We compare the performance of our proposed scheme with that of the conventional one with respect to burst loss rate, resource utilization and throughput by OPNET simulations.

• Nuclear Engineering and Technology
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• v.53 no.1
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• pp.148-163
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• 2021

Timely fault identification is important for safe and reliable operation of the electric valve system. Many research works have utilized different data-driven approach for fault diagnosis in complex systems. However, they do not consider specific characteristics of critical control components such as electric valves. This work presents an integrated shallow-deep fault diagnostic model, developed based on signals extracted from DN50 electric valve. First, the local optimal issue of particle swarm optimization algorithm is solved by optimizing the weight search capability, the particle speed, and position update strategy. Then, to develop a shallow diagnostic model, the modified particle swarm algorithm is combined with support vector machine to form a hybrid improved particle swarm-support vector machine (IPs-SVM). To decouple the influence of the background noise, the wavelet packet transform method is used to reconstruct the vibration signal. Thereafter, the IPs-SVM is used to classify phase imbalance and damaged valve faults, and the performance was evaluated against other models developed using the conventional SVM and particle swarm optimized SVM. Secondly, three different deep belief network (DBN) models are developed, using different acoustic signal structures: raw signal, wavelet transformed signal and time-series (sequential) signal. The models are developed to estimate internal leakage sizes in the electric valve. The predictive performance of the DBN and the evaluation results of the proposed IPs-SVM are also presented in this paper.