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Effective fault tolerance techniques are essential to improve the yield of such complex chips.
Additionally, we proposed a quantitative FDI framework for effective fault diagnosis for hybrid systems.
The sensitive feature extraction from vibration signals is still a great challenge for effective fault classification of rolling element bearing.
Safe process operation requires effective fault detection (FD) methods that can identify faults in various process parameters.
They can provide the administrator with effective fault information in initial phase and therefore reduce the loss caused by faults.
Extraction of health condition information from these sensors for effective fault detection and fault tracking is always challenging.
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In this paper, an effective fault-tolerant framework offering very high error coverage with zero detection latency is proposed to protect the data paths of VLIW processor cores.
Designing an effective and efficient fault-tolerant routing protocol is inherently hard, since the problem is NP-complete because of the unavailability of precise path information in adversarial environments [1].
Designing an effective and efficient fault-tolerant routing protocol is inherently hard since the precise path information is unavailable [8,9].
Therefore, how to design a more cost-effective fault-tolerant method becomes a critical issue.
The results indicate that the SSI-MSVM method is effective in fault diagnosis for a wind turbine bearing and can successfully identify fault types of bearing and achieve higher diagnostic accuracy than that of K-means clustering, fuzzy means clustering and traditional SVM.
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