本文選題：系統(tǒng)級(jí)故障診斷 + 概率性矩陣診斷算法　； 參考：《南京理工大學(xué)學(xué)報(bào)》2017年04期

【摘要】：系統(tǒng)級(jí)故障診斷是提高多處理器系統(tǒng)可靠性的必要手段。為了有效定位多處理系統(tǒng)中的故障單元,該文建立了一種基于PMC模型t可診斷條件下的概率性矩陣診斷算法。首先對(duì)一般概率性矩陣診斷算法進(jìn)行仿真分析獲悉其具有較高的誤檢率,在診斷過(guò)程中引進(jìn)絕對(duì)故障基和節(jié)點(diǎn)集團(tuán)思想,通過(guò)計(jì)算絕對(duì)故障基以尋找系統(tǒng)中的部分故障處理機(jī),集團(tuán)用于將不確定狀態(tài)的節(jié)點(diǎn)單元分類(lèi)以補(bǔ)充正常節(jié)點(diǎn)集合,改善了原診斷的限制條件。仿真實(shí)驗(yàn)驗(yàn)證:改進(jìn)后的概率性矩陣診斷算法保持了很高的檢測(cè)精度,并且隨著節(jié)點(diǎn)數(shù)的增多極大地降低了誤檢率,提高了診斷效果,使得該算法具有廣泛的適用性。
[Abstract]:System level fault diagnosis is an essential means to improve the reliability of multiprocessor systems. In order to effectively locate the fault units in multiprocessing systems, a probability matrix diagnosis algorithm based on PMC model t diagnostics is established in this paper. Firstly, the general probability matrix diagnosis algorithm is simulated and found that it has a high false detection rate. In the process of diagnosis, the idea of absolute fault base and node group is introduced, and the absolute fault base is calculated to find part of the fault processor in the system. The cluster is used to classify the node units of uncertain states to supplement the normal set of nodes, which improves the limit condition of the original diagnosis. Simulation results show that the improved probabilistic matrix diagnosis algorithm has high detection accuracy, and with the number of nodes increasing, the false detection rate is greatly reduced, and the diagnosis effect is improved, which makes the algorithm widely applicable.
【作者單位】： 西安電子科技大學(xué)數(shù)學(xué)與統(tǒng)計(jì)學(xué)院;
【基金】：國(guó)家自然科學(xué)基金(71271165)
【分類(lèi)號(hào)】：TP332
，

本文編號(hào)：2001394