Soft Fault Detection Algorithms for Multi-Parallel Data Streams Under the Cloud Computing
Author(s) -
Hongbing Meng
Publication year - 2018
Publication title -
journal of advanced computational intelligence and intelligent informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.172
H-Index - 20
eISSN - 1343-0130
pISSN - 1883-8014
DOI - 10.20965/jaciii.2018.p1114
Subject(s) - computer science , data stream , cloud computing , fault detection and isolation , data stream mining , fault (geology) , algorithm , data mining , real time computing , artificial intelligence , telecommunications , seismology , actuator , geology , operating system
In the fault detection of multi-parallel data streams, the error probability of traditional methods is large, which cannot effectively meet the soft fault detection for multi-parallel data stream, causing the problem of low detection efficiency. A soft fault detection algorithm based on adaptive multi-parallel data stream is proposed. The soft fault feature in the data stream is extracted, and the adaptive soft fault detection algorithm is used to detect the fault of the multi-parallel data stream, which can overcome the disadvantages of traditional methods, effectively improve the efficiency, safety and the accuracy. Experimental results showed that the proposed method can effectively improve the efficiency of fault detection.
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