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中文摘要：
针对现有组合Web服务诊断模型故障诊断准确率普遍不高的问题,提出一种新颖的基于改进隐马尔可夫模型（Improved-HMM）的故障诊断方法.首先,从组合服务监测数据中提取多维特征序列训练HMM模型.训练过程中,考虑到基于BW的方法仅在某观测条件下进行参数评估,获得的参数准确度不高,提出基于贝叶斯估计的学习方法,得到更客观的参数;进一步,基于改进的HMM模型计算当前特征序列对应的各类故障类型发生概率,推断最有可能的故障类型.实验结果表明,提出的方法具有较高的诊断率和较低的漏报率,适合在网络环境中进行实时故障检测.
英文摘要：
To address the problem that most of the existing composite Web service models are of low accuracy on fault disgnosis, a novel composite Web service oriented fault diagnosis approach was proposed based on an improved hidden Markov model （I-HMM）. Firstly, HMM model was trained by using the processed multi-dimensional feature sequences. In this process, the BW-based methods were not used for parameters estimation, since inaccurate parameters would often resulted in due to the single observation. Instead, a Bayes estimation based method to gain more objective paratemeters was proposed. Finally, the probabilities of different fault types caused by the current feature sequence were computed. The one of the maximum probability was inferred as the ultimate fault type. Experimental results showed that the method was effective and efficent. Due to the high diagnostic rate and the low false rate, it was suitable for real-time fault detection in network environment.
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