When a fault occurs in a process, it slowly propagates within the system and affects the measurements triggering a sequence of alarms in the control room. The operators are required to diagnose the cause of alarms and take necessary corrective measures. The idea of representing the alarm sequence as the fault propagation path and using the propagation path to diagnose the fault is explored. A diagnoser based on hidden Markov model is built to identify the cause of the alarm signals. The proposed approach is applied to an industrial case study: Tennessee Eastman process. The results show that the proposed approach is successful in determining the probable cause of alarms generated with high accuracy. The model was able to identify the cause accurately, even when tested with short alarm sub-sequences. This allows for early identification of faults, providing more time to the operator to restore the system to normal operation.

中文翻译：

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基于隐马尔可夫模型的报警信号原因诊断方法

当过程中发生故障时，它会在系统内缓慢传播并影响测量值，从而在控制室触发一系列警报。要求操作人员诊断报警原因并采取必要的纠正措施。探索了将警报序列表示为故障传播路径并使用传播路径诊断故障的想法。建立了一个基于隐马尔可夫模型的诊断器来识别报警信号的原因。建议的方法应用于工业案例研究：田纳西伊士曼工艺。结果表明，所提出的方法成功地确定了产生高精度警报的可能原因。该模型能够准确地识别原因，即使在使用短警报子序列进行测试时也是如此。这允许及早识别故障，