求翻译：In this paper, fault diagnosis based on patterns exhibited in the sensors measuring the process variables is considered. The temporal patterns that a process event leaves on the measured sensors, called event signatures, can be utilized to infer the state of operation using a pattern-matching approach. However, the qualitative nature of the features leads to imprecise classification boundaries at the trend-identification stage and hence at the trend-matching stage. Moreover, noise and other underlying phenomena may lead to non-reproducibility of the same trends chosen to represent an event. Thus, a crisp inference process might lead to a large knowledge-base of signatures; it could also cause misclassification. To overcome this, a fuzzy-reasoning approach is proposed to ensure robustness to the inherent uncertainty in the identified trends and to provide succinct mapping. A two-staged strategy is employed: (i) identifying the most likely fault candidates based on a similarity measure between the observed trends and the event-signatures in the knowledge-base and, (ii) estimation of the fault magnitude. The fuzzy-knowledge-base consists of a set of physically interpretable if是什么意思？

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