Reliability.mediawiki

@@ -501,7 +501,7 @@ The data structure inside EDU, consists mainly of a hash table that comprises th
 By terminating the periodic check function, the pointer will refer to the second check process and so on until the check processes list is finished.
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-===== Fault detection mechanism in time domain =====
+==== Fault detection mechanism in time domain ====
 To cover fault hypothesis in time domain, two types of messages may be distinguished according to its timing behavior:
 *'''Periodic messages:''' this type of messages has a fixed period after which a new message of the same type should be transmitted over the network. One periodic message could be transmitted more than one time within one cluster cycle, therefore periodic messages with the same period but at different phases may be differentiated.
 *'''Sporadic messages''': in contrast to periodic messages, the sporadic messages have to be re-generated within a certain range of time. In other words it has a minimum and maximum inter arrival time and it should be re-transmitted within this range. 
@@ -522,7 +522,7 @@ The calendar re-arranges itself dynamically after each message arrival in such a
 
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-===== Semantic fault detection mechanism =====
+==== Semantic fault detection mechanism ====
 If the ensuring of the deterministic behavior for both the middleware and the communication infrastructure will help a lot in classifying faults regarding time, this will not be the case when a sensor or actuator deviates from its normal operation. It is more complicated to catch an error from the message semantic. However a wide vary of methods are already introduced to detect anomalies of a certain process. These methods may be classified as already done by Isserman in <ref>Isermann, Rolf. Fault Diagnosis System. Heidelberg : Springer, 2006.</ref>.
 *Signal based fault detection: by taking the measured signal several criteria may be applied directly e.g. limit checking or trend checking or may be by analyzing the measured signal a certain specification can be estimated and then tested.
 *Model based fault detection: this method is more complicated, it takes the measured signals for both input and output for a certain process and apply them to the mathematical model of the process. Then several features can be estimated e.g. parameters, state variables or residuals. By comparing these observed features with their nominal values analytical symptoms are generated.