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The methodology consists in probabilistic mapping of the measured data into a fault variable.
Furthermore, the applied methodology can quickly detect the fault variable irrespective of using the fault detection indices where the variable showing highest variation is most likely to be the fault variable.
The fault in one process variable readily affects the other variables which makes it difficult to identify the fault variable precisely.
Normal and fault case scenarios are developed and compared statistically to identify the fault variable and to estimate the fault propagation path in the system.
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Our procedure consists of scripts which take the initial CNF, selects desired fault variables, sets output and drug weights, and solves the CNF using Maxsatz.
We inject faults (variable level faults as well as bit level faults) into objects declared with the extended data types.
Requirements for high availability in computing systems today demand that systems be self-adaptive to maintain expected qualities-of-service in the presence of system faults, variable environmental conditions, and changing user requirements.
This matrix and this vector are used to solve a linear system of equations where the following post-fault variables are: current in substation, the voltage at the fault point and voltages in the PV grid-connected nodes.
Order analysis (OA) methods, which include tachometer-based OA and tacholess OA methods, have been proven to be effective tools for diagnosing bearing fault under variable speed conditions.
This phenomenon is well known, of course, but is particularly dramatic when the rupture extends over a wide fault with variable velocity structure.
For the source fault, a variable slip model of the Tohokuoki earthquake (Ozawa, personal communication; Fig. 1), based on continuous Global Positioning System (GPS) observation, was used to calculate ACFF.
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