The phrase "automated fault" is correct and usable in written English.
It can be used in contexts related to technology, engineering, or systems where faults or errors are detected and managed automatically.
Example: "The system is designed to handle automated faults, ensuring minimal downtime during operations."
Alternatives: "automated error" or "automated issue".
Existing chiller data collected for the design of automated fault detection and diagnosis methodology is used.
The fault injection test bench procedure requires automated fault injection, programmable fault load conditions and combined analogue/digital domain verification.
The device under test is implemented using discrete n- and p-FETs arranged as a modular test board together with automated fault injection and test lines.
An automated fault location method is developed using a two stage radial basis function neural network (RBFNN) in which the first RBFNN determines the fault distance from each source while the second RBFNN identifies the exact faulty line.
Dynamic simulation tools that could accurately simulate operational data for both the fault-free and faulty dynamic operation of heating, ventilation, and air conditioning (HVAC) systems and equipment are needed for developing and evaluating advanced control and automated fault detection and diagnosis strategies.
In [63], an automated fault location method was discussed which was developed using a two stage radial basis function neural network (RBFNN).
In future work, the counterexample, generated on the verification of a failing design, will be used to automate fault localization [67], regarding digital filters implementations. 1 Available at http://dsverifier.org/. 2 http://dsverifier.org/.org/
This treatment describes the details of a systematic protocol useful for performing optimal automated process fault analysis.
Using this algorithm consequently directly simplifies the solution of the more complicated problem of automated process fault analysis into the much more tractable, and incrementally solvable, problem of adequately modeling normal process operations.
Our goal in this work is to explore a feasible approach called MPI to automate a fault localization process by automatically modeling symptom-fault causality in a highly dynamic and large-scale SDN network.
Accordingly, we develop a management system to automate network fault identification merely based on the analysis of the abnormal events from the monitored timing constraint distributed system.
