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Due to technical and practical faults (e.g., malfunction of the rhinomanometer, errors in performing the NCT or in the documentation of results), evaluable data were obtained only from 61% of the ITT study population.
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This approach combines design aspects and practical fault analysis issues.
Thus, voltage swells during the practical fault recovery change with transients and unbalances.
Yet, increased complexity and critical operation states might increase the practical fault risk.
This article mainly aims to discuss the feasibility and possibility of practical fault detection for hydraulic pump using neural network in conjunction with chaos theory.
The comparison approach is believed to be one of the most practical fault identification approaches for diagnosing hard and soft faults.
As analyzed in the paper, the RECS cannot always ride through the continuous period during the practical fault recovery without increasing the dc-link voltage, even when the input active power is zero.
According to the maximum sequence PCC voltages of the practical fault recovery analyzed in Section 2, the RECS cannot always both counteract the overvoltage and eliminate the dc-link voltage fluctuations during the continuous period.
In this article, a parallel radial basis function network in conjunction with chaos theory (CPRBF network) is presented, and applied to practical fault detection for hydraulic pump, which is a critical component in aircraft.
As verified by the simulation, the scheme can promise the RECS to deal with the practical fault recovery period and mitigate the dc-link voltage fluctuations, which improves the reliability of the RECS and the power system.
As consensus is such an important problem and time limits for sending messages and executing tasks in real systems, such as the Internet, are unknown, the FLP impossibility represents a challenge for developing practical fault-tolerant distributed systems.
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