In the past K.E. Holbert has collaborated on articles with H.M. Hashemian. One of their most recent publications is Safety related application (Part I)Sensor response time monitoring using noise analysis. Which was published in journal Progress in Nuclear Energy.

More information about K.E. Holbert research including statistics on their citations can be found on their Copernicus Academic profile page.

K.E. Holbert's Articles: (2)

Safety related application (Part I)Sensor response time monitoring using noise analysis

AbstractRandom noise techniques in nuclear power plants have been developed for system surveillance and for analysis of reactor core dynamics. The noise signals also contain information about sensor dynamics, and this can be extracted using frequency, amplitude and time domain analyses. Even though noise analysis has been used for sensor response time testing in some nuclear power plants, an adequate validation of this method has never been carried out. This paper presents the results of limited work recently performed to examine the validity of the noise analysis for sensor response time testing in nuclear power plants. The conclusion is that noise analysis has the potential for detecting gross changes in sensor response but it cannot be used for reliable measurement of response time until more laboratory and field experience is accumulated. The method is more advantageous for testing pressure sensors than it is for temperature sensors. This is because: 1) for temperature sensors, a method called Loop Current Step Response test is available which is quantitatively more exact than noise analysis, 2) no method currently exists for on-line testing of pressure transmitters other than the Power-Interrupt test which is applicable only to force balance pressure transmitters, and 3) pressure sensor response time is affected by sensing line degradation which is inherently taken into account by testing with noise analysis.

Empirical process modeling technique for signal validation

AbstractSome techniques for fault detection involve the comparison of measured process signals with independent estimates. The prediction of process variables can be achieved either by physical or empirical modeling of a plant subsystem. An automated procedure for generating empirical process models is developed here. Independent prediction of critical signals is required for consistency checking of instrument outputs, for their degradation monitoring and for isolating common-mode failures. The steady-state empirical models are developed using data from different steady-state conditions. Signal anomaly is identified by comparing the error between the model-based prediction and the actual measurement with a fuzzy function (curve) utilizing the signal tolerance as a threshold. In the event a signal is declared as failed, the predicted estimate is used as input to a control/safety system or for display to an operator. Application of the methodology to signal validation using operational data from a commercial PWR and the EBR-II is presented.

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