From measurement collection to remaining useful life estimation : defining a diagnostic-prognostic frame for optimal maintenance scheduling of choke valves undergoing erosion
Condition Based Maintenance (CBM) aims at regulating maintenance scheduling based on data analyses and system condition monitoring. Clear advantages of optimizing maintenance scheduling include relevant cost savings and improved safety and plant availability. A critical aspect is the integration of CBM strategies with condition monitoring technologies for handling a wide range of information sources and eventually making optimal decisions on when and what to repair. In this work, a practical case study concerning maintenance of choke valves in offshore oil platforms has been investigated. Choke valves used in offshore oil platforms undergo erosion caused by the sand grains transported by the oil-water-gas mixture extracted from the well. Erosion is a critical problem which can affect the correct functioning of the valves, result in revenue losses and cause environmental hazards. In this respect, this work proposes a diagnostic-prognostic scheme for assessing the actual health state of a choke va lv and eventually estimating its Remaining Useful Life (RUL). In particular, the focus has been on th e identification of those parameters which contribute to the actual erosion of the choke valve, the development of a model-based approach for calculating a reliable indicator of the choke valve health state, the actual estimation of the choke RU L based on that indicator using statistical approache s and, finally, the investigation of methods to reduc e the uncertainty of the RUL estimation by adding highly meaningful knowledge on the erosion state of the choke valve .
Failure prediction using personalized models and an application to heart failure prediction
Background To reduce disruptions of processes and the cost of maintenance, predicting the onset of failure (or a similar event) of a physical system (or components of a physical system) has become important. Prediction of onset of failure would allow appropriate corrective actions at the right time. In this paper, we present a method to predict the “onset” of failure (the start of a degradation process or similar types of events) of a physical system that minimizes data collection and personalizes it for the physical system. The method applies to situations where one monitors the operating characteristics of the physical system at regular time intervals by means of attached sensors and other measurement instruments. It creates a model of the physical system, during normal operations, using the time-series data produced by the sensors and measurement instruments. However, it does not create or use any time-series models. It simply examines the distribution of time-series data across different time periods. It uses this model of normal operations in subsequent time periods to monitor the physical system for deviations from normality. Results We illustrate this method with an application to predict the “onset” of subsequent decompensated heart failures for patients already treated for a heart failure at a hospital. As part of an NIH study, these heart failure patients received two ECG patches, an accelerometer and a bio-impedance measurement device for regular monitoring for a period after their release from the hospital. Conclusions When dealing with non-homogenous, disparate physical systems, personalized models can be better predictors of a phenomenon compared to generalized models based on data collected from an assortment of such physical systems. In medicine such models can be a powerful addition to the set of medical diagnostic tools. And such personalized models can be built rather quickly without waiting for extensive data collection.
Numerical tools applied to power reactor noise analysis
In order to be able to calculate the space- and frequency-dependent neutron noise in real inhomogeneous systems in two-group theory, a code was developed for the calculation of the Green's function (dynamic transfer function) of such systems. This paper reports on the development as well as the test and application of the numerical tools employed. The code that was developed yields the space-dependence of the fluctuations of the neutron flux induced by fluctuating properties of the medium in the two-group diffusion approximation and in a two-dimensional representation of heterogeneous systems, for both critical systems and non-critical systems with an external source. Some applications of these tools to power reactor noise analysis are then described, including the unfolding of the parameters of the noise source from the induced neutron noise, measured at a few discrete locations throughout the core. Other concrete applications concern the study of the space-dependence of the Decay Ratio in Boiling Water Reactors, the noise-based estimation of the Moderator Temperature Coefficient of reactivity in Pressurized Water Reactors, the modeling of the beam- and shell-mode core-barrel vibrations in Pressurized Water Reactors, and the investigation of the validity of the point-kinetic approximation in subcritical systems driven by an external source. In most of these applications, calculations performed using the code are compared with at-power plant measurements. Power reactor noise analysis applications of the above type, i.e. core monitoring without disturbing plant operation, is of particular interest in the framework of the extensive program of power uprates worldwide.