Assessment of Early Stopping through Statistical Health Prognostic Models for Empirical RUL Estimation in Wind Turbine Main Bearing Failure Monitoring

Details about a fault’s progression, including the remaining-useful-lifetime (RUL), are key features in monitoring, industrial operation and maintenance (O&M) planning. In order to avoid increases in O&M costs through subjective human involvement and over-conservative control strategies, this work presents models to estimate the RUL for wind turbine main bearing failures. The prediction of the RUL is estimated from a likelihood function based on concepts from prognostics and health management, and survival analysis. The RUL is estimated by training the model on run-to-failure wind turbines, extracting a parametrization of a probability density function. In order to ensure analytical moments, a Weibull distribution is assumed. Alongside the RUL model, the fault’s progression is abstracted as discrete states following the bearing stages from damage detection, through overtemperature warnings, to over overtemperature alarms and failure, and are integrated in a separate assessment model. Assuming a naïve O&M plan (wind turbines are run as close to failure as possible without regards for infrastructure or supply chain constrains), 67 non run-to-failure wind turbines are assessed with respect to their early stopping, revealing the potential RUL lost. These are turbines that have been stopped by the operator prior to their failure. On average it was found that wind turbines are stopped 13 days prior to their failure, accumulating 786 days of potentially lost operations across the 67 wind turbines

The Effect on Wireless Sensor Communication When Deployed in Biomass

Wireless sensor networks (WSN) have been studied in a variety of scenarios over recent years, but work has almost exclusively been done using air as the transmission media. In this article some of the challenges of deploying a WSN in a heterogeneous biomass, in this case silage, is handled. The dielectric constant of silage is measured using an open-ended coaxial probe. Results were successfully obtained in the frequency range from 400 MHz to 4 GHz, but large variations suggested that a larger probe should be used for more stable results. Furthermore, the detuning of helix and loop antennas and the transmission loss of the two types of antennas embedded in silage was measured. It was found that the loop antenna suffered less from detuning but was worse when transmitting. Lastly, it is suggested that taking the dielectric properties of silage into account during hardware development could result in much better achievable communication range

Bayesian-based localization of wireless capsule endoscope using received signal strength

In wireless body area sensor networking (WBASN) applications such as gastrointestinal (GI) tract monitoring using wireless video capsule endoscopy (WCE), the performance of out-of-body wireless link propagating through different body media (i.e. blood, fat, muscle and bone) is still under investigation. Most of the localization algorithms are vulnerable to the variations of path-loss coefficient resulting in unreliable location estimation. In this paper, we propose a novel robust probabilistic Bayesian-based approach using received-signal-strength (RSS) measurements that accounts for Rayleigh fading, variable path-loss exponent and uncertainty in location information received from the neighboring nodes and anchors. The results of this study showed that the localization root mean square error of our Bayesian-based method was 1.6 mm which was very close to the optimum Cramer-Rao lower bound (CRLB) and significantly smaller than that of other existing localization approaches (i.e. classical MDS (64.2mm), dwMDS (32.2mm), MLE (36.3mm) and POCS (2.3mm)).Engineering and Applied Science