Autonomous Sensor System for Low-Capacity Wind Turbine Blade Vibration Measurement.

This paper presents the design, implementation, and validation of an on-blade sensor system for remote vibration measurement for low-capacity wind turbines. The autonomous sensor system was deployed on three wind turbines, with one of them operating in harsh weather conditions in the far south of Chile. The system recorded the acceleration response of the blades in the flapwise and edgewise directions, data that could be used for extracting the dynamic characteristics of the blades, information useful for damage diagnosis and prognosis. The proposed sensor system demonstrated reliable data acquisition and transmission from wind turbines in remote locations, proving the ability to create a fully autonomous system capable of recording data for monitoring and evaluating the state of health of wind turbine blades for extended periods without human intervention. The data collected by the sensor system presented in this study can serve as a foundation for developing vibration-based strategies for real-time structural health monitoring.

Near-instantaneous battery End-of-Discharge prognosis via uncertain event likelihood functions.

The prediction of the time of occurrence of future events has been studied for decades in various scientific disciplines. Such events have historically been defined as moments where variables of interest (or indicators) hit pre-determined thresholds (i.e. the first-hitting time). Recently, semi-closed mathematical expressions were reported in the literature to analytically characterize the probability distribution of the occurrence time of future events, extending the event triggering criteria based on thresholds to a more general and intuitive threshold-less approach, where the occurrence of events is declared through the use of uncertain event likelihood functions. The End-of-Discharge time prognosis problem is formalized in this article via the definition of uncertain event likelihood functions. Moreover, a novel numerical method is proposed to compute the probability distribution of its future time of occurrence based on this conceptualization and exploiting the capability of uncertain event likelihood functions to assimilate uncertainty in analytic expressions. A case study related to energy autonomy in electromobility applications is considered; specifically an electric bicycle. Obtained results show that the proposed method achieves an extraordinary level of convergence in less than a tenth of a second, while the same level of convergence using Monte Carlo simulations under the traditional threshold crossing approach takes hours to be achieved.

Adaptive passivity-based control extended for unknown control direction.

This research extends the design of adaptive passivity-based controllers (APBC), proposing a normalized APBC (NAPBC) for nonlinear dynamical systems with a direction of control unknown. The plant also has an accessible single-input and a single-output, smooth behavior, linear explicit parametric dependence, and unknown parameters. The proposed method can handle unknown control direction through an alternate and more straightforward method than Nussbaum gains, having two fewer parameters. We present the stability proof of the controlled system. Besides, the proposed NAPBC expands the tuning method for normalized fixed gains or time-varying gains, reducing the trial and error procedure. Finally, we apply the proposed methodology step by step to a conical tank-scale pilot plant. Comparative experimental results show the proposed NAPBC has better indexes ISI, Ess, MO, IAE, and Ts.

Extending the integrate-and-fire model to account for metabolic dependencies.

It is widely accepted that the brain, like any other physical system, is subjected to physical constraints that restrict its operation. The brain's metabolic demands are particularly critical for proper neuronal function, but the impact of these constraints continues to remain poorly understood. Detailed single-neuron models are recently integrating metabolic constraints, but these models' computational resources make it challenging to explore the dynamics of extended neural networks, which are governed by such constraints. Thus, there is a need for a simplified neuron model that incorporates metabolic activity and allows us to explore the dynamics of neural networks. This work introduces an energy-dependent leaky integrate-and-fire (EDLIF) neuronal model extension to account for the effects of metabolic constraints on the single-neuron behavior. This simple, energy-dependent model could describe the relationship between the average firing rate and the Adenosine triphosphate (ATP) cost as well as replicate a neuron's behavior under a clinical setting such as amyotrophic lateral sclerosis (ALS). Additionally, EDLIF model showed better performance in predicting real spike trains - in the sense of spike coincidence measure - than the classical leaky integrate-and-fire (LIF) model. The simplicity of the energy-dependent model presented here makes it computationally efficient and, thus, suitable for studying the dynamics of large neural networks.

Online joint estimation and prediction for system-level prognostics under component interactions and mission profile effects.

Failure prognostics has become a central element in predictive maintenance. In this domain, the accurate determination of the remaining useful life (RUL) allows making effective maintenance and operation decisions about the assets. However, prognostics is often approached from a component point of view, and system-level prognostics, taking into account component interactions and mission profile effects, is still an underexplored area. To address this issue, we propose an online joint estimation and prediction methodology using a modeling framework based on the inoperability input-output model (IIM). This model can consider the interactions between components and also the mission profile effects on a system's degradation. To estimate the system's parameters in real-time, with a minimum of prior knowledge, an online estimation process based on the gradient descend algorithm is recursively performed when acquiring new measurements. After each update, the estimated model is used to predict the system RUL. The performance of the proposed approach is highlighted through different numerical examples. In addition, these developments are applied to a real industrial application, the Tennessee Eastman Process, in order to show their effectiveness.