The increasing adoption of electric vehicles (EVs) demands accurate methodologies for predicting their maximum driving range (MDR) under dynamic and uncertain operating conditions. Monte Carlo simulations (MCs) serve as a fundamental tool for MDR prognostics. However, their enormous computational cost makes real-time implementation impractical. In order to address this challenge, we propose a novel framework that replaces MC-based methods with Uncertain Event Likelihood Functions (UELFs) for real-time driving range prognostics, significantly reducing computational overhead while maintaining predictive accuracy. The UELF framework leverages probabilistic modeling and efficient numerical solutions to predict the likelihood of end-of-power availability events, complemented by machine learning (ML) models such as a stochastic dropout-based Gated Recurrent Unit for vehicle speed and a Light Gradient Boosting Machine for energy consumption prediction. These models are trained on data from various geographic and environmental conditions, ensuring generalization and robustness. Test results confirm that the UELF-based approach achieves accuracy comparable to MC simulations while reducing computational time by over 99 %, enabling seamless integration into online applications. Combining state-of-the-art ML methodologies with advanced uncertainty quantification strategies successfully bridges the gap between abstract mathematical models and practical engineering solutions, offering a scalable and efficient tool for advancing electromobility and supporting real-time decision-making algorithms in EV energy management and route planning.

Understanding the intricate interplay between neural dynamics and metabolic constraints is crucial for unraveling the mysteries of the brain. Despite the significance of this relationship, specific details concerning the impact of metabolism on neuronal dynamics and neural network architecture remain elusive, creating a notable gap in the existing literature. This study employs an energy-dependent neuron and plasticity model to analyze the role of local metabolic constraints in shaping both the dynamics and structure of Spiking Neural Networks (SNN). Specifically, an energy-dependent version of the leaky integrate-and-fire model is utilized, along with a three-factor learning rule that incorporates postsynaptic available energy as the third factor. These models allow for fine-tuning sensitivity in the presence of energy imbalances. Analytical expressions predicting the network's activity and structure are derived, and a fixed point analysis reveals the emergence of attractor states characterized by neuronal and synaptic sensitivity to energy imbalances. Analytical findings are validated through numerical simulations using an excitatory-inhibitory network. Furthermore, these simulations enable the study of SNN activity and structure under conditions simulating metabolic impairment. In conclusion, by employing energy-dependent models with adjustable sensitivity to energy imbalances, our study advances the understanding of how metabolic constraints shape SNN dynamics and structure. Moreover, in light of compelling evidence linking neuronal metabolic impairment to neurodegenerative diseases, the incorporation of local metabolic constraints into the investigation of neuronal network structure and activity opens an intriguing avenue for inspiring the development of therapeutic interventions.

Condition Monitoring (CM) has become an essential tool in complex engineering systems like wind turbines. They can prevent unexpected failures and contribute to a more reliable system. Information attained from monitoring can be employed for maintenance scheduling, hence, minimizing maintenance costs. Remaining Useful Life (RUL) is a critical aspect of CM. This paper introduces a new RUL prediction method for wind turbine blades using a novel fuzzy-based failure dynamic modeling via a Supervisory Control and Data Acquisition (SCADA) system. For this goal, a recursive Principal Component Analysis (PCA) is employed to compress the SCADA data and extract real-time Principal Components (PCs). Next, a wavelet-based Probability Density Function (PDF) is applied to obtain the probability of staying healthy from the extracted PCs. It is anticipated that blade degradation will lead to a subsequent decline in the PDF curve. A failure trajectory is then captured by transforming the PDF into the PC’s surface. Subsequently, the T–S fuzzy system is utilized to form the mathematical model of degradation from this failure trajectory. Next, a Bayesian algorithm is adaptively administered to predict the RUL. Experimental test results on Canadian wind farms explain a high performance of the proposed failure prognosis method in comparison with a Bayesian algorithm.

The failure progression of wind turbine bearings comprises of multiple degraded health states due to applied load by varying operating conditions (VOC). Therefore, determining the VOC impact on the failure dynamics severity is an essential task for bearing failure prognostics. This article introduces a hybrid prognosis method using real-time supervisory control and data acquisition (SCADA) and vibration signals to predict remaining useful life (RUL) for wind turbine bearings. The SCADA data are utilized to define the role of environmental conditions such as wind speed and ambient temperature in bearing failure dynamics. Afterward, for each environmental condition, failure dynamics are identified by the vibration signal. Finally, RUL of the faulty bearings is forecast via an adaptive Bayesian algorithm using the failure dynamics, conditional to the VOC. The efficacy of the method is validated using experimental data, and test results indicate a higher RUL accuracy compared to the Bayesian algorithm.

Fault diagnosis and prognosis are some of the most crucial functionalities in complex and safety-critical engineering systems, and particularly fault diagnosis, has been a subject of intensive research in the past four decades. Such capabilities allow for detection and isolation of early developing faults as well as prediction of fault propagation, which can allow for preventive maintenance, or even serve as a countermeasure to the possibility of catastrophic incidence as a result of a failure. Following a short preliminary overview and definitions, this article provides a survey of recent research on fault prognosis. Additionally, we report on some of the significant application domains where prognosis techniques are employed. Finally, some potential directions for future research are outlined.

This paper addresses the problem of uncertainty quantification in system-level prognostics. To this purpose, a three-step methodology, based on the inoperability input-output model, is presented. The first step concerns the estimation of the system inoperability, using a new adapted particle filtering method, while considering the interactions between its components. The second step focuses on the long-term prediction of the system inoperability in order to determine its evolution. Finally, in the third step, a method for calculating the remaining useful life of the system, based on its configuration, is formulated. The proposed methodology is applied on data obtained from the Tennessee Eastman Process simulations to predict the shutdown due to violation of process constraints.

Fault diagnosis and prognosis (FDP) are important capabilities that can enable autonomous detection and prediction of failures' progress in complex engineering systems. This paper introduces an innovative modular FDP method for a hydro-control valve system. The hydro-control valve is a critical part of the space launch vehicle propulsion system, and health monitoring of this hydro-valve is essential to ensure safety and reliability of the spacecraft. In this study, three main failures, i.e., piston leakage, drain blockage, and filter malfunction, in the hydro-control valve system are considered for monitoring and prognosis. The proposed FDP system has three main components including fault detection and diagnosis (FDD) unit, failure parameter estimation unit, and remaining useful life (RUL) estimation unit. A feature selection strategy and a support vector machine technique are together utilized to capture redundancy in multisensor data information and to isolate failures in the FDD unit. Then, a decentralized network of three adaptive neuro-fuzzy inference systems (ANFIS) is developed to estimate the failure parameters. Afterward, the RUL unit is constructed using an adaptive Bayesian algorithm. Finally, a performance measure, called the relative accuracy index, is introduced and applied to evaluate the performance of the proposed health monitoring system. Simulation studies confirm the effective performance of the proposed design methodology.

Manufacturers of lithium-ion batteries inform capacity degradation for regular, symmetrical charge/discharge cycles, which is clearly problematic in real life applications where charge/discharge cycles are hardly regular. In this context, this paper presents a methodology that can model the degradation of lithium-ion batteries when these are charged and discharged erratically. The proposed methodology can model degradation of a lithium-ion battery type subject to erratic charge/discharge cycles where degradation data under symmetrical charge/discharge cycles (namely, under a standard protocol) has been provided by the manufacturer. To do so we use the concepts of (i) SOC swing, (ii) average swing range and (iii) Coulombic efficiency to model the degradation process in a simple manner through interpolation techniques. We use both deterministic and Monte Carlo simulations to obtain capacity degradation as a function of the number of cycles.

Science fiction had anticipated the prediction of future occurrence of crimes. In fact, that prediction is actually possible. It can be done with some imprecision and by computer algorithms using available data from various sources. The prediction involves approximate time and risk maps of occurrence of certain type of felonies such as home burglaries, armed robberies and violent thefts. The police can then use this information for increasing their patrolling accordingly and thereby reducing the crime occurrence rate. We present a crime prediction solution developed for Chilean large cities. Its novel approach includes three independent software modules which make predictions based on different algorithms. The final prediction is the cooperative integration of the individual ones. The developed system has been tested on historical data and its performance has been considered acceptable for police field use. An interesting result is that the performance of each individual module is inferior to the joint performance, validating a hypothesis that different algorithms may exploit different features of the available data.

In robotic mapping and simultaneous localization and mapping, the ability to assess the quality of estimated maps is crucial. While concepts exist for quantifying the error in the estimated trajectory of a robot, or a subset of the estimated feature locations, the difference between all current estimated and ground-truth features is rarely considered jointly. In contrast to many current methods, this paper analyzes metrics, which automatically evaluate maps based on their joint detection and description uncertainty. In the tracking literature, the optimal subpattern assignment (OSPA) metric provided a solution to the problem of assessing target tracking algorithms and has recently been applied to the assessment of robotic maps. Despite its advantages over other metrics, the OSPA metric can saturate to a limiting value irrespective of the cardinality errors and it penalizes missed detections and false alarms in an unequal manner. This paper therefore introduces the cardinalized optimal linear assignment (COLA) metric, as a complement to the OSPA metric, for feature map evaluation. Their combination is shown to provide a robust solution for the evaluation of map estimation errors in an intuitive manner.

This paper analyses and compares the performance of a number of approaches implemented for the detection of capacity regeneration phenomena (measured in ampere-hours) in the degradation trend of energy storage devices, particularly Lithium-Ion battery cells. All implemented approaches are based on a combination of information-theoretic measures and sequential Monte Carlo methods for state estimation in nonlinear, non-Gaussian dynamic systems. Properties of information measures are conveniently used to quantify the impact of process measurements on the posterior probability density function of the state, assuming that sub-optimal Bayesian estimation algorithms (such as classic or risk-sensitive particle filters) are to be used to obtain an empirical representation of the system uncertainty. The proposed anomaly detection strategies are tested and evaluated both in terms of (i) detection time (early detection) and (ii) false alarm rates. Verification of detection schemes is performed using simulated data for battery State-Of-Health accelerated degradation tests, to ensure absolute knowledge on the time instant where a regeneration phenomenon occurs.

We present the implementation of a particle-filtering-based prognostic framework that utilizes statistical characterization of use profiles to (i) estimate the state-of-charge (SOC), and (ii) predict the discharge time of energy storage devices (lithium-ion batteries). The proposed approach uses a novel empirical state-space model, inspired by battery phenomenology, and particle-filtering algorithms to estimate SOC and other unknown model parameters in real-time. The adaptation mechanism used during the filtering stage improves the convergence of the state estimate, and provides adequate initial conditions for the prognosis stage. SOC prognosis is implemented using a particle-filtering-based framework that considers a statistical characterization of uncertainty for future discharge profiles based on maximum likelihood estimates of transition probabilities for a two-state Markov chain. All algorithms have been trained and validated using experimental data acquired from one Li-Ion 26650 and two Li-Ion 18650 cells, and considering different operating conditions.

A class of stochastic volatility models (SVMs) with time-varying parameters is presented for online volatility estimation in nonstationary environments. This is achieved by modelling both the volatility and model parameters as states of a hidden Markov model (HMM), thus allowing for the use of particle filters to estimate the resulting posterior densities. The proposed models, based on the logarithmic SVM and the unobserved GARCH model, are evaluated for the estimation of the volatility of the NASDAQ-C and the Chilean IGPA financial indices between June 2007 and January 2010, where the late-2000s financial crisis is included. Simulations show that the proposed time-varying models are well suited for online volatility estimation as (i) they achieve an accuracy comparable to those of offline (batch) algorithms, and (ii) their parameters can be used to identify market changes.