In recent years, lithium-ion batteries have gained significant attention due to their crucial role in various applications, such as electric vehicles and renewable energy storage. Accurate prediction of the remaining useful life (RUL) of these batteries is essential for optimizing their performance and ensuring reliable operation. In this paper, we propose a novel methodology for RUL prediction using an individual control chart (ICC) to identify and remove degraded data, a convolutional neural network (CNN) to smooth the noise of sensor data and long short-term memory (LSTM) networks to effectively capture both spatial and temporal dependencies within battery data, enabling accurate RUL estimation. We evaluate our proposed model using a comprehensive dataset, and experimental results demonstrate its superior performance compared to existing methods. Our findings highlight the potential of ICC-CNN-LSTM for RUL prediction in lithium-ion batteries and provide valuable insights for future research.

In the field of sustainable mobility, this study highlights the importance of using machine learning for predictive modeling based on real traffic data collected from instrumented bicycles. The advent of advanced technologies like sustainable mobility apps, sensors, and advanced data analysis methods led to the ability to collect data from various sources, which enabled researchers to estimate battery state of charge (SOC) accurately. Most current research uses them in the lab experiments for data collection. In this work, we use real-time sensors data to construct data-driven models for lithium-ion battery SOC estimation. This research integrates both electric bicycle battery, environmental and route variables to achieve the following goals: (1) Collect a multimodal data set including operational, topography, vehicle, and external variables, (2) Preprocess data obtained from sensors installed on the electric bicycle battery, (3) Create models of lithium-ion battery SOC based on electric bicycle battery and environmental variables, and (4) Assess data-driven models and compare their performance for lithium-ion battery SOC with high accuracy. To achieve that, we conducted a real study to predict the Remaining Useful Life (RUL), as a measure of state of charge, of electric bicycle battery. The study was carried out on a 15 km cycle route in Medellín, Colombia, for 28 days. To estimate the RUL, we used four different machine learning algorithms: Long Short-Term Memory (LSTM), Support Vector Regression (SVR), AdaBoost, and Gradient Boost. Notably, data preprocessing techniques played a pivotal role, with a particular focus on smoothing sensor data using Convolutional Neural Networks (CNN). The results showed a significant improvement in prediction accuracy when using data preprocessing, confirming its importance in improving model performance. Furthermore, the comparison of network performance facilitated the selection of the most effective model for the test data. This study underscores the value of using real-world data to develop and validate predictive models in the pursuit of sustainable mobility solutions, and highlights the critical role of data-driven methodologies in addressing today’s urban transportation challenges.

Purpose: This paper aims to determine the stage of the life cycle where the trucks of a waste collection fleet from a Colombian city are located through a reliability approach. The reliability analysis and the evaluation of curve of operational costs allow to know the moment in which it is necessary to make decisions regarding an asset, its maintenance or possible replacement.Design/methodology/approach: For a dataset presented as maintenance work orders, the time to failures (TTFs) for each vehicle in the fleet were calculated. Then, a probability density function for those TTFs was fitted to locate each vehicle in a region of the bathtub curve and to calculate the reliability of the whole fleet. A general functional analysis was also developed to understand the function of the vehicles.Findings: It was possible to determine that the largest proportion of the fleet was in the final stage of the life cycle, in this sense, the entire fleet represent critical assets which in most of cases could be worth replacement or overhaul.Originality/value: In this study, an address is exposed for the identification of critical equipment by reliability and statistical analysis. This analysis is also integrated with the maintenance management process. This is a broadly interested topic since it allows to support the maintenance and operational decision-making process, indicating the focus of resource allocation all over the entire asset life cycle.

Uncertainty is a key factor in the reliability assessment of complex engineering systems, especially when they operate under variable conditions that affect component degradation. This study presents a framework for the systematic and uncertainty-based prioritization of critical components and failure modes. The method combines Reliability Block Diagrams, Fault Tree Analysis, and Importance Measures with Failure Mode and Effects Analysis. This two-level approach links component failures with their effect on system reliability. Uncertainty is introduced through the statistical parameters of component reliability distributions and the resulting impact on system behavior is examined. Components with the highest importance are then examined through Failure Mode and Effects Analysis to identify main failure modes and calculate their Risk Priority Numbers. The framework is applied to a fleet of Solid Waste Collection and Compaction Trucks used by a waste management company in a Colombian city. This system operates under high-load variability, mechanical shocks, and environmental stress. The combined Importance Measures and Risk Priority Number analysis provides a probabilistic basis for identifying critical components and their dominant failure modes, linking reliability uncertainty with maintenance prioritization. The results show that combining Importance Measures and Risk Priority Number improves the identification of critical components and dominant failure modes, supporting maintenance prioritization based on reliability impact. The framework offers a practical approach for reliability assessment and maintenance planning under uncertainty, linking component-level uncertainty with system performance to guide decision-making in complex systems.

Este trabajo presenta el desarrollo de un sistema de valoración cuantitativa de la capacidad de los inspectores para tomar decisiones acertadas, usando el método de análisis de riesgos y el índice Kappa de Cohen. En este artículo se establece un procedimiento para la aplicación del método de análisis de riesgos valorando cuantitativamente el desempeño de inspección visual. Para medir el impacto de diferentes escenarios con variaciones en el nivel de capacitación, experiencia, unificación de criterios, características físicas, métodos y estándares disponibles, se realiza un estudio de simulación, para diferentes inspectores en tres escenarios. Los resultados permitieron obtener niveles de calidad del proceso entre 95% y 99%. Es posible evidenciar que reducir la variabilidad del resultado de los procesos de inspección mediante la estandarización de métodos, unificación de criterios y capacitación del personal mejora el nivel de calidad. El procedimiento presentado y simulado en este artículo puede ser replicable en procesos de inspección cualitativos ya sea procesos de manufactura o de servicios.