Search Results (57)

Ensuring the reliability and robustness of spacecraft systems remains a key challenge, particularly given the limited feasibility of continuous real-time monitoring during on-orbit operations. In the domain of Fault Detection, Isolation, and Recovery (FDIR), no universal strategy has yet emerged. Traditional approaches often rely on precise, model-based methods executed onboard. This study explores data-driven alternatives for self-diagnosis and fault detection using Machine Learning techniques, focusing on spacecraft Guidance, Navigation, and Control (GNC) subsystems. A high-fidelity functional engineering simulator is employed to generate realistic datasets from typical onboard signals, including sensor and actuator outputs. Fault scenarios are defined based on potential failures in these elements, guiding the data-driven feature extraction and labeling process. Supervised learning algorithms, including Support Vector Machines (SVMs) and Artificial Neural Networks (ANNs), are implemented and benchmarked against a simple threshold-based detection method. Comparative analysis across multiple failure conditions highlights the strengths and limitations of the proposed strategies. Results indicate that Machine Learning techniques are best applied not as replacements for classical methods, but as complementary tools that enhance robustness through higher-level self-diagnostic capabilities. This synergy enables more autonomous and reliable fault management in spacecraft systems. Full article

Combine harvesters, as essential equipment in agricultural engineering, frequently experience structural faults due to their complex structure and harsh working conditions, which severely affect their reliability and operational efficiency, leading to significant downtime and reduced agricultural productivity during critical harvesting periods. Therefore, developing accurate and timely Fault Detection and Diagnosis (FDD) techniques is crucial for ensuring food security. This paper provides a systematic and critical review and analysis of the latest advancements in research on data-driven FDD methods for structural faults in combine harvesters. First, it outlines the typical structural sections of combine harvesters and their common structural fault types. Subsequently, it details the core steps of data-driven methods, including the acquisition of operational data from various sensors (e.g., vibration, acoustic, strain), signal preprocessing methods, signal processing and feature extraction techniques covering time-domain, frequency-domain, time–frequency domain combination, and modal analysis among others, and the use of machine learning and artificial intelligence models for fault pattern learning and diagnosis. Furthermore, it explores the required system and technical support for implementing such data-driven FDD methods, such as the applications of on-board diagnostic units, remote monitoring platforms, and simulation modeling. It provides an in-depth analysis of the key challenges currently encountered in this field, including difficulties in data acquisition, signal complexity, and insufficient model robustness, and consequently proposes future research directions, aiming to provide insights for the development of intelligent maintenance and efficient and reliable operation of combine harvesters and other complex agricultural machinery. Full article

Motor vehicles emit a large amount of air pollutants. Inspection and Maintenance (I/M) systems serve as a pivotal strategy for mitigating emissions from operational diesel trucks. However, the prevalent issue of blind repairs persists due to insufficient diagnostic capabilities at maintenance stations (M stations). To address this challenge, a multi-source information fusion methodology is proposed, integrating load deceleration testing from inspection stations (I stations), on-board diagnostics (OBD) data, and manual measurements at M stations. Critical diagnostic parameters—including nitrogen oxides (NOx) and particulate matter (PM) emissions, the ratio of measured wheel-side power to rated power, intake volume, common rail pressure, and exhaust back pressure—are systematically selected through statistical analysis and expert evaluations. An adaptive membership function is developed to resolve ambiguities in emission thresholds, enabling the construction of a robust fault diagnosis framework. Validation using 800 National V diesel truck maintenance records from a provincial automotive electronic health platform (2022 data) demonstrates a diagnostic accuracy of 92.8% for 153 emission-exceeding vehicles, surpassing traditional machine learning approaches by over 20%. By minimizing unnecessary repairs and optimizing maintenance efficiency, this approach significantly reduces resource waste and the lifecycle environmental footprints of diesel fleets. The proposed fuzzy-logic-based model effectively detects latent faults during routine maintenance, directly contributing to sustainable transportation through reductions in NOx and PM emissions—critical for improving air quality and advancing global climate objectives. This establishes a scalable technical framework for the effective implementation of I/M systems in alignment with sustainable urban mobility policies. Full article

The health monitoring system has been the main technological approach to extending the life of gas turbine engines and reducing maintenance costs resulting from performance degradation caused by asymmetric factors like carbon deposition, damage, or deformation. One of the most critical techniques within the health monitoring system is performance degradation prediction. At present, most research on degradation prediction is carried out using NASA’s open dataset, C-MAPSS, without considering that monitoring measurements are not always available, as in the ideal dataset. This limitation makes fault diagnosis algorithms and remaining useful life prediction methods difficult to apply to real gas turbine engines. Therefore, to solve the problem of performance degradation prediction in light-duty gas turbine engines, a prediction diagram is proposed based on Long Short-Term Memory (LSTM). Various types of onboard signals are taken into consideration among the experimental data. Only accumulated usage time, total temperature and total pressure before the inlet, low-pressure rotor speed, high-pressure rotor speed, fuel flow rate, exhaust temperature, and thrust are used in the training process, which is indispensable for an aero-engine. A genetic algorithm (GA) is introduced into the training process to optimize the hyperparameters of LSTM. The performance degradation prediction modeled with the GA-LSTM method is validated using experimental data. The maximum prediction error of thrust is 70 daN, and the mean absolute percentage error (MAPE) is less than 0.04. This study provides a practical approach to implementing performance degradation prediction in health monitoring systems to improve gas turbine engine reliability, economy, and environmental performance. Full article

A battery-operated biomedical wearable device gradually assists in clinical tasks to monitor patients’ health states regarding early diagnosis and detection. This paper presents the development of a self-powered portable electronic module by integrating an onboard energy-harvesting facility for electrocardiogram (ECG) signal processing and personalized health monitoring. The developed electronic module provides a customizable approach to power the device using a lithium-ion battery, a series of silicon photodiode arrays, and a solar panel. The new architecture and techniques offered by the developed method include an analog front-end unit, a signal processing unit, and a battery management unit for the acquiring and processing of real-time ECG signals. The dynamic multi-level wavelet packet decomposition framework has been used and applied to an ECG signal to extract the desired features by removing overlapped and repeated samples from an ECG signal. Further, a random forest with deep decision tree (RFDDT) architecture has been designed for offline ECG signal classification, and experimental results provide the highest accuracy of 99.72%. One assesses the custom-developed sensor by comparing its data with those of conventional biosensors. The onboard energy-harvesting and battery management circuits are designed with a BQ25505 microprocessor with the support of silicon photodiodes and solar cells which detect the ambient light variations and provide a maximum of 4.2 V supply to enable the continuous operation of an entire module. The measurements conducted on each unit of the proposed method demonstrate that the proposed signal-processing method significantly reduces the overlapping samples from the raw ECG data and the timing requirement criteria for personalized and wearable health monitoring. Also, it improves temporal requirements for ECG data processing while achieving excellent classification performance at a low computing cost. Full article

Motor vehicles emit a large amount of air pollutants. NOx and particulate matter (PM) account for 53.2% and 74.7%, respectively, of vehicle emissions in China. Using the technical guidelines for compiling road vehicle emission inventories, the emission factors based on the onboard diagnostics (OBD) system of heavy-duty diesel vehicles are obtained. The trajectory of heavy-duty diesel vehicles is corrected using big data interpolation, and the correction coefficients for different vehicle speeds are fitted to calculate the corresponding correction factors. Simultaneously, the Weather Research and Forecasting model is used for the meteorological correction of emissions, a heavy-duty diesel vehicle emission inventory under the community multiscale air quality model is established, and the distribution characteristics of pollution emissions from heavy-duty diesel vehicles in Chengdu are analyzed at the time and space levels. Overall, the pollutant gasses emitted by heavy-duty diesel vehicles in Chengdu are largely concentrated at the city center. In 2023, the total annual emissions of the pollutants NOx, CO, fine PM, and volatile organic compounds from heavy-duty diesel vehicles in Chengdu were 10,590.60, 28,852.90, 686.18, and 657.60 tons, respectively. NOx and CO have the highest proportions among the major pollutants, accounting for 70.7% and 26%, respectively. Full article

Maritime transport has adapted to recent political and economic shifts by addressing stringent pollution reduction requirements, redrawing transport routes for safety, reducing onboard technical incidents, managing data security risks and transitioning to autonomous vessels. This paper presents a novel approach to predictive maintenance in the maritime industry, leveraging Artificial Intelligence (AI) and Machine Learning (ML) techniques to enhance fault detection and maintenance planning for naval systems. Traditional maintenance strategies, such as corrective and preventive maintenance, are increasingly ineffective in meeting the high safety and efficiency standards required by maritime operations. The proposed model integrates AI-driven methods to process operational data from shipboard systems, enabling more accurate fault diagnosis and early identification of system failures. By analyzing historical operational data, ML algorithms identify patterns and estimate the functional states, helping prevent unplanned failures and costly downtime. This approach is critical in environments where technical failures are a leading cause of incidents, as demonstrated by the high rate of machinery-related accidents in maritime operations. Our study highlights the growing importance of AI and ML in predictive maintenance and offers a practical tool for improving operational safety and efficiency in the naval industry. The paper discusses the development of a fault detection approach, evaluates its performance on real shipboard data-through tests on a seawater cooling system from an oil tanker and concludes with insights into the broader implications of AI-driven maintenance in the maritime sector. Full article

This study reassesses an overview of the potential of the radio frequency (RF)-based state diagnostics of three-way catalysts (TWC) based on a previous study with an emphasis on the defect chemistry of the catalyst material during reoxidation and reduction. Some data are based on the previous works but are newly processed, and the signal parameters resonant frequency and inverse quality factor are evaluated with respect to applicability. The RF-based method uses electromagnetic resonances in a cavity resonator to provide information on the storage level of the oxygen storage component. The analysis focuses on a holistic investigation and evaluation of the major effects influencing the RF signal during operation. On the one hand, the response to the oxygen storage behavior and the resolution of the measurement method are considered. Therefore, this study merges original data from multiple former publications to provide a comprehensive insight into important measurement effects and their defect chemistry background. On the other hand, the most important cross-sensitivities are discussed and their impact during operation is evaluated. Additionally, the effect of catalyst aging is analyzed. The effects are presented separately for the two resonant parameters: resonant frequency and (unloaded) quality factor. Overall, the data suggest that the quality factor has a way higher signal quality at low temperatures (<400 °C) and the resonant frequency is primarily suitable for high operating temperatures. At most operating points, the quality factor is even more robust against interferences such as exhaust gas stoichiometry and water content. Correctly estimating the catalyst temperature is the most important factor for reliable results, which can be achieved by combining the information of both resonant signals. In the end, the data indicate that microwave-based state diagnosis is a powerful system for evaluating the oxygen storage level over the entire operating range of a TWC. As a research tool and in its application, the system can therefore contribute to the improvement of the emission control of future gasoline vehicles. Full article

One of the main challenges to be faced in deep space missions is to protect the health and ensure the maximum efficiency of the crew by preparing methods of prevention and in situ diagnosis. Indeed, the hostile environment causes important health problems, ranging from muscle atrophy, osteopenia, and immunological and metabolic alterations due to microgravity, to an increased risk of cancer caused by exposure to radiation. It is, therefore, necessary to provide new methods for the real-time measurement of biomarkers suitable for deepening our knowledge of the effects of space flight on the balance of the immune system and for allowing the monitoring of the astronaut’s health during long-term missions. APHRODITE will enable human space exploration because it fills this void that affects both missions in LEO and future missions to the Moon and Mars. Its scientific objectives are the design, production, testing, and in-orbit demonstration of a compact, reusable, and reconfigurable system for performing the real-time analysis of oral fluid samples in manned space missions. In the frame of this project, a crew member onboard the ISS will employ APHRODITE to measure the selected target analytes, cortisol, and dehydroepiandrosterone sulfate (DHEA-S), in oral fluid, in four (plus one additional desired session) separate experiment sessions. The paper addresses the design of the main subsystems of the analytical device and the preliminary results obtained during the first implementations of the device subsystems and testing measurements on Earth. In particular, the system design and the experiment data output of the lab-on-chip photosensors and of the front-end readout electronics are reported in detail along with preliminary chemical tests for the duplex competitive CL-immunoassay for the simultaneous detection of cortisol and DHEA-S. Different applications also on Earth are envisaged for the APHRODITE device, as it will be suitable for point-of-care testing applications (e.g., emergency medicine, bioterrorism, diagnostics in developing countries, etc.). Full article

Highway bridges stand as paramount elements within transportation infrastructure systems. The ability to ensure swift recovery after extreme events, such as earthquakes, is a fundamental trait of resilient communities. Consequently, expediting the recovery process necessitates near real-time diagnosis of structural damage to provide dependable information. In this study, a data-driven approach for damage detection and assessment is investigated, focusing on bridge columns—the pivotal supporting elements of bridge systems—based on simulations derived from nonlinear time history analysis. This research introduces a set of cumulative intensity-based damage features, whose efficacy is demonstrated through unsupervised learning techniques. Leveraging the support vector machine, a prominent pattern recognition algorithm in supervised learning, alongside Bayesian optimization with a Gaussian process, seismic damage detection and assessment are explored. Encouragingly, the methodology yields high estimation accuracies for both binary outcomes (indicating the presence of damage or the occurrence of collapse) and multi-class classifications (indicating the severity of damage). This breakthrough opens avenues for the practical implementation of on-board sensor computing, enabling near real-time damage detection and assessment in bridge structures. Full article

The transport sector is tackling the challenge of reducing vehicle pollutant emissions and carbon footprints by means of a shift to electrified powertrains, i.e., battery electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs). However, electrified vehicles pose new issues associated with the design and energy management for the efficient use of onboard energy storage systems (ESSs). Thus, strong attention should be devoted to ensuring the safety and efficient operation of the ESSs. In this framework, a dedicated battery management system (BMS) is required to contemporaneously optimize the battery’s state of charge (SoC) and to increase the battery’s lifespan through tight control of its state of health (SoH). Despite the advancements in the modern onboard BMS, more detailed data-driven algorithms for SoC, SoH, and fault diagnosis cannot be implemented due to limited computing capabilities. To overcome such limitations, the conceptualization and/or implementation of BMS in-cloud applications are under investigation. The present study hence aims to produce a new and comprehensive review of the advancements in battery management solutions in terms of functionality, usability, and drawbacks, with specific attention to cloud-based BMS solutions as well as SoC and SoH prediction and estimation. Current gaps and challenges are addressed considering V2X connectivity to fully exploit the latest cloud-based solutions. Full article

This article aims to assess, discuss and analyze the disturbances caused by electromagnetic field (EMF) noise of medical devices used near living tissues, as well as the corresponding functional control via the electromagnetic compatibility (EMC) of these devices. These are minimally invasive and non-ionizing devices allowing various healthcare actions involving monitoring, assistance, diagnoses and image-guided medical interventions. Following an introduction of the main items of the paper, the different imaging methodologies are conferred, accounting for their nature, functioning, employment condition and patient comfort and safety. Then the magnetic resonance imaging (MRI) components and their fields, the consequential MRI-compatibility concept and possible image artifacts are detailed and analyzed. Next, the MRI-assisted robotic treatments, the possible robotic external matter introductions in the MRI scaffold, the features of MRI-compatible materials and the conformity control of such compatibility are analyzed and conferred. Afterward, the embedded, wearable and detachable medical devices, their EMF perturbation control and their necessary protection via shielding technologies are presented and analyzed. Then, the EMC control procedure, the EMF governing equations and the body numerical virtual models are conferred and reviewed. A qualitative methodology, case study and simple example illustrating the mentioned methodology are presented. The last section of the paper discusses potential details and expansions of the different notions conferred in the paper, in the perspective of monitoring the disturbances due to EMF noise of medical devices working near living tissues. This contribution highlights the possibility of the proper functioning of medical instruments working close to the patient’s body tissues and their protection by monitoring possible disturbances. Thanks to these commitments, various health recommendations have been taken into account. This concerns piezoelectric actuated robotics, assisted with MRI and the possible use of conductive materials in this imager under certain conditions. The safe use of onboard devices with EMF-insensitive or intelligently shielded materials with short exposure intervals is also of concern. Additionally, the need to monitor body temperature in case of prolonged exposure of onboard devices to EMF is analyzed in the Discussion section. Moreover, the use of virtual tissue models in EMC testing to achieve more realistic evaluation capabilities also features in the Discussion section. Full article

Increasing the efficiency and reliability of modern railway transport is accompanied by an increase in monitoring and diagnostic systems for the current state of electric drives. Modern railway transport contains a large number of induction motors to ensure the operation of the drives of various mechanisms. In the article, based on the operational statistics of engine failures and the proposed scheme for diagnosing them, studies were carried out and a model was developed for assessing the reliability of a transport system equipped with an on-board diagnostic system for the current state. When building the models, the Markov method was used, including the construction of graphs for the five most relevant states of the induction electric motor during operation. The results obtained are relevant for evaluating the effectiveness of using the built-in diagnostic system and scheduling routine maintenance, which will affect the efficiency of railway transport. Based on the process of the diagnosis of railway transport systems with induction motors, five operating states of the object studied were interpreted: the state of full operation, state “S0”; the state of incomplete serviceability, state “S1”; critical serviceability, state “S2”; the state of the pre-damage condition, state “S3”; the state of unserviceability (defect), state “S4”. Subsequently, a five-state model of the operation process of railway transport systems with induction motors was developed. This model is also described by equations of state: Kolmogorov–Chapman equations. The reliability quantities determined form the basis for simulation reliability studies. The effect of the simulation study is the reliability quantities determined in the form of reliability functions and probabilities of the occurrences of the operating states of railway transport systems with induction motors; an important part of the reliability study of the system examined is to estimate the times of the occurrences in the object studied of the operating states in the future. Full article

The performance of modern, new generation-armored vehicles would greatly benefit from overall engineering, optimization, and integration techniques of advanced diesel engines-electrified transmissions. Modern axial flux electric motors and controllers are perfectly able to replace the classical automatic gearbox and complex steering system of traditional Main Battle Tanks. This study shows a possible design of a serial hybrid electric power pack for very heavy tanks with a weight well over 50 tons. The result is a hybrid power system that improves the overall performance of armored vehicles off-road and on-road, improving the acceleration and the smoothness of the ride. In addition, fuel consumption will be reduced because the internal combustion engine operates at fixed rpm. The electric motors will outperform the traditional engines due to their very high torque output even at “zero speed”. The weight of a hybrid system has also been calculated. In fact, in many cases, it is possible to use all off-the-shelf components. The on-board diagnosis of the subsystems in the hybrid powertrain makes it possible to achieve a Time Between Overhaul (TBO) of 4500 h with a failure probability inferior to one in 10,000. Full article

The growing trend of onboard computational autonomy has increased the need for self-reliant rovers (SRRs) with high efficiency for unmanned rover activities. Mobility is directly associated with a successful execution mission, thus fault response for actuator failures is highly crucial for planetary exploration rovers in such a trend. However, most of the existing mobility health management systems for rovers have focused on fault diagnosis and protection sequences that are determined by human operators through ground-in-the-loop solutions. This paper presents a special four-wheeled two-steering lunar rover with a modified explicit steering mechanism, where each left and right wheel is controlled by only two actuators. Under these constraints, a new motion planning method that combines reinforcement learning with the rover’s kinematic model without the need for dynamics modeling is devised. A failure-safe algorithm is proposed to address the critical loss of mobility in the case of steering motor failure, by expanding the devised motion planning method, which is designed to ensure mobility for mission execution in a four-wheeled rover. The algorithm’s performance and applicability are validated through simulations on high-slip terrain scenarios caused by steering motor failure and compared with a conventional control method in terms of reliability. This simulation validation serves as a preliminary study toward future works on deformable terrain such as rough or soft areas and optimization of the deep neural network’s weight factor for fine-tuning in real experiments. The failure-safe motion planning provides valuable insights as a first-step approach toward developing autonomous recovery strategies for rover mobility. Full article