bluuebunny/crossref_metadata_2025 huggingface dataset:
Con el oído puesto en el rumor de los ahogos. A propósito de los pasajes y sus fronteras
Authors: Rodríguez Susana
Published: March 2024

“Relato”, de Eduardo Atilio Romano, se constituye en umbral y pasaje en el poemario Estrecho mar (2006), que el escritor nacidoen San Ramón de la Nueva Orán (Salta, Argentina) produjo como resultado de su exilio en Nerja (Málaga, España). La experiencia del viaje —realizado por las dificultades económicas (ya no políticas) que obligaban a los jóvenes a una forma de exilio cuyas características serían motivo de otro ensayo— se condensa y presentifica en las imágenes de los africanos que intentan, a riesgo de su vida, cruzar el Estrecho de Gibraltar (pasaje al umbral de un mundo deseado: el europeo) a fin de, vaya paradoja, sobrevivir.La reflexión sobre la función que cumple el poema aludido, en relación con el resto de los poemas del libro y con la experiencia de vida del poeta, nos invita a pensar en la figura del pasaje como una doble instancia semiótica: lugar de manifestación de acciones, y umbral, zona fronteriza que es tránsito a un nuevo estado. Los fundamentos teóricos se encuentran en la concepciódel texto (Yuri Lotman) como dispositivo de memoria inteligente que permite captar su “espacio” (Michel de Certeau) como la escena en la que se inscribe el pasaje a otro mundo y a los otros. Así concebido, “Relato” es un texto palimpsesto (Silvia Barei) que se escribe sobre textos anteriores, re-escritura del pasaje ritual del nacimiento y del viaje.
Kafka a través de Job, Job a través de Kafka
Authors: Cabrera Isabel
Published: March 2024

Siguiendo la idea de Borges de que cada autor crea sus precursores, este artículo pretende leer parte de la obra de Kafka a través del Libro de Job e imaginar una suerte de diálogo entre ambos, para mostrar que usar de trasfondo el texto bíblico de Job permite afinar y relacionar, dentro de su propio contexto literario, algunas imágenes centrales de Kafka. Pero a diferencia de otras lecturas que tienen como propósito encontrar en Kafka cierta religiosidad similar a la que está presente en el texto bíblico de Job, este texto pugna por lo contrario, es Kafka quien nos permite ver la incipiente irreligiosidad del Libro de Job.
Fondo Greimas
Authors: Solís Zepeda María Luisa , Vázquez Rodríguez Blanca Alberta , Flores Ortiz Roberto , Blanco Desiderio
Published: March 2024

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درجة تقويم أثر وسائل التواصل الاجتماعي على الدعوة الإسلامية من وجهة نظر طلبة كلية الشريعة الجامعة الأردنية
Authors: Borqan Ibrahim Mohammad Khalid
Published: March 2024

الأهداف: هدفت الدّراسة التّعرّف إلى درجة تقويم أثر وسائل التّواصل الاجتماعيّ على الدّعوة والتّحديات المتّصلة بها من وجهة نظر طلبة كليّة الشّريعة في الجامعة الأردنيّة.
المنهجية: اتبعت الدراسة المنهج الوصفي المسحي، حيث تكوّنت عيّنة الدّراسة من (286) طالبًا وطالبة من كليّة الشّريعة، وتم اختيارهم بصورة عشوائية. واستخدمت الدّراسة أداة الاستبانة لجمع البيانات بعد التّحقق من صدقها وثباتها حسب الأصول، وتكونت من بعدين؛ الأول يُعنى بقياس درجة تقويم أثر وسائل التواصل الاجتماعي على الدعوة من وجهة نظر طلبة كليّة الشّريعة في الجامعة الأردنيّة، وأمّا الثاني فعُني بقياس درجة تقويم تحديات الدعوة على مواقع التواصل الاجتماعي من وجهة نظر مجتمع الدراسة ذاته. تم التحليل الإحصائي باستخدام المتوسطات الحسابيّة والانحرافات المعياريّة لمعرفة التمايز بين الفقرات بناء على مقياس ليكرت.
النتائج: كشفت الدّراسة عن نتائج "مرتفعة" في كلّ من بعدي الاستبانة، فأشارت إلى وعي مجتمع الدّراسة بأهميّة الدّعوة عبر وسائل التّواصل الاجتماعيّ من جهة، والتّحديات التي فرضتها هذه الوسائل في مجال الدّعوة الإسلاميّة من جهة أخرى. كما أوضحت الدّراسة وجود فروق ذات دلالة إحصائيّة عند مستوى (0.05>α) تبعًا لمتغيّر الجنس في البعد الأول الخاصّ بالآثار لصالح الإناث، وعدم وجود فروق ذات دلالة إحصائيّة عند مستوى (0.05>α) تبعا لمتغيري الجنس والتّخصص في البعد الثاني الخاصّ بالتّحديات.
الخلاصة: توصي الدراسة بضرورة الاهتمام بمتابعة أهم التحديات الدعوية على منصات التواصل الاجتماعي، منها خطورة تصدي غير المؤهلين للدعوة عبر هذه المنصات، ودراسة آثار ذلك من جوانب عديدة معرفية واجتماعيّة. بالإضافة إلى ضرورة الاهتمام بالخطاب الديني الموجّه للمرأة على منصات التواصل الاجتماعي، وتعزيزه، وإثرائه بالمعلومة الدقيقة والمفيدة.
ماذا خسر العالم بانحطاط المُفكِّرين
Authors: هيئة التحرير
Published: March 2024

قد يذكّرنا عنوان كلمة التحرير بعنوان معروف جداً في الأوساط الثقافية والإصلاحية، وهو مؤلَّف الندوي "ماذا خسر العالم بانحطاط المسلمين"، الذي صدر عام 1944م. وهو الكتاب الذي قلب فيه معادلة الـمُؤثِّر والمتأثِّر (الغرب والمسلمين)، وطرح فيه أبو الحسن الندوي أسئلة مهمة منها: هل للمسلمين صلة وثيقة بالمصير الإنساني وبالأوضاع العالمية؟ وماذا سيربح العالم من تقدُّم المسلمين؟ وهل المسلمون حقّاً في وضع يمكن القول فيه: إن العالم قد خسر شيئاً بانحطاطهم؟ وهل المسلمون على مستوىً يجوز أن يُقال فيه: إن العالم قد خسر شيئاً بتقهقرهم وبتخلّفهم عن مجال القيادة العالمية؟. وعرض الندوي في كتابه دور الإسلام في حياة البشرية، وفي نقلها من النقيض إلى النقيض: دينياً واجتماعياً واقتصادياً وفكرياً إلخ، ليصل في النهاية إلى حاجة البشرية لقيادة حكيمة تستلهم هذا الدور الذي قامت به الحضارة الإسلامية؛ إذ إن خلوَّ البشرية من الإسهام الإسلامي وانحطاط المسلمين هو خسران للبشرية جمعاء لا للمسلمين وحدهم.
Zinc oxide (ZnO) nanowire by hydrothermal synthesis and its application to pH sensing
Authors: Jang Noah , Kim June Soo , Kim Hyunjun , Kim Da Ye , Wang Jiajie , Nam Yujin , Han Maeum , Kong Seong Ho
Published: March 2024

Recently, the importance of pH sensors in monitoring the pH of marine and water environments is growing. Although such sensors have been developed, existing fabrication methods have the challenge of requiring complex structures. Consequently, oxide-semiconductor pH sensors have been actively researched, with a particular focus on ZnO-based pH sensors that are economically viable and easy to manufacture and exhibit high sensitivity to subtle environmental changes. We propose an oxide-semiconductor pH sensor based on ZnO nanowires synthesized using a low-temperature hydrothermal method. The sensor is designed to have excellent sensitivity by achieving a high surface-to-volume ratio. ZnO nanostructure acted as a pH adsorption layer and the sensitivity by the nanostructure growth time of ZnO were evaluated. The nanostructure exhibited the highest sensitivity for a growth time 4 h, and response time and selectivity were also studied.
Visualization of oxygen vacancies at CeO
x
/Y-HZO interface by spectrum imaging method and multivariate analysis
Authors: Higashimine Koichi , Saito Mizuki , Mohit  , Tokumitsu Eisuke
Published: March 2024

Electron energy loss spectroscopy—spectrum imaging measurements using a scanning transmission electron microscope are carried out to clarify the details of microstructure at the interface of the CeO
x
-capped Y-HZO film prepared by the chemical solution deposition (CSD) method. We confirmed that by the present CSD the independent capped layer of CeOx successively deposited on Y-HZO. The crystal structure of CeO
x
film is mainly the cubic CeO2 structure with Ce4+. Chemical state maps are also successfully obtained by the multivariate analysis. We found that Ce3+ and Ce4+ coexist in the interface layer with cubic CeO2 crystal structure containing O vacancy. The results of the quantitative elemental distribution maps of energy dispersive X-ray spectroscopy also supported that O vacancies exist at the interface.
PHM Techniques for Condition-Based Maintenance Based on Hybrid System Model Representation
Authors: Maria García Claudia , Escobet Teresa , Quevedo Joseba
Published: March 2024

Condition-based maintenance (CBM) is a maintenance strategy that uses diagnosis and prognosis to determine system‟s health. The overall objective of this paper is design a real-time monitoring system for CBM, applied to a conveyor belt system, based on the integration of prognosis and health management technologies (PHM) and hybrid models. This work is focus on the prognosis part of PHM. A forecasting model based in Adaptive- Network-based Fuzzy Inference Systems (ANFIS) combined with a Gray-Scale Health Index (HI) is implemented to evaluate the system degradation. As shown throughout the paper, the hybrid model allows extracting the main features of the system that will be used in the prognostic algorithm. The obtained results show that the ANFIS prediction model linked to the degradation index HI can track the system degradation, thus have the potential for being used as a tool suitable for condition-based maintenance.
A State-Space Model for Vibration Based Prognostics
Authors: Bechhoefer Eric , Clark Steve , He David
Published: March 2024

Operation and maintenance of offshore wind farms will be more difficult and expensive than equivalent onshore wind farms. Accessibility for routine servicing and maintenance will be a concern: there may be times when the offshore wind farm is inaccessible due to sea, wind and visibility conditions. Additionally, maintenance tasks are more expensive than onshore due to distance of the wind farm from shore, site exposure, and the need for specialized lifting equipment to install and change out major components .As a result, the requirement for remote monitoring and condition based maintenance techniques becomes more important to maintain optimum turbine availability levels. The development of a prognostics health management (PHM) capability will allow a strategy that balances risk of running the turbine against lost revenue. Prognostics would give an estimate of the remaining useful life of a component under various loads, thus avoiding component failure. We present a state-space model for predicting the remaining useful life of a component based on vibration signatures. The model dynamics are explained and analysis is performed to evaluate the nature of fault signature distribution, and an indicator of prognostic confidence is proposed. The model is then validated under real world conditions.
Application of Oil Debris Monitoring For Wind Turbine Gearbox Prognostics and Health Management
Authors: Dupuis Richard
Published: March 2024

Experience has shown that premature gearbox failures are a leading maintenance cost driver that can easily lower the profit margin from a wind turbine operation. Prognostics and Health Management (PHM) techniques offer the potential of effectively managing gearbox health problems by detecting early damage, tracking the severity of damage, estimating the time to reach pre-defined damage limits, and providing key information for proactive maintenance decisions. Experience has revealed that major damage modes of wind turbine gearboxes are bearing spall and gear teeth pitting, both of which release metallic debris particles in the oil lubrication system. Oil debris monitoring is thus well suited to provide an early indication and quantification of internal damage to bearings and gears of a wind turbine gearbox.This paper reviews the application of oil debris monitoring as an effective PHM solution for wind turbine gearboxes. The paper describes the principle of operation of the oil debris monitoring technology and the principle of application for effective PHM of wind turbine gearboxes. The paper explains the common surface fatigue damage mode of bearing and gear rolling elements and the characteristics of the destructive debris that result from this damage mode. The paper outlines a simple means of deriving accumulated debris count damage limits based upon basic gearbox component geometry and the use of moving averages for estimating rates of debris generation as a simple yet effective damage data-driven propagation model. Finally, the application of oil debris monitoring as an effective PHM technology for wind turbine gearboxes is illustrated by presenting actual data obtained from seeded fault bearing and gear tests and fielded gearbox applications.
Advanced Vibration Sensing with Radar - ADVISER
Authors: Peczalski Andrzej Peczalski , Kim Kyusung , Mylaraswamy Dinkar
Published: March 2024

A new low-cost stand-off vibration sensor based on the Doppler radar principle is presented. The baseline performance of this sensor was compared with a high-quality accelerometer in a well-controlled laboratory environment. This advanced vibration imaging sensor (ADVISER) was also validated for its prognostic health monitoring ability with a fault emulator. The ADVISER was able to detect machine misbalance and bearing damage at a distance of 4 feet without making any contact. This exceeded the performance of a high-quality screw accelerometer mounted directly on the bearing enclosure. In this paper, we present the sensor’s principle of operation, summarize results of comparing it with standard accelerometers, and conclude with its potential use in industrial and aerospace applications.
Extracting Decision Trees from Diagnostic Bayesian Networks to Guide Test Selection
Authors: Wahl Scott , W. Sheppard John
Published: March 2024

In this paper, we present a comparison of five different approaches to extracting decision trees from diagnostic Bayesian nets, including an approach based on the dependency structure of the network itself. With this approach, attributes used in branching the decision tree are selected by a weighted information gain metric computed based upon an associated D-matrix. Using these trees, tests are recommended for setting evidence within the diagnostic Bayesian nets for use in a PHM application. We hypothesized that this approach would yield effective decision trees and test selection and greatly reduce the amount of evidence required for obtaining accurate classification with the associated Bayesian networks. The approach is compared against three alternatives to creating decision trees from probabilistic networks such as ID3 using a dataset forward sampled from the network, KL-divergence, and maximum expected utility. In addition, the effects of using χ2 statistics and probability measures for pre-pruning are examined. The results of our comparison indicate that our approach provides compact decision trees that lead to high accuracy classification with the Bayesian networks when compared to trees of similar size generated by the other methods, thus supporting our hypothesis.
Machine Remaining Useful Life Prediction Based on Adaptive Neuro-Fuzzy and High-Order Particle Filtering
Authors: Chen Chaochao , Vachtsevanos George , E. Orchard Marcos
Published: March 2024

Machine remaining useful life (RUL) prediction is a key part of Condition-Based Maintenance (CBM), which provides the time evolution of the fault indicator so that maintenance can be performed to avoid catastrophic failures. This paper proposes a new RUL prediction method based on adaptive neuro-fuzzy inference systems (ANFIS) and high-order particle filtering, which predicts the time evolution of the fault indicator and computes the probability density function (pdf) of RUL. The ANFIS is trained and integrated in a high-order particle filter to describe the fault propagation process; the high-order particle filter uses real-time data to update the current state estimates so as to improve the prediction accuracy. The performance of the proposed method is evaluated via the real-world data from a seeded fault test for a UH-60 helicopter planetary gear plate. The results show that it outperforms the conventional ANFIS predictor.
Condition Indicators for Gears
Authors: Klein Renata
Published: March 2024

Diagnostics of faults in gears requires development of reliable condition indicators. A large number of condition indicators, which are based on statistical moments of the synchronous average and its derivatives (difference and residual signals) were previously suggested. This study evaluates the efficiency of different gear condition indicators that are based on statistical moments and compares them with two new types of condition indicators that are suggested. The two new types of condition indicators are based on the order spectrum and the spectral kurtosis of the synchronous average.

The study was conducted on the labeled data of PHM'09 challenge. This data included recordings of vibrations in helical and spur gearboxes with seeded faults.
A Design Methodology of Optimized Diagnosis Functions for High Lift Actuation Systems
Authors: Modest Christian , Thielecke Frank
Published: March 2024

This paper presents a model-based approach to the optimal design of diagnosis system architectures for complex high lift actuation systems. The overall approach consists of two steps. In the first step, safety and reliability related requirements are introduced. These focus on the detectability and isolability of faults. Symptoms are used therefore. These are separated into safety and reliability related symptoms. In the second step, different alternatives to provide the symptoms are drawn and evaluated in order to gain an optimal design solution. A two stage analysis process is used therefore. The first stage focuses on the fulfillment of the safety related requirements whereas the second stage concentrates on the re- liability related requirements. All aspects of the analysis are depicted exemplary and formalized theoretically. The results of the application to the high lift actuation system of an Airbus A340-600 aircraft are presented afterwards and discussed in the end.
Signal Processing Techniques to Improve an Acoustic Emissions Sensor
Authors: Bechhoefer Eric , Qu Yongzhi , Zhu Junda , He David
Published: March 2024

Acoustic Emissions (AE) are stress waves produced by the sudden internal stress redistribution of material caused by changes in the internal structure of the material. Possible causes of these changes are crack initiation and growth, crack opening/closure, or pitting in monolithic materials (gear/ bearing material). Where as vibration can measure the effect of damage, AE is a direct measure of damage. Unfortunately, AE methodologies suffer from the need of high sample rates (4 to 10 Msps) and relatively immature algorithms for condition indictors (CI). This paper hypothesizes that the AE signature is the result of some forcing function (e.g. periodic motion in the case of rotating machinery). By using analog signal processing to demodulating the AE signature, one can reconstruct the information carried (e.g. modulation) by the AE signature and provide improved diagnostics. As most on-line condition monitoring systems are embedded system, analog signal processing techniques where used which reduce the effective sample rate needed to operate on the AE signal to those similarly found in traditional vibration processing systems. Further, by implementing another signal processing technique, time synchronous averaging, the AE signal is further enhanced. This allowed, for the first time, an AE signal to be used to identify a specific component within gearbox. This processing is tested on a split torque gearbox and results are presented.
Estimation of APU Failure Parameters Employing Linear Regression and Neural Networks
Authors: M.Pascoal Renata , O.L.Vianna Wlamir , P.P.Gomes João , K.H.Galvão Roberto
Published: March 2024

This study is concerned with the building of an appropriate model to estimate failure parameters of an Auxiliary Power Unit (APU). Linear and nonlinear models were used in order to evaluate which model is more suitable for this application. Data for model building and testing were obtained by simulating a nonlinear dynamic model of APU in Matlab/Simulink for various operating conditions to which it may be subjected to and with different levels of failure parameter degradation. Linear models were obtained by least-squares regression, whereas nonlinear models were obtained by training neural networks. The results obtained with these two models were compared. As a result, the neural network models were found to provide a better estimate of the APU failure parameters.
Electronic Returnless Fuel System Fault Diagnosis and Isolation: A Data-Driven Approach
Authors: Pattipati Bharath , Pattipati Krishna , A. Ghoneim Youssef , Howell Mark , Salman Mutasim
Published: March 2024

The Electronic Return-less Fuel System (ERFS) manages the delivery of fuel from the fuel tank to the engine. The pressure in the fuel line is electronically controlled by the fuel system control module by speeding up or slowing down the fuel pump. This allows the system to efficiently control the amount of fuel provided to the engine when compared to vehicles equipped with a standard fuel system wherein the fuel pump continuously runs at full speed. A failure in the fuel system that impacts the ability to deliver fuel to the engine will have an immediate effect on system performance. Consequently, improved reliability and availability, and reduction in the number of walk-home situations require efficient fault detection, isolation and prognosis of the ERFS system. This paper develops and implements data-driven fault detection, isolation and severity estimation algorithms for the ERFS. The HIL Fuel System Rig and a Chevrolet Silverado truck were used to collect and analyze the fuel system behavior under different fault conditions. Several data-driven classifiers, such as support vector machines, K- nearest Neighbor, Discriminant analysis, Bayes classifier, Partial- least squares, Quadratic and Linear classifiers, were implemented on a limited set of data for both training and testing. Regression techniques, such as Partial least squares regression and Principle component regression, are used to estimate the severity of faults. The resulting solution approach has the potential to be applicable to a wide variety of systems, ranging from automobiles to aerospace systems.
An Energy-Based Prognostic Framework to Predict Fatigue Damage Evolution in Composites
Authors: Chiach ́ıo Juan , Chiach ́ıo Manuel , Saxena Abhinav , Rus Guillermo , Goebel Kai
Published: March 2024

In this work, a prognostics framework to predict the evolution of damage in fiber-reinforced composites materials un- der fatigue loads is proposed. The assessment of internal damage thresholds is a challenge for fatigue prognostics in composites due to inherent uncertainties, existence of multiple damage modes, and their complex interactions. Our framework considers predicting the balance of release strain energies from competing damage modes to establish a reference threshold for prognostics. The approach is demonstrated on data collected from a run-to-failure tension-tension fatigue experiment measuring the evolution of fatigue damage in carbon-fiber-reinforced polymer (CFRP) cross-ply laminates. Results are presented for the prediction of expected degradation by micro-cracks for a given panel with the associated uncertainty estimates.
Integrated Sensor Arrays based on PiezoPaintTM for SHM Applications
Authors: Elkjaer Karl , Astafiev Konstantin , Ringgaard Erling , Zawada Tomasz
Published: March 2024

Recent progress in development of new functional materials that are flexible and can be processed at very low temperatures (below 100 °C) opens a new opportunity for applications, such as non-destructive evaluation (NDE), or structural health monitoring (SHM) by applying active materials directly on the structures made out of a variety of materials, e.g. metals (aluminium), plastics, and polymers, including CFRP (Carbon Fibre Reinforced Polymer). This paper presents sensor arrays based on a flexible piezoelectric material – PiezoPaintTM. The newly developed material exhibits relatively high sensitivity (d33 coefficient up to 45 pC/N), extremely low processing temperatures (< 120 °C), and high compliance in the cured state, enabling direct deposition of acoustic/vibration sensor arrays on structures to be monitored by means of screen- or pad- printing. The printed sensors have been applied for impact detection where four-element arrays and a fully integrated wiring system has been deposited directly on aluminium as well as CFRP plates. The presented results show very good performance in terms of sensitivity, flexibility of usage, and ultra-low weight, making PiezoPaintTM technology an attractive alternative for SHM particularly in aerospace applications.
Lubricants Health Monitoring
Authors: Jacazio Giovanni , Libraro Marco , Mornacchi Andrea , Sorli Massimo
Published: March 2024

Fault inception and growth in the components of a mechanical power drive are often heavily affected by the lubricant health. As a consequence, monitoring the lubricant health status and signaling a degradation of the lubricant properties could improve the reliability of the mechanical drive. A further merit of implementing a lubricant health monitoring system is the possibility to move from a time- based maintenance to a condition-based maintenance with the ensuing decrease of operating costs and reduction of the environmental impact. This would be of a particular advantage for those applications in which loss of operation generated by poor lubrication could cause a service disruption or lead to a safety critical condition. The proposed paper presents the initial part of an ongoing research activity on lubricants health monitoring. The work so far performed consisted of the following activities: perform a critical review of the studies addressing the lubricants condition; determine which are the significant features of the lubricants used in the majority of mechanical systems, that need to be extracted to assess the lubricant health; which occurences affect the lubricant health and how the oil health status can be assessed by different techniques, whose relative merits will be discussed; define the progression of the degradation of the lubricant health status; present the initial study of how different lubricant health monitoring techniques can be fused together to develop an efficient on-line lubricant health monitoring system. The paper thus presents the summary of the work performed to establish the fundamentals onto which a reliable PHM system for the lubricants of mechanical drives can be developed, that will be the final objective of the on-going research activity.
Health Monitoring of Hybrid Systems Using Hybrid Particle Petri Nets
Authors: Gaudel Quentin , Chanthery Elodie , Ribot Pauline
Published: March 2024

This paper presents an approach of model-based diagnosis for the health monitoring of hybrid systems. These systems have both continuous and discrete dynamics. Modified Particle Petri Nets, initially defined in the context of hybrid systems mission monitoring, are extended to estimate the health state of hybrid systems. This formalism takes into account both uncertainties about the system knowledge and about diagnosis results. The generation of a diagnoser is proposed to track online the system health state under uncertainties by using particle filter. To include more complex characteristics of the system, as its degradations for prognosis purpose, an enriched formalism called Hybrid Particle Petri Nets is defined.
Towards Performance Prognostics of a Launch Valve
Authors: Shevach Glenn , Blair Mark , Hing James , Venetsky Larry , Martin Everard , Wheelock John
Published: March 2024

Due to its criticality in aircraft carrier steam catapult operations, the performance of the Launch Valve is monitored using timer components to determine the elapsed time for the valve to achieve a set opening distance. Significant degradation in performance can lead to loss in end speed of the catapult and result in loss of aircraft/lives. This paper presents a method of using existing timing data for anomaly detection and predicting when maintenance is required (MIR) for a Launch Valve. Features such as mean and standard deviation of timing values are extracted from clock time data to detect anomalies. Neyman-Pearson Criterion and Sequential Probability Ratio Testing are used to formulate a decision on the degraded state. Once an anomaly is detected, an observation window of the previous N filtered samples are used in a risk sensitive particle filter framework. The resulting distribution is used in the prediction of shots until MIR. Performance degradation is extracted from training data and modeled as a third order polynomial. The algorithm was tested on two test sets and validated by Subject Matter Experts (SMEs) supplying the data. An Alpha-Lambda performance metric shows the time predictions until MIR fall inside an acceptable performance cone of 20% error.
Integrated Diagnostics and Prognostics for the Electrical Power System of a Planetary Rover
Authors: Daigle Matthew , Roychoudhury Indranil , Bregon Anibal
Published: March 2024

For electric vehicles, technology for monitoring, diagnosis, and prognosis of the electrical power system (EPS) becomes essential for safe and efficient operation. To this end, we develop a general system-level integrated diagnosis and prognosis framework, which detects, isolates, and identifies EPS faults, and predicts when the EPS will fail to deliver sufficient power. The approach takes advantage of recent work in structural model decomposition in order to distribute the global diagnosis and prognosis problems into local subproblems that can be solved in parallel, thus enabling implementation on distributed computational platforms. The framework is applied to the EPS of a planetary rover testbed, and is demonstrated using data from field experiments.
Gear Diagnostics – Fault Type Characteristics
Authors: Bliznyuk Alexander , Dadon Ido , Klein Renata , Bortman Jacob
Published: March 2024

To date, the majority of existing Condition Indicators for gears are based on various statistical moments of a recorded time history. A supplementary analysis proposed in this study, shall suggest an approach that may, in the future, enable the identification of faulty gearwheel and possibly fault type in the system. In this work, a combined analytical and empiric approach is applied. This approach is based on the assumption that reliable dynamic models can be utilized to predict the effects of faults on vibrational patterns. Dynamic model generated signatures are used to verify experimental findings. Moreover, discrepancies between simulated and actual results, combined with understanding of the assumptions and omissions of the model, are helpful in understanding and explaining the experimental results.A spur gear transmission setup was used for experiments, along with an electric AC motor and a friction belt loading device. The experimental runs were conducted at varying speed settings. Two types of faults, a tooth face fault and a tooth root fault, were seeded in the experimental transmission and into the model. The effect on extracted signal features is examined.

The purpose of this study is to evaluate fault detection capabilities of proposed diagnostic tools at the presence of two seeded faults of varying severity, verified by a dynamic model. Observed differences between examined fault types and their manifestation will be discussed. A basis for future work on prognostics capabilities is laid by a varying degree of tooth root fault.
A Vibration-Based Approach for Stator Winding Fault Diagnosis of Induction Motors: Application of Envelope Analysis
Authors: Jin Chao , P. Ompusunggu Agusmian , Liu Zongchang , D. Ardakani Hossein , Petré Fredrik , Lee Jay
Published: March 2024

Induction motors are usually considered as one of the key components in various applications. To maintain the availability of induction motors, it calls for a reliable condition monitoring and prognostics strategy. Among the common induction motor faults, stator winding faults are usually diagnosed with current and voltage signals. However, if the same performance can be achieved, the use of vibration signal is favorable because the winding fault diagnostic method can be integrated with bearing fault diagnostic method which has been successfully proven with vibration signal. Existing work concerning vibration for winding faults often takes it either as auxiliary to magnetic flux, or is not able to detect the winding faults unless severity is already quite significant. This paper proposes a winding fault diagnostic method based on vibration signals measured on the mechanical structure of an induction motor. In order to identify the signature of faults, time synchronous averaging was firstly applied on the raw vibration signals to remove discrete frequency components originating from the dynamics of the shaft and/or gears, and the spectral kurtosis filtering was subsequently applied on the residual signal to emphasize the impulsiveness. For the purpose of enhancing the residual signal in practice, a demodulation technique was implemented with the help of kurtogram. A series of experiments have been conducted on a three-phase induction motor test bed, where stator inter-turn faults can be easily simulated at different loads, speeds and severity levels. The experimental results show that the proposed method was able to detect inter-turn faults in the induction motor, even when the fault is incipient.
Formal Verification of Complex Systems based on SysML Functional Requirements
Authors: Mehrpouyan Hoda , Y. Tumer Irem , Hoyle Chris , Giannakopoulou Dimitra , Brat Guillaume
Published: March 2024

As modern systems continue to increase in size and complexity, they pose increasingly significant safety and risk management challenges. A model-based safety approach is an efficient way of coping with the increasing system complexity. It helps better manage the complexity by utilizing reasoning tools that require abstract models to detect failures as early as possible during the design process. This paper develops a methodology for the verification of safety requirements for design of complex engineered systems. The proposed approach combines a SysML modeling approach to document and structure safety requirements, and an assume-guarantee technique for the formal verification purpose. The assume- guarantee approach, which is based on a compositional and hierarchical reasoning combined with a learning algorithm, is able to simplify complex design verification problems. The objective of the proposed methodology is to integrate safety into early design stages and help the system designers to consider safety implications during conceptual design synthesis, reducing design iterations and cost. The proposed approach is validated on the quad-redundant Electro-Mechanical Actuator (EMA) of a Flight Control Surface (FCS) of an aircraft.
A Tool Chain for the V&V of NASA Cryogenic Fuel Loading Health Management
Authors: Schumann Johann , Gomez-Gonzalez Vanesa , Mahadevan Nagabhushan , Lowry Michael , Robinson Peter , Karsai Gabor
Published: March 2024

Complex machinery like spacecraft, aircraft, or chemical plants are equipped with fault detection and diagnosis systems. Due to their safety-critical nature, such diagnosis systems have to undergo rigorous Verification and Validation (V&V). In this paper, we present a tool suite to facilitate V&V of the deployed diagnostic system. The V&V relies on the paradigms of cross validation (to compare the diagnosis results of the deployed reasoner against those of other, more advanced reasoners), automatic fault scenario generation (to support extensive testing and coverage analysis), and parametric model analysis (to enrich test sets and for robustness and sensitivity analysis). We present the application of this tool architecture towards the V&V of the diagnosis system based on the TEAMS tool suite towards a subsystem in the NASA cryogenic fuel loading facility.
A Model-Based Approach for Reliability Assessment in Component- Based Systems
Authors: Nannapaneni Saideep , Dubey Abhishek , Abdelwahed Sherif , Mahadevan Sankaran , Neema Sandeep
Published: March 2024

This paper describes a formal framework for reliability assessment of component-based systems with respect to specific missions. A mission comprises of different timed mission stages, with each stage requiring a number of high- level functions. The work presented here describes a modeling language to capture the functional decomposition and missions of a system. The components and their alternatives are mapped to basic functions which are used to implement the system-level functions. Our contribution is the extraction of mission-specific reliability block diagram from these high-level models of component assemblies. This is then used to compute the mission reliability using reliability information of components. This framework can be used for real-time monitoring of system performance where reliability of the mission is computed over time as the mission is in progress. Other quantities of interest such as mission feasibility, function availability can also be computed using this framework. Mission feasibility answers the question whether the mission can be accomplished given the current state of components in the system and function availability provides information if the function is available in the future given the current state of the system. The software used in this framework includes Generic Modeling Environment (GME) and Python. GME is used for modeling the system and Python for reliability computations. The proposed methodology is demonstrated using a radio-controlled (RC) car in carrying out a simple surveillance mission.
Prognostic Optimization of Phased Array Antenna for Self-Healing
Authors: Allen David
Published: March 2024

Phased array antennas are widely used in many applications and consist of many antennas coupled together to enable digital beam-forming. As transmit/receive elements begin to degrade and eventually fail the antenna’s beam will distort from the desired pattern. We propose a novel optimization algorithm which takes into account not only the current state-of-health of the system, but potential future states-of- health from prognostic observations. The approach can be run entirely off-line (before the start of a mission), so requires no additional computational resources or sensors be added to the system and does not require the system to be able to detect the degradation/failures during a mission. Our main objective is to trade some current optimization flexibility for improved system robustness under future failures.
Review and Analysis of Algorithmic Approaches Developed for Prognostics on CMAPSS Dataset
Authors: Ramasso Emmanuel , Saxena Abhinav
Published: March 2024

Benchmarking of prognostic algorithms has been challenging due to limited availability of common datasets suitable for prognostics. In an attempt to alleviate this problem, several benchmarking datasets have been collected by NASA’s prognostic center of excellence and made available to the Prognostics and Health Management (PHM) community to allow evaluation and comparison of prognostics algorithms. Among those datasets are five C-MAPSS datasets that have been extremely popular due to their unique characteristics making them suitable for prognostics. The C-MAPSS datasets pose several challenges that have been tackled by different methods in the PHM literature. In particular, management of high variability due to sensor noise, effects of operating conditions, and presence of multiple simultaneous fault modes are some factors that have great impact on the generalization capabilities of prognostics algorithms. More than 70 publications have used the C-MAPSS datasets for developing data-driven prognostic algorithms. The C-MAPSS datasets are also shown to be well-suited for development of new machine learning and pattern recognition tools for several key preprocessing steps such as feature extraction and selection, failure mode assessment, operating conditions assessment, health status estimation, uncertainty management, and prognostics performance evaluation. This paper summarizes a comprehensive literature review of publications using C-MAPSS datasets and provides guidelines and references to further usage of these datasets in a manner that allows clear and consistent comparison between different approaches.
Testing Diagnostics Components Supervising Functional Safety Requirements
Authors: Nica Mihai , Pill Ingo , Wotawa Franz
Published: March 2024

For safety-critical applications, safety diagnostics components are an attractive safeguard for meeting some specified safety requirements under operation. Like a monitor, such a software artifact shall supervise a system under operation, and furthermore, if needed, it overrides the system’s control software in order to maintain safety. In this paper we contribute to testing such a component, suggesting an approach that draws on fault injection and, in order to enhance deployability, accommodates also needs in respect of business issues like intellectual property disclosure and resource efficiency. The required testing oracle we directly obtain from the defined and formalized functional safety requirements, for the purpose of assessing that the safety diagnostic component in- deed maintains safety also under faulty conditions.
Early Detection of Combustion Instability from Hi-speed Flame Images via Deep Learning and Symbolic Time Series Analysis
Authors: Sarkar Soumalya , G Lore Kin , Sarkar Soumik , Ramanan Vikram , R Chakravarthy Satyanarayanan , Phoha Shashi , Ray Asok
Published: March 2024

Combustion instability, characterized by self-sustained, large-amplitude pressure oscillations and periodic shedding of coherent vortex structures at varied spatial scales, has many detrimental effects on flight-propulsion dynamics and structural integrity of gas turbine engines. Hence, its early detection is one of the important tasks in engine health monitoring and prognostics. This paper proposes a dynamic data-driven approach, where a large volume of sequential hi-speed (greyscale) images is used to analyze the temporal evolution of coherent structures in combustion chamber for early detection of combustion instability at various operating conditions. The proposed hierarchical approach involves extracting low-dimensional semantic features from images using Deep Neural Networks followed by capturing the temporal evolution of the extracted features using Symbolic Time Series Analysis (STSA). Extensive experimental data have been collected in a swirl-stabilized dump combustor at various operating conditions for validation of the proposed approach. Intermediate layer visualization of deep learning reveals that meaningful shape-features from the flame images are extracted, which enables the temporal modeling layer to enhance the class separability between stable and unstable regions. At the same time, the semantic nature of intermediate features enables expert-guided data exploration that can lead to better understanding of the underlying physics. To the best of the authors knowledge, this paper presents one of the early applications of the recently reported Deep Learning tools in the area of prognostics and health management (PHM).
A Qualitative Fault Isolation Approach for Parametric and Discrete Faults Using Structural Model Decomposition
Authors: Daigle Matthew , Bregon Anibal , Roychoudhury Indranil
Published: March 2024

With increasing complexity of engineering systems, fault diagnostics plays a significant role in ensuring that they operate safely. Such systems most often exhibit mixed discrete and continuous, i.e., hybrid, behavior, and may encounter both parametric faults (unexpected changes in system parameters) as well as discrete faults (unexpected changes in component modes). Diagnosis becomes computationally very complex due to the large number of possible system modes, and possible mode changes that occur near the point of fault occurrence. This paper presents a qualitative fault isolation framework for integrated diagnosis of both parametric and discrete faults in hybrid systems, based on structural model decomposition. Fault isolation is performed by analyzing the qualitative information of the residual deviations, and considering observation delay. The great advantage of structural model decomposition for this problem is that it essentially defines several smaller independent diagnosis problems that become more efficient to solve, and makes the overall diagnosis problem more scalable. To demonstrate and test the validity ofour approach, we use a hydraulic multi-tank system as the case study in simulation. Results illustrate that the approach is both efficient and scalable.
A Computationally-Efficient Inverse Approach to Probabilistic Strain-Based Damage Diagnosis
Authors: Warner James E. , Hochhalter Jacob D. , Leser William P. , Leser Patrick E. , Newman John A.
Published: March 2024

This work presents a computationally-efficient inverse approach to probabilistic damage diagnosis. Given strain data at a limited number of measurement locations, Bayesian inference and Markov Chain Monte Carlo (MCMC) sampling are used to estimate probability distributions of the unknown location, size, and orientation of damage. Substantial computational speedup is obtained by replacing a three-dimensional finite element (FE) model with an efficient surrogate model. The approach is experimentally validated on cracked test specimens where full field strains are determined using digital image correlation (DIC). Access to full field DIC data allows for testing of different hypothetical sensor arrangements, facilitating the study of strain-based diagnosis effectiveness as the distance between damage and measurement locations increases. The ability of the framework to effectively perform both probabilistic damage localization and characterization in cracked plates is demonstrated and the impact of measurement location on uncertainty in the predictions is shown. Furthermore, the analysis time to produce these predictions is orders of magnitude less than a baseline Bayesian approach with the FE method by utilizing surrogate modeling and effective numerical sampling approaches.
Deep Learning for Structural Health Monitoring: A Damage Characterization Application
Authors: Sarkar Soumalya , Reddy Kishore K. , Giering Michael , Giering Michael
Published: March 2024

Structural health monitoring (SHM) is usually focused on damage detection (e.g., Yes/No) or approximate estimation of damage size. Any additional details of the damage such as configuration, shape, networking, geometrical statistics, etc., are often either ignored or significantly simplified during SHM characterization. These details, however, can be extremely important for understanding of damage severity and estimations of follow-up damage growth risk. To avoid expensive human participation and/or over-conservative SHM decisions, solutions of computational recognition for damage characterization are needed. Autonomous SHM from visual data is one of the significant challenges in the field of structural prognostics and health monitoring (PHM). The main shortcomings of the image-based PHM algorithms arise from the lack of robustness and fidelity to handle the variability of environment and nature of damage types. In recent times, deep learning has drawn huge amount of traction in the field of machine learning and visual pattern recognition due to its superior performance compared to the state-of-the-art techniques. This paper proposes to formulate and apply a deep learning technique to characterize the damage in the form of cracks on a composite material. The deep learning architecture is constructed by multi-layer neural network that is based on the fundamentals of unsupervised representational learning theory. The robustness and the accuracy of the approach is validated on an extensive set of real image data collected via applying variable load conditions on the structure. The paper has shown a high characterization accuracy over a wide range of loading conditions with limited number of labeled training image data.
Deep Health Indicator Extraction: A Method based on Auto-encoders and Extreme Learning Machines
Authors: Hu Yang , Palmé Thomas , Fink Olga
Published: March 2024

In this paper, we propose a novel deep learning method for feature extraction in prognostics and health management applications. The proposed method is based on Extreme Learning Machines (ELM) and Auto-Encoders (AE), which have demonstrated very good performance and very short training time compared to other deep learning methods on several applications, including image recognition problems. The proposed approach is applied to vibration condition monitoring data to extract features from normal operation (i.e. fault free conditions) without any additional expert knowledge or prior information on the type of signals and the information content in the datasets. The approach demonstrates a better performance in terms of trendability and monotonicity compared to commonly applied feature extraction methods.