bluuebunny/crossref_metadata_2025 huggingface dataset:
Modeling localized bearing faults using inverse Gaussian mixtures
Authors: Boškoski Pavle , Juričić Ðani
Published: March 2024

Localized bearing faults exhibit specific repetitive vibrational patterns. Due to the constant angular distance between the roller elements, the vibrational patterns occur on regular angular intervals. Under constant operating conditions such patterns become easily detectable as “periodic” events. Slippage or small variation in rotational speed are commonly modeled by introducing normally distributed time variations, which al- lows for occurrence of “negative” time intervals. In this paper we present an approach which models the occurrences of localized bearing fault patterns as a realization of random point process whose inter-event time intervals are governed by inverse Gaussian mixture. Having support on (0, ∞), the random impact times can acquire strictly positive values. The applicability of the model was evaluated on vibrational signals generated by bearing models with localized surface fault.
A Modeling Framework for Prognostic Decision Making and its Application to UAV Mission Planning
Authors: Balaban Edward , J. Alonso Juan
Published: March 2024

The goal of prognostic decision making (PDM) is to utilize information on anticipated system health changes in selecting future actions. One of the key challenges in PDM is find- ing a sufficiently expressive yet compact mathematical representation of the system for use with decision optimization algorithms. In this paper we describe a general modeling approach for a class of PDM problems with non-linear system degradation processes and uncertainties in state estimation, action effects, and future operating conditions. The approach is based on continuous Partially Observable Markov Decision Processes (POMDPs) used in conjunction with ’black box’ system simulations. The proposed modeling framework can be cast into simpler representations, depending on which sources of uncertainty are being included. The approach is illustrated with a mission planning case study for an unmanned aerial vehicle (UAV). In the case study a PDM system is tasked with optimizing the vehicle route after an in-flight component fault is detected. A stochastic algorithm (based on particle filtering) is used for decision optimization, with a second, deterministic algorithm providing a performance evaluation baseline. Both algorithms utilize a UAV physics simulator for generating predictions of future vehicle states. Performance benchmarking is done on a set of mission scenarios of increasing complexity.
Towards an Online Prognostic System for Predicting the Axial Shrinkage of AGR Cores
Authors: M. West Graeme , J. Wallace Christopher , D. J. McArthur Stephen
Published: March 2024

In the UK, there is the desire to extend the operation of the Advanced Gas-cooled Reactor (AGR) power plants beyond their initial design lifetimes of 35 years. As part of the justification of extended operation, an increased understanding of the current and future health of the graphite reactor cores is required. One measure of the health of the AGR power plants is the axial height of the graphite core, which can be determined through measurements undertaken during statutory outages. These measurements are currently used to manually make predictions about the future height of the core, through identifying the relevant data sources, extracting the relevant parameters and generating the predictions is time- consuming and onerous. This paper explores an online prognostic approach to support these manual predictions, which provides benefits in terms of rapid, updated predictions as soon as new data becomes available. Though the approach is described with reference to a case study of the UK’s AGR design of power plant, similar challenges of predicting passive structure health also exist in other designs of power plant with planned license extensions.
Battery Charge Depletion Prediction on an Electric Aircraft
Authors: Cuong Chi Quach , Bole Brian , Hogge Edward , Vazquez Sixto , Daigle Matthew , Celaya Jose ́ , Weber Adam , Goebel Kai
Published: March 2024

Validation of prognostic technologies through ground and flight tests is an important step in maturing these novel technologies and deploying them on real-world systems. To this end, a series of flight tests have been conducted using an un- manned electric vehicle during which the motor system batteries were monitored by a prognostic algorithm. The research presented here endeavors to produce and validate a technology for predicting the remaining time until end-of- discharge of the batteries on an electric aircraft as a function of an expected future flight and online estimates of the charge contained in the batteries. Flight data and flight experiment results are presented along with an assessment of model and algorithm performance.
Fleet-wide Diagnostic and Prognostic Assessment
Authors: Voisin Alexandre , Medina-Oliva Gabriela , Monnin Maxime , Leger Jean-Baptiste , Iung Benoit
Published: March 2024

In order to anticipate failures and reduce downtime, “predictive diagnostic” aims not only at warning about the failure events before they occur but also at identifying the causes of degradation leading to such detections. Then, based on the results of predictive diagnostic, “prognostic” aims at estimating the remaining useful life in order to plan a maintenance action before unit performances are affected. However, these are complex tasks. To overcome these difficulties, the notion of fleet may be very useful. In the present paper a fleet is composed of heterogeneous units (mainly components but could be systems or sub-systems) that are grouped together considering some similarities. The fleet can provide capitalized data and information coming from other members of the fleet for the improvement/development of the diagnostic/prognostic models. In order to achieve PHM with a fleet-wide dimension, it is thus necessary to manage relevant knowledge arising from the fleet taking into account heterogeneities and similarities amongst components, operational context, behaviours, etc. This paper will focus mainly in the formalization of a data-driven prognostic model considering a fleet-wide approach. The model is based on a prognostic approach of the system health using Relevant Vector Machine. The proposed model is based on historical data coming from similar units of a fleet. The heterogeneity of the monitored data is treated by assessing a global health index of the units. The proposed approach is shown on a case study. This case study illustrates how the fleet dimension facilitates predictive diagnostic and the definition of the prognostic model in the marine domain.
Electrochemical Impedance Spectroscopy for Coating Evaluation using a Micro Sensor
Authors: Waters Nicholas , Connolly Richard , Brown Douglas , Laskowski Bernard
Published: March 2024

This paper discusses a micro-linear polarization resistance (μLPR) sensor modified to perform coating evaluation by means of electrochemical impedance spectroscopy (EIS). A circuit model is used with the EIS data to measure solution resistance, pore resistance, charge transfer resistance, intact coating capacitance, and double layer capacitance. These measurements allow the end user to monitor degradation of protective coatings in real-time, through non-destructive means. This is demonstrated through an accelerated aging test using a coated metal plate with a modified μLPR sensor. A metal panel made from aluminum alloy 7075-T6 was coated with 2 mils of an epoxy-based polymer coating and 2 mils of high solids polyurethane. The sensor was adhered to the face of the coated panel in a manner that allowed the electrolyte solution consisting of 3.5% NaCl to flow between the sensor and the coated surface of the panel. EIS measurements were acquired every hour for a total of 35 hours and at the conclusion of the test, changes in key parameters within the circuit model identified the initial time and mechanism of coating degradation, in this case, delamination
Modeling Hydraulic Components for Automated FMEA of a Braking System
Authors: Struss Peter , Fraracci Alessandro
Published: March 2024

This paper presents work on model-based automation of failure-modes-and-effects analysis (FMEA) applied to the hydraulic part of a vehicle braking system. We describe the FMEA task and the application problem and outline the foundations for automating the task based on a (compositional) system model. Models of the essential hydraulic components suitable to generate the predictions needed for the FMEA are introduced and the required models of the control software outlined. These models are based on constraints, rather than simulation, and capture the dynamic response of the systems to an initial situation based on one global integration step and determine deviations from nominal functionality of the device. We also present the FMEA results based on this model.
Health Management and Diagnostics for Synthetic Aperture Radar (SAR) Payloads
Authors: Bower Gregory , Zook Jonathan , Bird Ross
Published: March 2024

A statistical method based on symbolic analysis is presented for health management of Synthetic Aperture Radar systems. The approach, based on symbolic theory, develops statistical models of the underlying system dynamics using an underlying Markov assumption and tracks the change in model over time to determine system health. The methodology was designed for minimal impact to legacy systems and required minimal computational effort in order to operate at radar data rates. The approach was applied to radar phase history data corrupted with simulated degradation. Two degradation mechanisms were studied: interference and array degradation. In addition, the results of combined degradation were also studied in this work.
Prognostic Modeling of Valve Degradation within Power Stations
Authors: J. McGhee M. , Galloway G. , Catterson V. M. , Brown B. , Harrison E.
Published: March 2024

Within the field of power generation, aging assets and a desire for improved maintenance decision-making tools have led to growing interest in asset prognostics. Valve failures can account for 7% or more of mechanical failures, and since a conventional power station will contain many hundreds of valves, this represents a significant asset base. This paper presents a prognostic approach for estimating the remaining useful life (RUL) of valves experiencing degradation, utilizing a similarity-based method. Case study data is generated through simulation of valves within a 400MW Combined Cycle Gas Turbine power station. High fidelity industrial simulators are often produced for operator training, to allow personnel to experience fault procedures and take corrective action in a safe, simulation environment, without endangering staff or equipment. This work repurposes such a high fidelity simulator to generate the type of condition monitoring data which would be produced in the presence of a fault. A first principles model of valve degradation was used to generate multiple run-to-failure events, at different degradation rates. The associated parameter data was collected to generate a library of failure cases. This set of cases was partitioned into training and test sets for prognostic modeling and the similarity based prognostic technique applied to calculate RUL. Results are presented of the technique’s accuracy, and conclusions are drawn about the applicability of the technique to this domain.
Demonstration of Prognostics-Enabled Decision Making Algorithms on a Hardware Mobile Robot Test Platform
Authors: Sweet Adam , Gorospe George , Daigle Matthew , R. Celaya Jose ́ , Balaban Edward , Roychoudhury Indranil , Narasimhan Sriram
Published: March 2024

Prognostics-enabled Decision Making (PDM) is an emerging research area that aims to integrate prognostic health information and knowledge about the future operating conditions into the process of selecting subsequent actions for the system. Previous work developing and testing PDM algorithms has been done in simulation; this paper describes the effort leading to a successful demonstration of PDM algorithms on a hardware mobile robot platform. The hardware platform, based on the K11 planetary rover prototype, was modified to allow injection of selected fault modes related to the rover’s electrical power subsystem. The PDM algorithms were adapted to the hardware platform, including development of a software module framework, a new route planner, and modifications to increase the algorithms’ robustness to sensor noise and system timing issues. A set of test scenarios was chosen to demonstrate the algorithms’ capabilities. The modifications to run with a hardware platform, the test scenarios, and the test results are described in detail. The results show a successful use of PDM algorithms on a hardware test platform to optimize mission planning in the presence of electrical system faults.
Imaging and Information Processing of Pitting-Corroded Aluminum Alloy Panels with Surface Metrology Methods
Authors: Li Honglei , R. Garvan Margaret , Li Jiaming , Echauz Javier , Brown Douglas , J. Vachtsevanos George
Published: March 2024

It has been established that corrosion is one of the most important factors causing structural deterioration, loss of metal, and ultimately decrease of product performance and reliability. Corrosion monitoring, accurate detection and interpretation are recognized as key enabling technologies to reduce the impact of corrosion on the integrity of critical aircraft and industrial assets. Interest in corrosion measurement covers a broad spectrum of technical approaches including acoustic, electrical and chemical methods. Surface metrology is an alternative approach used to measure corrosive rate and material loss by obtaining surface topography measurement at micrometer levels. This paper reports results from an experimental investigation of pitting corrosion detection and interpretation on aluminum alloy panels using 3D surface metrology methods, image processing and data mining techniques. Sample panels of AA 7075-T6, an aluminum alloy commonly used in aircraft structures, were coated on one side with a corrosion- protection coating and assembled in a lap-joint configuration. Then, a series of accelerated corrosion testing of the lap-joint panels were performed in a cyclic corrosion chamber running ASTM G85-A5 salt fog test. Panel surface characterization was evaluated with laser microscopy and stylus-based profilometry to obtain global and local surface images/characterization. Promising imaging and surface features were extracted and compared between the uncoated and coated panel sides, as well as on the uncoated sides under different corrosion exposure times. In the evaluation process, image processing, information processing and other data mining techniques were utilized. Information processing involves the steps of feature or Condition Indicator extraction and selection. The latter step addresses the problem of selecting those features that are maximally correlated with the actual corrosion state, for the purpose of corrosion detection, localization, quantification and state estimation. The results, verified by mass loss data, confirmed the contention that pits at the panel surfaces formed as a result of electrochemical corrosion attack, and showed that deteriorating pitting corrosion attack correlates with increasing corrosion exposure times. This study is a first step in the process of understanding, assessing and responding to the pitting corrosion and ultimately preventing material failure to insure aircraft structural integrity.
Managing Fleet Wide Sensory Data: Lessons Learned in Dealing with Volume, Velocity, Variety, Veracity, Value and Visibility
Authors: Johnson Preston
Published: March 2024

More than ever, asset operators and OEMs are investing in fleetwide monitoring systems. With the roll out of these monitoring systems, huge amounts of sensory data are generated. In a single Gigawatt power plant, asset monitoring systems sort through terabytes of sensory data per week. To contend with the volume and velocity of sensory data, analytics and data management techniques are employed along the life of sensory data from digitization at the asset, to storage in the information technology infrastructure. This paper presents techniques, both promising and fielded, for analytics to manage the volume, velocity, veracity, variety, and value of fleetwide asset monitoring data yielding opportunities for advanced visibility of actionable information.
Asynchronous Motor Test Bench for the Generation and Current Signal Diagnostics of Accelerated Bearing Damage
Authors: Anger Christoph , Preusche Christian , Klingauf Uwe
Published: March 2024

The data acquisition of run to failure data by means of de- grading components is one of the most delicate tasks in evaluating new diagnostic and prognostic approaches, since it is cost-intensive and time-consuming. Therefore, a test rig for a cost-efficient generation of artificial bearing damages is described below. The test rig is thereby based on an ordinary asynchronous motor.

This paper mainly concentrates on the description of the test rig’s setup and first diagnostic findings. One aim of the experiments is the investigation of several variations of the applied loads for the artificially accelerated bearing aging. Thus, radial force and fluting are examined. The latter causes a damage triggered by a current flow through the test bearing. Both load types reduce the overall lifespan of bearings to about few weeks.

The generated faults are a broken cage and chattermarks due to a radial force higher than the design point. The bearings are diagnosed by means of frequency analysis of the phase current signal, which is produced in the stator of the motor. Beside the current signal, also temperature, vibration and revolution of the shaft are measured, whereas the vibration signal is used only for the comparison to the current signal. The comprehensive measurement concept allows a performance evaluation of diagnostic and prognostic algorithms based on different physical indicators.

It can be shown that especially the current frequency spectrum of a faulty bearing differs significantly from a healthy one. In order to face the high amount of measurement data, the Principal Component Analysis is used for data reduction to generate features for the diagnosis and prognosis. Thus, a classification of different fault modes and loading conditions is possible.
Interactive Multiple-Model Application for Hydraulic Servovalve Health Monitoring
Authors: Olivares Loesch Vianna Wlamir , Yoneyama Takashi
Published: March 2024

Hydraulic systems are widely used as power source for several different applications. Servovalves are critical components and often subjected to failures. Estimating degradations from these components requires dynamic analysis of their behavior and consequently advanced monitoring techniques. This article proposes an online monitoring method to estimate a degradation parameter of the servovalve using an interactive multiple-model technique considering a bank of Extended Kalman Filters that models not only the valve itself but also the degradation trend. A single failure mode was considered related to the nozzle line clogging. The degradation estimates and the likelihood of the correctness of each model were analyzed in order to evaluate the proposed method.
Towards a Methodology for Design of Prognostic Systems
Authors: Ignacio Aizpurua Jose , M. Catterson Victoria
Published: March 2024

An effective implementation of prognostic technology can re- duce costs and increase availability of assets. As a result of the rapidly growing interest in prognostics, researchers have independently developed a number of applications for asset-specific modeling and prediction. Consequently, there is some inconsistency in the understanding of key concepts for designing prognostic systems. This further complicates the already-challenging design of new prognostic systems. In order to progress from application-specific solutions towards structured and efficient prognostic implementations, the development of a comprehensive and pragmatic methodology is essential. Prognostic algorithm selection is a key activity to achieve consistency throughout the design process. In this paper we present a design decision framework which guides the designer towards a prognostic algorithm through a cause- effect flowchart. Failure modes, application characteristics, and qualitative and quantitative metrics are used to determine an appropriate approach for the stated problem. The application of the methodology can reduce the time and effort required to develop a prognostic system, ensure that all the possible design options have been considered, and provide a means to compare different prognostic algorithms consistently. The framework has been applied to different prognostic problems within the power industry to illuminate its effectiveness. Case studies are presented to show how the framework guides designers through the choice of prognostic algorithm according to system requirements. The results demonstrate the applicability of the methodology to the design of prognostic systems which consistently meet the established requirements.
Omni-Directional Regeneration (ODR) of Gap Sensor Signal for Journal Bearing System Diagnosis
Authors: Ha Jung Joon , Chul Jeon Byung , D. Youn Byeng , Kim Donghwan , Kim Yeonwhan
Published: March 2024

We have developed a technique that enhances the detectability of sensors used to acquire data from a journal bearing rotor system. Usually, at an axial position for the rotating shaft on a journal bearing system, two sensors are fixed in radial direction at a right angle. The conventional diagnosis researches use only the acquired signals. However, two fixed sensors may not give sufficient information for diagnosis of the system since anomalies can happen in arbitrary direction. To improve the robustness of the diagnosis, coordinate transformed gap sensor signal is generated in arbitrary direction without installing extra sensors or adjusting sensor positions. With the original signals, the generated signals are used in the process of diagnosis. The powerful but simple method is described in the paper, and is verified by data sets from the experiment.
Adaptive Multi-scale PHM for Robotic Assembly Processes
Authors: Y. Choo Benjamin , A. Beling Peter , E. LaViers Amy , A. Marvel Jeremy , A. Weiss Brian
Published: March 2024

Adaptive multiscale prognostics and health management (AM-PHM) is a methodology designed to support PHM in smart manufacturing systems. As a rule, PHM information is not used in high-level decision-making in manufacturing systems. AM-PHM leverages and integrates component- level PHM information with hierarchical relationships across the component, machine, work cell, and production line levels in a manufacturing system. The AM-PHM methodology enables the creation of actionable prognostic and diagnostic intelligence up and down the manufacturing process hierarchy. Decisions are made with the knowledge of the current and projected health state of the system at decision points along the nodes of the hierarchical structure. A description of the AM-PHM methodology with a simulated canonical robotic assembly process is presented.
Review of Markov Models for Maintenance Optimization in the Context of Offshore Wind
Authors: Dawid Rafael , McMillan David , Revie Matthew
Published: March 2024

The offshore environment poses a number of challenges to wind farm operators. Harsher climatic conditions typically result in lower reliability while challenges in accessibility make maintenance difficult. One of the ways to improve availability is to optimize the Operation and Maintenance (O&M) actions such as scheduled, corrective and proactive maintenance. Many authors have attempted to model or optimize O&M through the use of Markov models. Two examples of Markov models, Hidden Markov Models (HMMs) and Partially Observable Markov Decision Processes (POMDPs) are investigated in this paper. In general, Markov models are a powerful statistical tool, which has been successfully applied for component diagnostics, prognostics and maintenance optimization across a range of industries. This paper discusses the suitability of these models to the offshore wind industry. Existing models which have been created for the wind industry are critically reviewed and discussed. As there is little evidence of widespread application of these models, this paper aims to highlight the key factors required for successful application of Markov models to practical problems. From this, the paper identifies the necessary theoretical and practical gaps that must be resolved in order to gain broad acceptance of Markov models to support O&M decision making in the offshore wind industry.
An Influence Gauge to Detect and Explain Relations between Measurements and a Performance Indicator
Authors: Lacaille Jérôme
Published: March 2024

What about a software tool that behaves like a gauge able to estimate the quantity of information contained in a group of measurements? Then if we have a performance indicator or a defect rate, how may we compute the maximum performance explanation contained in our dataset? The first question may be answered by entropy and the second with mutual information. The present paper recalls a simple way to use those mathematical tools in an application one wants to launch each time a new dataset has to be studied. Often the PHM team in Snecma is asked to participate in special workforces to analyze sudden crisis. This methodology helps at the very beginning of the process to identify our mathematical capability to build an explanation model. This was the case during a small engine start crisis when some spark plugs were not working. Another time we used this tool to identify the flying condition when a gearbox was heating. This methodology was first developed for industry purposes like the optimization of machine tools or process recipes. Its success is in the simplifications of the computations that enlighten the interpretability of the results. Each signal is quantified in a way that improves the mutual information with the performance indicator. This is done signal by signal, but also for any small subsets of multivariate measurements until the confidence given by the quantity and quality of the data reaches its maximum. The segmentation of the data helps and boosts the computation of the integrals. Moreover, as this methodology uses quantified data as inputs it works as well with any sort of inputs such as continuous, discrete ordered and even categorized measurements. Once a best subset of measurements is selected a simple non-linear model is built using a relaxation algorithm. This model is a set of hypercubes that classifies the input space in a very simple and interpretable way. The methodology given below is a rough approach and may be replaced by more efficient regression algorithms if one only have continuous measurements but it has some advantages like a way to search a “best rule” according to some constraints and a graphic navigation tool very efficient to correct recipes.
Quality Control Based Tool Condition Monitoring
Authors: Jain Amit Kumar , Lad Bhupesh Kumar
Published: March 2024

Quality control and tool condition monitoring are two most important aspects of machining process. This paper studies the correlation between tool wear and surface roughness to explore the possibility of modelling the interdependencies between these two aspects. An experimental study is presented in this paper to model the relationship between product quality parameter i.e. average surface roughness and tool wear. Current study reveals that there is a strong positive correlation between surface roughness and tool wear. To map this relationship an ensemble (random forest) fault estimation model is developed for identification and estimation of cutting tool health state. The results from fault estimation model are then used to provide guidelines for future process monitoring and developing dynamic quality control policy.
A Review of Prognostics and Health Management for Power Semiconductor Modules
Authors: Degrenne Nicolas , Ewanchuk Jeffrey , David Erwan , Boldyrjew Roman , Mollov Stefan
Published: March 2024

In this paper, a review of current techniques used in the prognostics and state-of-health monitoring for power semiconductor modules is provided. Given the increasing trend in power modules having a larger share of the power electronic market, understanding their lifetime limitations is critical to improving the life-cycle cost of the power electronic product. Hence, this paper reviews common failure mechanisms in power modules and the state-of-art in predicting the lifetime of the module based on a given mission profile. Prognostics are reviewed in terms of stress- based and condition monitoring-based methods, while the potential of prognostics is presented for applications that utilize power modules.
Abort Trigger False Positive and False Negative Analysis Methodology for Threshold-based Abort Detection
Authors: J. Melcher Kevin , A. Cruz José , B. Johnson Stephen , Lo Yunnhon
Published: March 2024

This paper describes a quantitative methodology for bounding the false positive (FP) and false negative (FN) probabilities associated with a human-rated launch vehicle abort trigger (AT) that includes sensor data qualification (SDQ). In this context, an AT is a hardware and software mechanism designed to detect the existence of a specific abort condition. Also, SDQ is an algorithmic approach used to identify sensor data suspected of being corrupt so that suspect data does not adversely affect an AT’s detection capability. The FP and FN methodologies presented here were developed to support estimation of the probabilities of loss of crew and loss of mission for the Space Launch System (SLS) which is being developed by the National Aeronautics and Space Administration (NASA). The paper provides a brief overview of system health management as being an extension of control theory; and describes how ATs and the calculation of FP and FN probabilities relate to this theory. The discussion leads to a detailed presentation of the FP and FN methodology and an example showing how the FP and FN calculations are performed. This detailed presentation includes a methodology for calculating the change in FP and FN probabilities that result from including SDQ in the AT architecture. To avoid proprietary and sensitive data issues, the example incorporates a mixture of open literature and fictitious reliability data. Results presented in the paper demonstrate the effectiveness of the approach in providing quantitative estimates that bound the probability of a FP or FN abort determination.
Integration of Failure Assessments into the Diagnostic Process
Authors: Koitz Roxane , Wotawa Franz
Published: March 2024

The complexity of technical systems requires increasingly advanced fault diagnosis methods to improve safety and reliability. Particularly in domains where maintenance poses an extensive part of the entire operation cost, accurate identification of failure sources has a large economic impact. Modelbased diagnosis, as a subfield of Artificial Intelligence, allows to determine root causes based on observed anomalies and has already been applied to a variety of domains. Abductive model-based diagnosis considers information on failures and how they affect detectable system measurements. Thus, this type of fault localization procedure depends on systematic and analytic knowledge on components, their possiblemalfunctions, and the subsequent effects. In this paper, we examine various common failure assessments such as Failure Mode Effect Analysis, in regard to serving as a basis for abductive diagnosis. In particular, we analyze the methods concerning their advantages and limitations as sources of failure information within our diagnosis process.
Fast optimization for aircraft descent and approach trajectory
Authors: Luchinskiy Dmitry G , Schuet Stefan , Brenton J. , Timucin Dogan , Smith David , Kaneshige John
Published: March 2024

We address the problem of online scheduling of the descent aircraft trajectory. The problem is considered in a general framework of the multiphase optimal control. First, we obtain solution of this problem using traditional approach. Next, we develop novel solution algorithm using two key components: (i) inference of the dynamical and control variables of the descending trajectory from the low dimensional flight profile and (ii) solution of the resulting low-dimensional optimization problem using efficient local search. We show that the developed algorithm is much faster than the traditional one and discuss its future application to the simultaneous optimization of the runway throughput and the descent trajectory for each aircraft in convective weather conditions.
Challenges And Opportunities in Applying Vibration Based Condition Monitoring in Railways
Authors: Tobon-Mejia Diego A. , Dersin Pierre , Tripot Gerard
Published: March 2024

Electrical rotating machines are among the most common assets used in industry. In railways applications these devices are present in fixed and rolling stock systems, such as turnouts and traction components. Condition based maintenance (CBM) of rotating machines may significantly improve the availability of critical railway assets. Moreover, by efficiently assessing the state of health of targeted components, it becomes possible to introduce advanced asset management strategies for life cycle cost optimization. In comparison with traditional maintenance approaches, health monitoring enables better maintenance scheduling, fleet size optimization and maintenance costs reduction. CBM applied to rotating machines has been actively studied by many researchers in a wide variety of fields such as: signal processing, anomaly detection, failure diagnostic and failure prognostics. However, there is still a considerable gap between the methods studied in research and the ones successfully applied in industry, and especially in the railway field. This paper discusses the challenges and opportunities for application of CBM methods to electrical rotating machines in railway applications. For the purpose of illustration, a case study focusing on traction motor bearings is considered. Time domain and frequencydomain signal processing techniques are employed to extract features from bearing degradation data. The data analyzed in the present study have been obtained in a bearing test bench and during a test conducted on a real traction motor used in trains. The results of the considered methods are discussed and future research directions are suggested.
Wind Turbine Intelligent Gear Fault Identification
Authors: Koukoura Sofia , Carroll James , McDonald Alasdair
Published: March 2024

This paper aims to present the development of a framework for monitoring of wind turbine gearboxes and prognosis of gear fracture faults, using vibration data and machine learning techniques. The proposed methodology analyses gear vibration signals in the order domain, using a shaft tachometer pulse. Indicators that represent the health state of the gear are algorithmically extracted. Those indicators are used as features to train diagnostic models that predict the health status of the gear. The efficacy of the proposed methodology is demonstrated with a case study using real wind turbine vibration data. Data is collected for a wind turbine at various time steps prior to failure and according to the maintenance reportsthere is enough data to form a healthy baseline. The data is classified according to the time before failure that the signal was collected.The learning algorithms used are discussed and their results are compared. The case study results indicate that this data driven model can lay the groundwork for a robust framework for the early detection of emerging gear tooth fracture faults. This can lead to minimisation of wind turbine downtime and revenue increase.
An improved model for remaining useful life prediction on capacity degradation and regeneration of lithium-ion battery
Authors: Deng Li-Ming , Hsu Yu-Cheng , Li Han-Xiong
Published: March 2024

The regeneration phenomena of the lithium-ion battery are widely existed in reality but rarely studied due to the gap between experiment conditions and practical working conditions. In this paper, the capacity regeneration phenomena are considered during the degradation process of batteries. An improved empirical model incorporating both rest time and discharge cycles for remaining useful life (RUL) prediction is proposed. The degradation process and regeneration process have been described by different components and integrated to formulate the whole model. The dual estimation framework is employed to decouple the states and parameters during the degradation and regeneration process. The datasets from NASA Prognostics Center of Excellence (PCoE) have been adopted for model validation. The proposed model is compared with other empirical model and also different estimation methods. The results are satisfactory, and demonstrate the capability of the proposed model for the RUL prediction of Lithium-ion battery.
A Simulation Engine for Predicting State-of-Charge and State-of-Health in Lithium-Ion Battery Packs of Electric Vehicles
Authors: Espinoza Pablo A. , Pérez Aramis , Orchard Marcos E. , Navarrete Hugo F. , Pola Daniel A.
Published: March 2024

Recent developments in lithium-ion technology have enabled a revolution in the automotive industry. Fully electric vehicles (EVs) operate under distinctly variable conditions, requiring high-voltage battery packs to meet their torque/power demands. Our goal is to provide a simulation engine which, for a given battery pack size, determines when recharging or battery pack replacement are needed. To that end, we study both the State-of-Charge (SOC) and the State-of-Health (SOH) indicators, using discrete state space models for both. Predictions are based on a probabilistic characterization of EV usage profiles, which in turn are a function of generic user-input, such as mission maps, vehicle mechanical characteristics,driving schedules, and battery pack configuration. State space models benefit from the incorporation of metamodels for the ohmic internal resistance and the Coulomb efficiency of the pack. Both meta-models i) effectively introduce additional phenomenology –such as dependency onthe magnitude of discharged current and depth of discharge (DoD)–, and ii) provide a link between SOC/SOH and how each discharge cycle affects the health status of the battery pack as a whole. The approach for the simulation engine presented here is stochastic in nature, meaning that prognostics for the SOC and SOH are generated in a particle filter-based scheme. Thus risk and confidence intervals can be obtained for the end-of-discharge and end-of-life respectively
CLINICAL STUDY ON FUNDUS CHANGES IN PREGNANCY INDUCED HYPERTENSION IN A TERTIARY CARE HOSPITAL
Authors: Kiranmai M. , Bhuvaneshwari K. , Renuka K.
Published: March 2024

Pregnancy-induced hypertension (PIH) is a syndrome of hypertension with or without proteinuria and oedema. PIH
complicates 6 – 10% of pregnancies, major cause of new born, foetal, maternal morbidity, and mortality. The Incidence of
Preeclampsia in nulliparous is more common than in multiparous women. Headache, visual disturbances and right upper quadrant pain are
indicative of imminent eclampsia. Diagnosis is by clinical examination and various diagnostic modalities like regular BP measurement, weight
check-up, fundus examination, and other laboratory investigations. They are at greater risk of abruptio placenta, early diagnosis is needed to
reduce complications and improve foetal outcome.
CLEAR CELL HIDRADENOMA OF THE SCALP- DIAGNOSED ON CYTOLOGY
Authors: S. Bhat Rajika , Sousa Rocha Premila De , Jaladi Pratiksha , Pinto R. G. Wiseman
Published: March 2024

Clear cell hidradenoma is an uncommon benign adnexal tumor that arises as nodules from eccrine sweat glands. The
diagnosis is usually conrmed on histopathology, and the lesion poses a diagnostic challenge for cytopathologist and is
rarely diagnosed on ne needle aspiration cytology (FNAC) (1). Here we report a case of clear cell hidradenoma in a 34-year female, that was
initially diagnosed on FNAC and later conrmed on histopathology. Thus, our study emphasizes that FNAC can be used as a rst line diagnostic
modality for nodular lesions of the skin.
DIAPHRAGMATIC FISSURE OF LIVER- A CASE REPORT.
Authors: P Rafeeq. , PR Ashalatha , M Sudha.
Published: March 2024

We report a case of Diaphragmatic ssure on anterior surface of right lobe of liver seen in an adult male cadaver during
routine dissection. The ssure is vertically extending in the middle of the anterior surface of right lobe and extends till the
inferior border. Length of the ssure is 9 cm and depth is 1.2 cm. Pons hepatis with 13mm length also seen in the same liver. Incidence of
Accessory ssures of liver is 25% and frequency increases from 7th decade. 15%of the cadaveric livers may show accessory ssures. The
knowledge of the diaphragmatic ssures may be utilized by Anatomists, Surgeons, Forensic specialists and Radiologists to avoid misdiagnosis
and complications. When peritoneally disseminated tumor cells implant in such ssures, it may mimic intrahepatic lesions.
A STUDY OF RISK FACTORS OF HAEMORRHOIDAL DISEASE IN ADULT IN A TERTIARY CARE CENTRE
Authors: Sumpi Subu , Muri Tabu , Pao Karan , Komut Ojing , Das Neha , Das Narayan
Published: March 2024

Background/Aims: Haemorrhoids are the most common benign anorectal disorder in clinical practice and it cause
substantial discomfort, disability, and decrease in quality of life(QOL). Risk factors commonly associated with
haemorrhoidal disease include advancing age, female gender, lower socioeconomic status, low bre diet, chronic constipation, chronic
diarrhoea, chronic straining during defecation, pregnancy, alcohol intake, sedentary life style, obesity and spinal cord injuries etc. The objective
of this study is to estimate the incidence of various risk factors and clinical presentation of haemorrhoidal disease in adult in a tertiary care centre
at TRIHMS, Naharlagun. This was a prospective study of 200 patients of haemorrhoids attend at SOPD Materials And Methods: at TRIHMS,
Naharlagun for a period of 6 months from 01-01-2022 to 30-06-2022. The diagnosis of haemorrhoids was based on thorough history, careful
physical examination and proctoscopy examination of anal canal and distal rectum. High-risk patients investigated with colonoscopy
examination. Patients who met the inclusion criteria were included in this study. All the relevant details of patients about age, sex, clinical
presentation, risk factors and different degree of haemorrhoids were recorded. The common age group Results: of haemorrhoids was from 18
years to 37 years(67.5%) and followed by 38 years to 57 years (30.5%). There was 53% of females and 47% of males. Per rectal bleeding
(71.5%)followed by pain during defecation (54.5%) and prolapsed anal mass (32.5%). The common risk factors are non-vegetarian diet
(95.5%), constipation (88.5%), straining during defecation (63.5%) and use of mobile phone in toilet (92.5%).Internal haemorrhoids- second
degree (47.5%) and third degree (40.5%) are common types of haemorrhoids in this study. Haemorrhoid Conclusion: s are the common benign
anorectal disorder in clinical practice. There are many modiable risk factors (lifestyle, dietary habits, addictive habits, use of mobile phone in
toilet etc.) and non-modiable risk factors (age, gender, heredity and genetic makeup) are important for development of haemorrhoids. So proper
suggestion and proper education of the patients is essential to take proper precautions and avoid unnecessary complications of haemorrhoids.
BRONCHOSCOPY AND FOREIGN BODY REMOVAL IN CHILDREN – CAN WE CHANGE THE OUTCOME?
Authors: Prabhakaran P Aswin , Abraham Mohan K , Sibi Raaj M. G.
Published: March 2024

Background: Diagnosis of Foreign body aspiration is often delayed due to lack of awareness. Objective: This study was
done to nd out the incidence and sequel of delayed diagnosis in the patients referred to a tertiary care center. Study
Design: Participants: The data of patients presenting with foreign body bronchus over a period of 12 years were reviewed. All children who
underwent foreign body removal at our centre from 2010 to 2022. Rigid bronchoscope and foreign body Intervention: removal was done in all
cases. Successful removal could be accomplished in all cases. We had a total of 109 patients. Time Outcome: Results: taken to report to the
hospital after aspiration ranged from 1 to 365 days with a mean of 15 and median of 3 days. Mean age was 2.4 years. Sixty-two had foreign body of
the right side, 40 on the left side, 2 on both sides, 3 at Carina and 2 in trachea. Groundnut was the most common foreign body with 61 children
having them. Twenty-four children underwent CT chest for conrmation of diagnosis. One child required a second bronchoscopy after 24 hours
for complete removal of the foreign body. One child had erosion and perforation of the bronchus. We did not have any death in the series.
Conclusions: Awareness about foreign body aspiration has to be increased among the general public and primary care physician to facilitate an
early referral. Foreign body can be effectively and safely managed with rigid bronchoscope and optical forceps.
AN OLD GASTROJEJUNAL ANASTOMOTIC SITE PERFORATION PRESENTING AS ACUTE ABDOMEN: A CASE REPORT
Authors: Thamannaz S , Reddy P Viswanath
Published: March 2024

Complications most frequently encountered with GastroJejunal anastomosis after a bypass procedure are Marginal ulcer,
GI bleeding and strictures. Marginal ulceration (MU) at the anastomotic site after gastric bypass is a well-recognized
complication. Its incidence varies between 1% and 16%. Direct exposure of gastric acid is the important cause of ulcer formation which may get
further complicated by the use of NSAID's, alcohol intake and smoking. But perforation secondary to ulceration at anastomotic site is very rare
and seldom reported. We report a case of 37 year old male patient who developed perforation at the gastrojejunal anastomotic site of old
gastrojejunostomy for peptic ulcer disease.
CORPORATE SOCIAL RESPONSIBILITY (CSR) DALAM PENINGKATAN KESEJAHTERAAN MASYARAKAT
Authors: Saputeri Noorikha Pandayahesti , Tyas Eki , Fakhrurozi Moh , Sari Mutiara , Warsiyah Warsiyah , Harahap Sofyan
Published: March 2024

Tapis Pesisir merupakan sebuah perusahaan industri pembuatan kain Tapis dengan berbagai motif eksklusif menggunakan teknik rajut. Sebuah perusahaan dalam menjalankan produksi Tapis tentunya akan menghasilkan limbah dari setiap proses produksinya. Oleh karena itu, sudah menjadi tanggung jawab perusahaan untuk melaksanakan Corporate Social Responsibility (CSR) karena keberadaannya di tengah lingkungan masyarakat. Penelitian ini bertujuan untuk mengetahui bagaimana implementasi dari usaha Tapis Pesisir terhadap peningkatan kesejahteraan masyarakat yang berada di sekitar wilayah perusahaan tepatnya di desa Pekon Way Sindi, Pesisir Barat. Penelitian ini menggunakan pendekatan kualitatif, dengan jenis penelitian lapangan (field research). Sumber data yang digunakan berupa sumber data primer dan sekunder. Data tersebut didapatkan melalui observasi, wawancara dan dokumentasi. Setelah itu, data tersebut dianalisis menggunakan metode analisis deskriptif kualitatif karena dalam hal ini penulis mendeskripsikan Corporate Social Responsibility (CSR) yang dijalankan oleh Tapis Pesisir. Hasil penelitian diketahui bahwa Corporate Social Responsibility (CSR) yang dijalankan oleh Tapis Pesisir merupakan suatu kewajiban yang harus dilaksanakan sebagai tanggung jawab perusahaan terhadap karyawan, masyarakat maupun lingkungan sekitar. Implementasi Corporate Social Responsibility (CSR) Tapis Pesisir terhadap peningkatan kesejahteraan masyarakat dilaksanakan melalui dua bentuk yaitu CSR sosial dan CSR lingkungan. Dari kedua program Corporate Social Responsibility (CSR) Tapis Pesisir tersebut dapat meningkatkan kesejahteraan masyarakat dengan manfaat yang didapatkan oleh semua pihak yang ada.