bluuebunny/crossref_metadata_2025 huggingface dataset:
Towards Real-time, On-board, Hardware-supported Sensor and Software Health Management for Unmanned Aerial Systems
Authors: Schumann Johann , Y. Rozier Kristin , Reinbacher Thomas , J. Mengshoel Ole , Mbaya Timmy , Ippolito Corey
Published: March 2024

Unmanned aerial systems (UASs) can only be deployed if they can effectively complete their missions and respond to failures and uncertain environmental conditions while maintaining safety with respect to other aircraft as well as humen and property on the ground. In this paper, we design a real-time, on-board system health management (SHM) capability to continuously monitor sensors, software, and hardware components for detection and diagnosis of failures and violations of safety or performance rules during the flight of a UAS. Our approach to SHM is three-pronged, providing: (1) real-time monitoring of sensor and/or software signals; (2) signal analysis, preprocessing, and advanced on- the-fly temporal and Bayesian probabilistic fault diagnosis; (3) an unobtrusive, lightweight, read-only, low-power realization using Field Programmable Gate Arrays (FPGAs) that avoids overburdening limited computing resources or costly re-certification of flight software due to instrumentation.Our implementation provides a novel approach of combin- ing modular building blocks, integrating responsive runtime monitoring of temporal logic system safety requirements with model-based diagnosis and Bayesian network-based probabilistic analysis. We demonstrate this approach using actual data from the NASA Swift UAS, an experimental all-electric aircraft.
A Framework to Debug Diagnostic Matrices
Authors: Kodali Anuradha , Robinson Peter , Patterson-Hine Ann
Published: March 2024

Diagnostics is an important concept in system health and monitoring of space operations. Many of the existing diagnostic algorithms utilize system knowledge in the form of diagnostic matrix (D-matrix, also popularly known as diagnostic dictionary, fault signature matrix or reachability matrix). The D-matrix maps tests on observed conditions to failures. This matrix is mostly gleaned from physical models during system development. But, sometimes, this may not be enough to obtain high diagnostic performance during operation due to system modifications and lag and noise in sensor measurements. In such a case, it is important to modify this D-matrix based on knowledge obtained from sources such as time-series data stream (simulated or maintenance data) within a framework that includes the diagnostic/inference algorithm. A systematic and sequential update procedure, diagnostic modeling evaluator (DME) is proposed to modify D-matrix and wrapper/test logic considering the least expensive update first. The user sets the diagnostic performance criteria. This iterative procedure includes conditions ranging from modifying 0’s and 1’s in the matrix, adding/removing the rows (failure sources)/columns (tests), or modifying test/wrapper logic used to determine test results. We will experiment this framework on ADAPT datasets from DX challenge 2009.
Distilling the Verification Process for Prognostics Algorithms
Authors: Roychoudhury Indranil , Saxena Abhinav , R. Celaya Jose , Goebel Kai
Published: March 2024

The goal of prognostics and health management (PHM) systems is to ensure system safety, and reduce downtime and maintenance costs. It is important that a PHM system is verified and validated before it can be successfully deployed. Prognostics algorithms are integral parts of PHM systems. This paper investigates a systematic process of verification of such prognostics algorithms. To this end, first, this paper distinguishes between technology maturation and product development. Then, the paper describes the verification process for a prognostics algorithm as it moves up to higher maturity levels. This process is shown to be an iterative process where verification activities are interleaved with validation activities at each maturation level. In this work, we adopt the concept of technology readiness levels (TRLs) to represent the different maturity levels of a prognostics algorithm. It is shown that at each TRL, the verification of a prognostics algorithm de- pends on verifying the different components of the algorithm according to the requirements laid out by the PHM system that adopts this prognostics algorithm. Finally, using simplified examples, the systematic process for verifying a prognos- tics algorithm is demonstrated as the prognostics algorithm moves up TRLs.
A PHM Approach to Additive Manufacturing Equipment Health Monitoring, Fault Diagnosis, and Quality Control
Authors: Yoon Jae , He David , Van Hecke Brandon
Published: March 2024

Fabrication of three-dimensional (3D) objects through direct deposition of functional materials using 3D printing equipment is called additive manufacturing (AM). Benefits of AM include producing goods quickly and on-demand, with greater customization and complexity and less material waste. While the use of AM has been growing, a number of challenges continue to impede its more widespread adoption, particularly in the areas of non-destructive evaluation/non- destructive testing (NDE/NDT) techniques for AM equipment health monitoring and measurement. In this paper, a prognostics and health management (PHM) approach to AM equipment health monitoring, fault diagnosis and quality control is presented and illustrated with a case study. The presented PHM approach is developed using two types of NDE/NDT sensors: acoustic emission (AE) sensor and piezoelectric strain sensor. A seeded driving belt fault on a fused filament fabrication desktop 3D printer is used to validate the feasibility of the PHM approach in the case study. The case study results have shown the effectiveness of the presented method for AM equipment fault diagnosis and quality control.
Applying Swarm Intelligence and Bayesian Inference for Wind Turbine SCADA-Based Condition Monitoring and Prognostics
Authors: Ye Xiang , Osadciw Lisa
Published: March 2024

Diagnosis and prognosis of potential faults is crucial to maintain and improve the efficiency of the wind energy system. In this paper, we propose a SCADA-based condition monitoring and prognostics system. We apply particle swarm optimization to recognize different patterns of turbine health condition by fusing performance test results. As monitoring daily turbine health condition, we design a data-driven Bayesian inference approach to predict turbine potential failures by tracking the abnormal variations.
A Data Driven Method for Model Based Diagnostics and Prognostics
Authors: D. Bryant Michael
Published: March 2024

This article’s model based diagnostics system has four modules. Diagnosis and fault location forms physics models of the machine, measures states off the real in-service machine, generates simulated machine states and simulated sensor outputs for the machine model with loads same as the real machine, and compares simulated sensor outputs to real sensor outputs. The parameter tuning module adjusts (tunes) the parameters of the model until the simulated sensor outputs closely mimic real sensor outputs. Tuning transfers information on the system’s health from the sensor data to the model’s parameters. Parameters changed from nominal values locate faults and bad parts. For the health assessment module to assess machine health, we view a machine as a “machine channel” that organizes power and information flow through the machine. Machines focus power via an organization inherent in its components and design. Broken or degraded components disrupt this organization and the power and information flows. Shannon’s information theory for communications channels can then be applied as a health metric to this “machine channel”. Ageing of components degrades machine functional health. To prognose future health, differential equations that model ageing of the machine’s components are formulated and solved. These equations predict component degradation, and update values of parameters in the model associated with component ageing. With these future parameter values, simulations of the machine operation model can then predict “future” machine behavior, and system health. This article demonstrates these methods on motors and a pump.
Using Johnson Distribution for Automatic Threshold Setting in Wind Turbine Condition Monitoring System
Authors: S. Marhadi Kun , Alexandros Skrimpas Georgios
Published: March 2024

Setting optimal alarm thresholds in vibration based condition monitoring system is inherently difficult. There are no established thresholds for many vibration based measurements. Most of the time, the thresholds are set based on statistics of the collected data available. Often times the underlying probability distribution that describes the data is not known. Choosing an incorrect distribution to describe the data and then setting up thresholds based on the chosen distribution could result in sub-optimal thresholds. Moreover, in wind turbine applications the collected data available may not represent the whole operating conditions of a turbine, which results in uncertainty in the parameters of the fitted probabil- ity distribution and the thresholds calculated. In this study Johnson distribution is used to identify shape, location, and scale parameters of distribution that can best fit vibration data. This study shows that using Johnson distribution can eliminate testing or fitting various distributions to the data, and have more direct approach to obtain optimal thresholds. To quantify uncertainty in the thresholds due to limited data, implementations with bootstrap method and Bayesian inference are investigated.
Temporal Causal Diagrams for Diagnosing Failures in Cyber-Physical Systems
Authors: Mahadevan Nagabhushan , Dubey Abhishek , Karsai Gabor , Srivastava Anurag , Liu Chen-Ching
Published: March 2024

Resilient and reliable operation of cyber physical systems of societal importance such as Smart Electric Grids is one of the top national priorities. Due to their critical nature, these systems are equipped with fast-acting, local protection mechanisms. However, commonly misguided protection actions together with system dynamics can lead to un-intentional cascading effects. This paper describes the ongoing work using Temporal Causal Diagrams (TCD), a refinement of the Timed Failure Propagation Graphs (TFPG), to diagnose problems associated with the power transmission lines protected by a combination of relays and breakers.

The TCD models represent the faults and their propagation as TFPG, the nominal and faulty behavior of components (including local, discrete controllers and protection devices) as Timed Discrete Event Systems (TDES), and capture the cumulative and cascading effects of these interactions. The TCD diagnosis engine includes an extended TFPG-like reasoner which in addition to observing the alarms and mode changes (as the TFPG), monitors the event traces (that correspond to the behavioral aspects of the model) to generate hypotheses that consistently explain all the observations. In this paper, we show the results of applying the TCD to a segment of a power transmission system that is protected by distance relays and breakers.
A Novel Linear Polarization Resistance Corrosion Sensing Methodology for Aircraft Structure
Authors: W. Brown Douglas , J. Connolly Richard , Garvan Margaret , Li Honglei , S. Agarwala Vinod , Vachtsevanos George
Published: March 2024

A direct method of measuring corrosion on a structure using a micro-linear polarization resistance (μLPR) sensor is presented. The new three-electrode μLPR sensor design presented in this paper improves on existing LPR sensor technology by using the structure as part of the sensor system, allowing the sensor electrodes to be made from a corrosion resistant or inert metal. This is in contrast to a two- electrode μLPR sensor where the electrodes are made from the same material as the structure. A controlled experiment, conducted using an ASTM B117 salt fog, demonstrated the three-electrode μLPR sensors have a longer lifetime and better performance when compared to the two-electrode μLPR sensors. Following this evaluation, a controlled experiment using the ASTM G85 Annex 5 standard was performed to evaluate the accuracy and precision of the three-electrode μLPR sensor when placed between lap joint specimens made from AA7075-T6. The corrosion computed from the μLPR sensors agreed with the coupon mass loss to within a 95% confidence interval. Following the experiment, the surface morphology of each lap joint was determined using laser microscopy and stylus-based profilometry to obtain local and global surface images of the test panels. Image processing, feature extraction, and selection tools were then employed to identify the corrosion mechanism (e.g. pitting, intergranular).
Fault Detection and Severity Analysis of Servo Valves Using Recurrence Quantification Analysis
Authors: Samadani M. , A. Kitio Kwuimy C. , Nataraj C.
Published: March 2024

This paper presents the application of recurrence plots (RPs) and recurrence quantification analysis (RQA) in model-based diagnostics of nonlinear systems. A detailed nonlinear mathematical model of a servo electro-hydraulic system has been used to demonstrate the procedure. Two faults have been considered associated with the servo valve including the increased friction between spool and sleeve and the degradation of the permanent magnet of the valve armature. The faults have been simulated in the system by the variation of the corresponding parameters in the model and the effect of these faults on the RPs and RQA parameters has been investigated. A regression-based artificial neural network has been finally developed and trained using the RQA parameters to estimate the original values of the faulty parameters and identify the severity of the faults in the system.
Fault Tolerant Control for Manufacturing Discrete Systems by Filter and Diagnoser Interactions
Authors: Philippot A. , Marangé P. , Gellot F. , F. Pétin J. , Riera B.
Published: March 2024

The paper deals with an online safety mechanism to define interactions between a diagnoser and a control filter for fault tolerant control of manufacturing discrete systems. The diagnoser observes the plant behavior whereas the control filter ensures the safety from the controller. This online interaction is based by events communication where the control law is never reconfigured. The proposed approach is applied to CISPI platform from the CRAN laboratory (Research Center for Automatic Control of Nancy).
Estimation of Bogie Performance Criteria Through On-Board Condition Monitoring
Authors: Shahidi Parham , Maraini Dan , Hopkins Brad , Seidel Andrew
Published: March 2024

In this paper, bogie performance criteria are reviewed and it is shown that a real-time, on-board condition monitoring system can efficiently monitor these criteria to improve failure mode detection in freight rail operations. Although the dynamics of rail car bogie performance are well understood in the industry, this topic has recently received renewed attention through impending regulatory changes. These changes seek to extend empty rail car performance criteria to include loaded rail cars as well. Currently, the monitoring of bogie performance is primarily accomplished by wayside detection systems in North America. These systems are only sparsely deployed in the track network and do not offer the ability to monitor bogies continuously. The lack of these elements leads to unexpected downtimes resulting in costly reactive maintenance and lengthy periods of time before an adequate performance history can be established. This paper reviews performance criteria which critically influence bogie performance and proposes a vibration based condition monitoring strategy to estimate system component deterioration and their contribution to the development of bogie hunting. The strategy addresses both sensing techniques and monitoring algorithms to maximize the efficiency of the monitoring solution. In particular it is proposed that understanding the relation of different hunting modes to car body oscillations can be used for a deeper understanding of the rail car condition which current technologies are not able to provide.
Particle Filtering-Based System Degradation Prediction Applied to Jet Engines
Authors: Wang Peng , X. Gao Robert
Published: March 2024

This paper investigates a real-time fault detection and degradation prediction scheme for dynamical systems such as jet engines, based on Regularized Particle Filtering (RPF). Particle Filtering is a prognosis method for the prediction of state degradation and remaining useful life (RUL) due to its demonstrated performance in handling non-linear and non-Gaussian situations. RPF overcomes the problem of sample impoverishment among particles over the resampling process. Based on measured data from hybrid sensing and nonlinear models, which link system parameters and degradation state to the measurement, RPF has been applied to establishing a framework for both state and parameter estimation, to achieve prognosis at the component level. In addition, a modified system evolution model is proposed to track both exponential and transient types of system performance degradation. The developed method is evaluated using simulated data created with C- MAPSS, which contains measured parameters associated with engine degradation under nominal and varied fault types (fan, compressor and turbine) during a series of flights. The developed system-parameter estimation method is found effective in state estimation and degradation prediction in jet engines.
Hardware Development for the Controlled Fault Injection into a Turbofan Engine Air-Bleed Valve
Authors: E. Gorospe George , L. Simon Donald , F. Goebel Kai
Published: March 2024

Gas path fault diagnostics assists operators in determining, and managing the health of gas turbine engines. Engine data depicting fault progression under realistic operating conditions is useful for the maturation of these diagnostic methods. In this paper, we present hardware created to inject a progressive fault in an air bleed valve of a high bypass turbofan engine during on-wing engine testing. The developed hardware interrupts and overrides the engine control computer’s command of the valve and allows for the nondestructive, progressive off-schedule operation of the air bleed valve. Numeri- cal simulation results based on NASA’s Commercial Modular Aero-Propulsion System Simulation 40k are presented to illustrate representative changes in measured engine parameters that can be expected during such an experiment.
Diagnostic Reasoning using Prognostic Information for Unmanned Aerial Systems
Authors: Schumann Johann , Roychoudhury Indranil , Kulkarni Chetan
Published: March 2024

With increasing popularity of unmanned aircraft, continuous monitoring of their systems, software, and health status is becoming more and more important to ensure safe, correct, and efficient operation and fulfillment of missions. The paper presents integration of prognosis models and prognostic information with the R2U2 (REALIZABLE, RESPONSIVE, and UNOBTRUSIVE Unit) monitoring and diagnosis framework. This integration makes available statistically reliable health information predictions of the future at a much earlier time to enable autonomous decision making. The prognostic information can be used in the R2U2 model to improve diagnostic accuracy and enable decisions to be made at the present time to deal with events in the future. This will be an advancement over the current state of the art, where temporal logic observers can only do such valuation at the end of the time interval. Usefulness and effectiveness of this integrated diagnostics and prognostics framework was demonstrated using simulation experiments with the NASA Dragon Eye electric unmanned aircraft.
PHM Based Predictive Maintenance Option Model for Offshore Wind Farm O&M Optimization
Authors: Lei Xin , A. Sandborn Peter , Goudarzi Navid , A. Bruck Maira
Published: March 2024

A simulation-based real options analysis (ROA) approach is used to determine the optimum predictive maintenance opportunity for multiple wind turbines with remaining useful life (RUL) predictions in offshore wind farms managed under outcome-based contracts, i.e., power purchase agreements (PPAs). When an RUL is predicted for a subsystem in a single turbine using PHM, a predictive maintenance option is triggered that the decision-maker has the flexibility to decide if and when to exercise before the subsystem or turbine fails. The predictive maintenance value paths are simulated by considering the uncertainties in the RUL predictions and wind speeds (that govern the turbine’s revenue earning potential). By valuating a series of European options expiring on all possible predictive maintenance opportunities, a series of option values can be obtained, and the optimum predictive maintenance opportunity can be selected. The optimum predictive maintenance opportunity can also be determined using a stochastic discounted cash flow (DCF) approach that assumes the predictive maintenance will always be implemented on the selected opportunity. For a wind farm managed via a PPA with multiple turbines indicating RULs concurrently, the predictive maintenance value for each turbine depends on the operational state of the other turbines, the amount of energy delivered and to be delivered by the whole wind farm. A case study is presented in which the stochastic DCF and European ROA approaches are applied to a single turbine and to a wind farm managed via a PPA. The optimum predictive maintenance opportunities obtained from the two approaches are compared and it is demonstrated that the European ROA approach will suggest a more conservative opportunity for predictive maintenance with a higher expected option value than the expected net present value (NPV) from the stochastic DCF approach.
Lithium-Ion Battery End-of-Discharge Time Estimation and Prognosis based on Bayesian Algorithms and Outer Feedback Correction Loops: A Comparative Analysis
Authors: Tampier Carlos , Pérez Aramis , Jaramillo Francisco , Quintero Vanessa , E. Orchard Marcos , F. Silva Jorge
Published: March 2024

Battery energy systems are currently one of the most common power sources found in mobile electromechanical devices. In all these equipment, assuring the autonomy of the system requires to determine the battery state-of-charge (SOC) and predicting the end-of-discharge time with a high degree of accuracy. In this regard, this paper presents a comparative analysis of two well-known Bayesian estimation algorithms (Particle filter and Unscented Kalman filter) when used in combination with Outer Feedback Correction Loops (OFCLs) to estimate the SOC and prognosticate the discharge time of lithium-ion batteries. Results show that, on the one hand, a PF-based estimation and prognosis scheme is the method of choice if the model for the dynamic system is inexact to some extent; providing reasonable results regardless if used with or without OFCLs. On the other hand, if a reliable model for the dynamic system is available, a combination of an Unscented Kalman Filter (UKF) with OFCLs outperforms a scheme that combines PF and OFCLs.
Battery Capacity Anomaly Detection and Data Fusion
Authors: Weddington John , Yan Wuzhao , Dou Wanchun , Zhang Bin
Published: March 2024

Anomaly detection is a critical enabling technique of PHM, especially in safety critical applications. In order for the PHM system to begin prediction of remaining useful life of a given system or component, the fault must be detected. This paper presents an integrated anomaly detection system for state-of- health of lithium-ion batteries. Two algorithms for state estimation and anomaly detection are used: the extended Kalman filter and the particle filter. A Dempster-Shafer Theory-based fusion approach is implemented to reduce the uncertainty of detection. The results on battery data show that the fusion improves the detection results significantly.
Processing and Interpretation of Crack-Propagation Sensors
Authors: G. Nenadic Nenad , A. Ardis Paul , Hood Adrian , G. Thurston Michael , G. Lewicki David
Published: March 2024

The goal of gearbox prognostics, once a failure has been detected, is to estimate the degree of damage. Development of probabilistic damage assessment requires high quality ground truth. The present report describes practical analysis, processing, and interpretation of signals from crack-propagation sensors and damage estimation during gear teeth crack propagation. The study considers two types of crack propagations: one for a fatigue-tester-based crack propagation, and the other for a propagation in a gearbox. Crack closing occurs in both types of crack propagation and must be accounted for in assessing the damage. The analysis is conducted for sensors connected as a voltage divider. Signal conditioning and wire breakage inference are examined in detail. In addition, we discuss how equipping each gear tooth with two crack- propagation sensors, one on each gear face, can provide a better damage estimation.
Improved Fault Detection by Appropriate Control of Signal Bandwidth of the TSA
Authors: Bechhoefer Eric , Zhang Xinghui
Published: March 2024

Vibration analysis is perhaps the longest serving technology used in condition monitoring. It is usually assumed that higher sampling rates improve fault detection due to the increased bandwidth of the acquisition. That said, increased bandwidth may decrease the signal to noise, impairing fault detection. Alternatively, if the fault features’ bandwidth is greater than the system bandwidth, the fault cannot be observed. One tool of vibration analysis is the Time Synchronous Average (TSA) analysis. Statistics of the TSA itself can be used as a fault feature, or statistics based on analysis performed on the TSA (energy operator, residual analysis, amplitude/frequency modulation analysis) are used as fault features. Additionally, the computation of the TSA requires a tachometer signal for zero crossing, which has its own bandwidth effect on the TSA analysis. This paper discusses bandwidth control techniques to improve fault detection using the TSA. The techniques are validated using real world pinion data. These techniques have other advantages for embedded condition monitoring systems.
Using Deep Learning Based Approaches for Bearing Fault Diagnosis with AE Sensors
Authors: He Miao , He David , Bechhoefer Eric
Published: March 2024

In the age of Internet of Things and Industrial 4.0, the prognostic and health management (PHM) systems are used to collect massive real-time data from mechanical equipment. Mechanical big data has the characteristics of large-volume, diversity and high-velocity. Effectively mining features from such data and accurately identifying the machinery health conditions with new advanced methods become new issues in PHM. A major problem of using the existing PHM methods for machinery fault diagnosis with big data is that the features are manually extracted relying on much prior knowledge about signal processing techniques and diagnostic expertise, limiting their capability in fault diagnosis with big data. This paper presents a deep learning based approach for bearing fault diagnosis using acoustic emission (AE) sensors with big data. Different from widely used shallow neural network architecture with only one hidden layer, the branch of machine learning methods with multiple hidden layers are regarded as deep learning method. The presented approach pre-processes AE signals using short time Fourier transform (STFT) and extract features. Based on the simply processed AE features, an optimized deep learning structure, large memory storage retrieval neural network (LAMSTAR) is used to perform bearing fault diagnosis. The unique structure of LAMSTAR enables it to establish more efficient and sparse distributed feature maps than traditional neural networks. By leveraging the labelled information via supervised learning, the trained network is endowed with discriminative ability to classify bearing faults. The AE signals acquired from a bearing test rig are used to validate the presented method. The test results show the accurate classification performance on various fault types under different working conditions, namely input shaft rotating speeds. It also proves to be effective on diagnosing bearing faults with relatively low rotating speeds.
Solenoid Valve Diagnosis for Railway Braking Systems with Embedded Sensor Signals and Physical Interpretation
Authors: Seo Boseong , Jo Soo-Ho , Oh Hyunseok , Youn Byeng D.
Published: March 2024

This paper proposes a fault diagnosis method for solenoid valves in urban railway braking systems. For dominant failure modes of solenoid valves, sensor signals including electrical current, and input and output pressure were acquired and analyzed. The physical behaviors of the solenoid valves are modeled analytically. Numerous forces including magnetic, elastic, and gravity forces are incorporated in the model. With the analytical model and sensor signals, health indices are defined. The health indices are used to quantify the condition of the solenoid valves with different failure modes. Finally, a fault diagnosis method is proposed with the health indices and failure criteria. We anticipate that this study can help decrease maintenance costs and improve reliability of urban railway braking systems.
Condition Monitoring of a Reciprocating Compressor Using Wavelet Transformation and Support Vector Machines
Authors: Falzone Shawn , Kolodziej Jason R.
Published: March 2024

Condition monitoring techniques were applied to a reciprocating compressor in order to determine if faults were present in a system. Through the use of vibration based sensors, fault monitoring of the crank-side discharge valve springs was accomplished. Data was collected through a range of injected fault conditions and analyzed through the use of discrete wavelet transformations. The wavelet coefficients produced were transformed into a six-dimensional feature space though the use of first and second order statistics. By using a support vector machine classifier, the nominal and faulted condition data was used to train a fault monitoring classifier. This classifier was verified through the use of additional test data, and resulted in classification rates of 90% and above. This result is based on the trial of a multitude of different wavelets and support vector kernels in order to achieve the optimal performance for the dataset.
Spatio-temporal Probabilistic Modeling Based on Gaussian Mixture Models and Neural Gas Theory for Prediction of Criminal Activity
Authors: Jaramillo Francisco , Quintero Vanessa , Pérez Aramis , Orchard Marcos
Published: March 2024

Criminal risk models are used to assist security forces both in the identification of zones with high of criminal activity for better resource allocation and prediction of future criminal events for the prevention of new crimes. In this sense, spatio-temporal models are widely employed by their capacity of characterizing the criminal risk inside of a zone of interest and updating the model to new crime data. This paper improves an existing method based on spatio-temporal probabilistic risk functions. The spatial probabilistic characterization uses geo-referenced information of criminal incidents related to public services to approximate a risk function based on a Gaussian Mixture Model (GMM). The temporal characterization is supported by Importance Sampling methods and Neural Gas Theory to incorporate the information from new measurements, in a recursive manner, updating the spatial probabilistic risk function. Finally, we propose a prediction scheme for criminal activity that also uses Neural Gas Theory, in conjunction with hypothetical future criminal events sampled from a GMM that characterizes the spatial distribution associated with recent criminal activity. The time index related to each hypothetical future crime event is probabilistically characterized using an exponential distribution. Results using real data and the defined performance indexes show an improvement both in the temporal updating as well as the proposed prediction approach.
Low-complexity Behavioral Model for Predictive Maintenance of Railway Turnouts
Authors: Barkhordari Pegah , Galeazzi Roberto , Tejada Alejandro de Miguel , Santos Ilmar F.
Published: March 2024

Maintenance of railway infrastructures represents a major cost driver for any infrastructure manager since reliability and dependability must be guaranteed at all times. Implementation of predictive maintenance policies relies on the availability of condition monitoring systems able to assess the infrastructure health state. The core of any condition monitoring system is the a-priori knowledge about the process to be monitored, in the form of either mathematical models of different complexity or signal features characterizing the healthy/faulty behavior. This study investigates the identification of a low-complexity behavioral model of a railway turnout capable of capturing the dominant dynamics due to the ballast and railpad components. Measured rail accelerations, acquired through a receptance test carried out on the switch panel of a turnout of the Danish railway network, have been utilized together with the Eigensystem Realization Algorithm – a type of subspace identification – to identify a fourth order model of the infrastructure. The robustness and predictive capability of the low-complexity behavioral model to reproduce track responses under different types of train excitations have been successfully validated. It is anticipated that the identified model will be instrumental for the development of methods for diagnosis and prognosis of faults and degradation process in switches and crossings.
Dynamic Modeling of Maintenance Prices in the Aerospace Industry
Authors: Ekladious George S. Eskander , Zhao Xiaojun , Mostafa Hala , Georgescu Ramona
Published: March 2024

In the aerospace industry, pricing of the maintenance, repair and operations involve complex business rules that are being formulated by domain experts. Automation of such a process becomes a challenge, especially when pricing rules significantly differ based on contract conditions, operators, maintenance shops, etc. This paper presents a pricing prediction approach, where the predictors are dynamically built to fit the different pricing rules. To this end, a clustering mechanism efficiently splits the space to dissimilar clusters that are likely to follow different pricing rules. Then, candidate models are designed and ranked for the different clusters. At the exploitation phase, a testing data sample is assigned to a cluster, and processed using the best model for that cluster. Results show significant accuracy improvement compared to the static modeling approach.
A Comparison of Acoustic Emission and Vibration Measurements for Condition Monitoring of an Offshore Drilling Machine
Authors: Hemmer Martin , Waag Tor I.
Published: March 2024

This paper investigates the application of heterodyned Acoustic Emission (AE) compared to more conventional vibration measurements for Condition Monitoring (CM) of an offshore drilling machine, with a particular focus on the large, axial tapered roller bearing supporting the drill string weight in a top drive. The focus on cost reduction and operational uptime in the oil and gas industry motivates research on improved CM methods for fault detection, identification and ultimately prediction. However, bearing failure on this type of machines are currently responsible for a significant share of operational downtime on drilling rigs. In the experiment, a previously used and replaced bearing is compared to a new, healthy bearing with the purpose of identifying possible condition indicators (CI) from the vibration and AE measurements. AE root-mean-square values (RMS) was identified as a CI, being more consistent with the expected bearing health than vibration measurements and also less affected by operating speed. The AE measurements also show complementary forced frequency identification capabilities compared to the vibration measurements. The particular failure mode with bearing roller end damage is described and seen in conjunction with the results.
A New Application for Failure Prognostics – Reduction of Automotive Electronics Reliability Test Duration
Authors: Kleyner Andre , Vasan Arvind , Pecht Michael
Published: March 2024

This paper presents a novel application of failure prognosis to shorten the time of reliability testing. Typically, prognostic outcome is used to make real time health management decisions such as modify mission plan, change system operation parameters to reduce stress and increase remaining useful life, and more. In this work we demonstrate the use of prognostics to reduce the duration of lengthy and expensive tests, such as power temperaturecycling and high temperature endurance in the automotive electronics validation process.
Effect of Ambient Temperature on Performance of Gas Turbine Engine
Authors: Liu Yuan , Banerjee Avisekh , Kumar Amar , Srivastava Alka , Goel Nita
Published: March 2024

Health monitoring data, namely power output and exhaust gas temperature are used for performance analysis of an industrial gas turbine engine. Apart from ambient temperature, influences of fuel consumptions and compressor fouling on the engine performance are also considered in the work. A model-free data analytics approach is used to study the effects of individual governing factors. A performance index (ratio of power generation to fuel consumption) is proposed as the metrics for monitoring the engine performance. The step-by-step analysis suggests that the engine performance shows two opposing trends with the ambient temperature. Fouling is also clearly shown to reduce the engine performance using the proposed indices.
Estudios sobre género. La investigación y la innovación para la igualdad y el impulso de las mujeres en la sociedad
Authors: Dios Sánchez Irene
Published: March 2024

Esta obra es fruto de la ejecución de un proyecto de I+D+i en el marco del Programa Operativo FEDER Andalucía, proyecto Impulsa2 para el desarrollo personal, social y profesional de las mujeres en el ámbito rural. La necesidad de investigar sobre la igualdad de género e impulsar acciones que permitan la difusión y la transferencia de estos resultados a la sociedad contribuye a que podamos convivir en una sociedad más justa y democrática, y promueve que todas estas labores favorezcan la igualdad de oportunidades entre hombres y mujeres en el ámbito personal, social, laboral y profesional. La obra consta de cinco capítulos que abordan investigaciones y propuestas con perspectiva de género. Se pretende visibilizar estudios profesionales y especializados con el firme deseo de luchar por la igualdad real y efectiva de mujeres y hombres. El objetivo es el aprendizaje y la discusión de la temática que en la obra se defiende, no solo como concepto, sino como realidad tangible en el día a día. Se realiza un recorrido por áreas que aparentemente parecen distintas, pero que en todas ellas se aprecia cómo confluyen elementos siempre presentes cuando se trata de desigualdad y violencia de género. Las autoras forman parte del Grupo de investigación SEJ-551: «Investigación en interculturalidad, plurilingüismo, género y formación del profesorado» del Sistema de Información Científica de Andalucía (SICA), de la Consejería de Transformación Económica, Industria, Conocimiento y Universidades.
A REVIEW OF QUANTUM PHYSICS IN BIOLOGICAL SYSTEMS
Authors: Al Deen Shah Mohamed Nizam , Patric Jeff , M A Vidya , P Satelu Krishnanand
Published: March 2024

This review on quantum biology, a burgeoning eld at the intersection of life and the bizarre rules of the quantum world, is
investigating how phenomena like tunneling, superposition, and entanglement might inuence biological processes.
From the dance of odor molecules and smell receptors, potentially inuenced by tunneling for odor discrimination, to the mind-bending
possibility of entangled electrons guiding bird migration, the eld is uncovering a hidden language within life. In photosynthesis, superposition
allows light-excited electrons to explore multiple pathways, maximizing their efciency in converting light into energy. Enzymes, the
masterminds of countless reactions, might benet from tunneling, allowing substrates to overcome energy hurdles, and coherence, enhancing
selectivity. The radical pair mechanism, with its entangled electrons, is a potential explanation for how birds navigate with Earth's magnetic eld.
Finally, the article ventures into the uncharted territory of consciousness, pondering the possibility of quantum effects playing a role in neuronal
ion channels, though signicant challenges remain in verifying and understanding these effects within the brain's complex environment. Overall,
this exciting nascent eld holds the potential to illuminate intricate biological mechanisms and offer glimpses into the enigmatic realm of
consciousness.
CIRUGIA DE KASAI: INDICAÇÕES E TÉCNICAS
Authors: Habr Lara Elias , Fernandes Nathalia Coelho , Gorgen Indiara Maria Távora , Moura Isabella de Mendonça Alves Rodrigues , Barcelos Gustavo Rocha , Oliveira Gabriella Mendonça Leão de , Teixeira Daniel Bozzi , Floresta Isabela Zabisky , Carneiro João Gabriel Rodrigues , Veiga Luísa Dias Alencastro , Pontes Raquel Rios de Castro , Maroubo Leonardo Mailho , Fernandes Eduardo Gomes , Uema Vinicius Hideki Hamassaki
Published: March 2024

A cirurgia de Kasai, ou hepatoportoenterostomia, é um procedimento cirúrgico utilizado no tratamento da atresia biliar em neonatos. Essa condição congênita resulta na obstrução das vias biliares, levando à acumulação de bile no fígado. O objetivo da cirurgia de Kasai é restabelecer o fluxo biliar, contornando a obstrução. Durante a cirurgia, uma anastomose é criada entre o fígado e uma porção do intestino delgado, permitindo a drenagem direta da bile. A escolha da técnica específica, como a anastomose em Y ou em H, depende das características anatômicas do paciente. O exame intraoperatório, frequentemente auxiliado por colangiografia, é crucial para avaliar a permeabilidade da anastomose. Embora a cirurgia de Kasai possa ser eficaz quando realizada precocemente, sua viabilidade está condicionada à extensão da obstrução e à presença de complicações. Em casos nos quais a cirurgia não é suficiente para manter um adequado fluxo biliar, ou quando há progressão para cirrose hepática avançada, o transplante hepático torna-se uma alternativa terapêutica importante. Essa abordagem multidisciplinar, envolvendo hepatologistas, cirurgiões pediátricos, enfermeiros especializados e outros profissionais de saúde, é essencial para um diagnóstico preciso, escolha adequada da técnica cirúrgica e manejo pós-operatório. Em resumo, a cirurgia de Kasai desempenha um papel crucial no tratamento da atresia biliar, mas sua eficácia está intrinsecamente ligada à abordagem precoce e à colaboração entre diferentes especialidades médicas.
Parangolés: Taniec i uruchomienie ciała widza w sztuce brazylijskiej lat sześćdziesiątych
Authors: Sosnowska Agnieszka
Published: March 2024

Autorka analizuje twórczość artysty Hélio Oiticiki i neokonkretyzm brazylijski, aby na tym przykładzie zobrazować zmianę paradygmatu dzieła sztuki zachodzącą od lat sześćdziesiątych, interpretowaną jako droga od dzieła sztuki pojmowanego jako obiekt do efemerycznego wydarzenia. Zmiana ta implikuje osadzenie percepcji dzieła sztuki w doświadczeniu, redefinicję roli odbiorcy, otwarcie na życie codzienne i rzeczywistość społeczną i polityczną. Artykuł koncentruje się na powstałym w latach 1964–1968 cyklu Parangolés, aby ukazać, jak taniec stał się dla Oiticiki medium transformacji sztuki, a także życia społecznego. Parangolés wyglądają jak kolorowe płachty czy długie szarfy. Szyje się je bez uwzględnienia rozmiarów konkretnego ciała. Oplatając się nimi, trzeba dostosować je do swojej sylwetki i poprzez opanowanie materii stworzyć kompozycję w przestrzeni. Doświadcza się ich więc jako mobilnych rzeźb, które poruszają się razem z użytkownikiem czy bezpośrednio na nim.
PENERAPAN PERATURAN KAPOLRI NOMOR 2 TAHUN 2016 TENTANG PENYELESAIAN PELANGGARAN DISIPLIN ANGGOTA KEPOLISIAN NEGARA REPUBLIK INDONESIA TERHADAP ANGGOTA KEPOLISIAN YANG MELAKUKAN KEKERASAN KEPADA MASYARAKAT
Authors: Yulianto Irwan
Published: March 2024

Kepolisian Negara Republik Indonesia, adalah salah satu institusi pemerintah yang bertugas sebagai ujung tombak penegakan hukum di Indonesia. Tugas yang diemban ini tidaklah ringan karena akan berhadapan dengan masyarakat. Penegakan hukum, bukan saja masyarakat harus sadar hukum dan taat hukum, tetapi lebih bermakna pada pelaksanaan hukum sebagaimana mestinya dan bagi yang melanggar harus pula ditindak menurut prosedur dan ketentuan hukum yang berlaku. Tipe Penelitian yang digunakan adalah jenis penelitian normatif atau penelitian kepustakaan. Penelitian Kepustakaan, yaitu penelitian yang dilakukan dengan cara meneliti bahan pustaka atau bahan sekunder yang terdiri dari bahan hukum primer, bahan hukum sekunder dan bahan hukum tersier. Dilihat dari bentuknya penelitian ini termasuk penelitian deskriptif. Penelitian deskriptif adalah penelitian yang dimaksud untuk memberikan bahan yang diteliti mungkin tentang manusia, keadaan atau gejala-gejala lainnya. Berdasarkan uraian latar belakang dan pembahasan penelitian ini, maka dapat diambil kesimpulan bahwa Dasar hukum Polri dalam memberikan sanksi hukum bagi polisi yang melakukan kekerasan kepada masyarakat yaitu Peraturan Pemerintah Republik Indonesia (PPRI) Nomor 2 Tahun 2003 tentang Peraturan Disiplin Anggota Kepolisian Negara Republik Indonesia. Berdasarkan rumusan pasal 1 angka 3 PPRI Nomor 2 Tahun 2003 tentang Peraturan Disiplin Anggota Kepolisian Negara Republik Indonesia adalah penegakan, pembinaan kedisiplinan serta pemeliharaan dalam hal tata tertib anggota kepolisian. Peraturan Disiplin Anggota Kepolisan Negara Republik Indonesia tidak hanya didasarkan pada kebutuhan profesional, tetapi juga telah diatur secara normatif dalam Undang-undang Nomor 2 Tahun 2002 tentang Kepolisian Negara Republik Indonesia yang ditindaklanjuti dengan terwujudnya Peraturan Pemerintah Republik Indonesia Nomor 2 Tahun 2003 Tentang Peraturan Disiplin Anggota Kepolisian Negara Republik Indonesia serta Perkap Kapolri Nomor 2 Tahun 2016 tentang Penyelesaian Pelanggaran Anggota Polri tertuang dalam Bab III , Bagian I Pasal 33 dan Prinsip hukum dari Polri tentang sanksi hukum bagi polisi yang melakukan kekerasan kepada masyarakat ditinjau dari Peraturan Kepala Kepolisian Negara Republik Indonesia Nomor 2 Tahun 2016 tentang Penyelesaian Pelanggaran Disiplin Anggota Kepolisian Negara Republik Indonesia.