bluuebunny/crossref_metadata_2025 huggingface dataset:
Improved Probabilistic Remaining Useful Life Estimation in Structures: Modeling Multi-site Fatigue Cracking in Oil and Gas Service Structures
Authors: Al Tamimi Abdallah , Modarres Mohammad
Published: March 2024

The purpose of this research is to develop a multi-site damage probabilistic life prediction model that could be used to assess the integrity of engineering structures susceptible to fatigue in presence of neighboring cracks. Both experiments and simulation were used to produce the data required for the model development. The experiments were performed to investigate the interaction of two adjacent semi-elliptical cracks under cyclic loading. A series of tests at different loads and for different crack aspect ratios were conducted under uniaxial constant amplitude fatigue loads on API-5L grade B steel samples. Crack growth rate of two initial semi-elliptical cracks was investigated both on the sample surface and in the depth direction. Moreover, Crack growth and interaction was investigated using a simulation technique that incorporates the stress intensity factor of a single crack with an existing cracks interaction correction factor models from the literature. Finally, a Bayesian inference modeling technique is adopted to estimate the life prediction model parameters, assess any model bias and uncertainty and validate it.
An Aircraft Lifecycle Approach for the Cost-Benefit Analysis of Prognostics and Condition-based Maintenance based on Discrete-Event Simulation
Authors: B. Hölzel Nico , Schilling Thomas , Gollnick Volker
Published: March 2024

The paper will provide a lifecycle cost-benefit analysis of the use of Prognostics and Health Management (PHM) systems and a conditioned-based maintenance (CBM) concept in future aircraft. The proposed methodology is based on a discrete-event simulation for aircraft operation and maintenance and uses an optimization algorithm for the planning and scheduling of CBM tasks. In the study, a 150- seat short-range aircraft equipped with PHM and subject to a CBM program will be analyzed. The PHM-aircraft will be compared with an Airbus A320-type of aircraft with maintenance expenditures equivalent to a conventional block check maintenance program. The analysis results will support the derivation of technical and economic requirements for prognostic systems and CBM planning concepts.
Adaptation of an Electrochemistry-based Li-Ion Battery Model to Account for Deterioration Observed Under Randomized Use
Authors: Bole Brian , S. Kulkarni Chetan , Daigle Matthew
Published: March 2024

Tracking the variation in battery dynamics as a function of health is presently attracting attention in academia and industry due to the increased usage of expensive batteries in dynamic systems such as aircraft and electric cars. The online adaptation of battery models to account for age-dependent changes in dynamics is necessary to maintain accurate estimates of the remaining system operations that can be supported under battery power. A novel method for the adaptation of parameters in an electrochemical model of a lithium- ion battery is presented here. An unscented Kalman filtering algorithm is shown to enable the production of internal battery state estimates and age-dependent electrochemical model parameter estimates using only battery current and voltage data collected over randomized discharge profiles. The use of only data collected over randomized discharge profiles distinguishes this work from other works that make use of reference discharge cycles to judge battery health. The experimental results presented here compare online model estimates produced by the proposed algorithm to offline model estimates obtained by periodically taking batteries offline to run reference discharge cycles.
Application of Unscented Kalman Filter for Condition Monitoring of an Organic Rankine Cycle Turbogenerator
Authors: Pierobon Leonardo , Schlanbusch Rune , Kandepu Rambabu , Haglind Fredrik
Published: March 2024

This work relates to a project focusing on energy optimization on offshore facilities. On oil and gas platforms it is common practice to employ gas turbines for power production. So as to increase the system performance and reduce emissions, a bottoming cycle unit can be designed with particular emphasis on compactness and reliability. In such context, organic Rankine cycle turbogenerators are a promising technology. The implementation of an organic Rankine cycle unit is thus considered for the power system of the Drau- gen offshore platform in the northern sea, which is the case study for this project. Considering the plant dynamics, it is of paramount importance to monitor the peak temperatures within the once-through boiler serving the bottoming unit to prevent the decomposition of the working fluid. This paper accordingly aims at applying the unscented Kalman filter to estimate the temperature distribution inside the primary heat exchanger by engaging a detailed and distributed model of the system and available measurements. Simulation results prove the robustness of the unscented Kalman filter with respect to process noise, measurement disturbances and initial conditions.
A Probabilistic Approach for Reliability and Life Prediction of Electronics in Drilling and Evaluation Tools
Authors: A. Kale Amit , Carter-Journet Katrina , A. Falgout Troy , Heuermann-Kuehn Ludger , Zurcher Derick
Published: March 2024

The capability to predict performance and lifetime of drilling electronics is the key to preventing costly downhole tool failures and ensuring success of any drilling operation. Drilling electronics operate under extremely harsh downhole environments with temperatures beyond 150C and vibration levels exceeding 15g. In addition to temperature and vibration, there are several factors affecting electronic reliability that have high uncertainty and cannot be accurately measured. There is a growing trend in the oil and gas industry to drill faster and operate at higher temperatures and pressures, forcing tools to operate beyond design specifications. This has resulted in increased failure rate leading to higher maintenance costs and system downtime for drilling operators as well as service providers. This paper develops a methodology to estimate the life of drilling electronics by using operational data, drilling dynamics and historical maintenance information. The methodology combines parameter estimation techniques, statistical reliability analysis and Bayesian math in a probabilistic framework. Parameter estimation is used to calibrate statistical equations to field data and probabilistic analysis is used to obtain the likelihood of failure. In the paper, the model parameters are represented as random variables, each with a probability distribution. Drilling electronics under downhole conditions can have several failure modes and each failure mode can be caused by the interaction of several variables. When information on each failure mechanism is not readily available, the failure is expressed in terms of several candidate models. Bayesian updating is used to incorporate real time operational history for a specific part and select the most accurate failure model for that part. Tis is for the first time, a systematic approach is developed for predicting the life of electronics in downhole drilling environments using statistical modeling and probabilistic methods on life cycle history and operational data from the field.
Creep Mechanisms vis-à-vis Power Law vs. Grain Boundary Sliding in α-β Titanium Alloys for Physics Based Prognostics
Authors: Kumar Amar , Srivastava Alka , Goel Nita , Banerjee Avi , K Koul Ashok
Published: March 2024

This work is performed in support of our continued physics-based prognostics system development using a life cycle management-expert system (LCM-ES) framework. The physical damage based modeling approach involving global behavior and localized response of a component at the microstructural level is used. The current research aims at constructing parts of a deformation mechanism map (DMM) for α-β Ti alloy. The appropriate constitutive equations are used for power-law creep and grain boundary sliding mechanisms. Simulations are performed using the Newton- Raphson method using Matlab software code in order to obtain contour lines corresponding to strain rates ranging from 104 to 10-12 over the homologous temperature ranges of 0.10 to 0.655. The dominance of power-law creep and grain boundary sliding over a wider range of stresses and temperatures in Ti-64 alloy is studied. The simulation results are validated using experimental data points. The predicted contour lines in the map match fairly well. The structure- creep mechanism relationships in α-β Ti alloy under different stress, temperature and strain rate conditions are discussed.
Adapting nearest neighbors-based monitoring methods to irregularly sampled measurements
Authors: M. Cec ́ılio Ineˆs , R. Ottewill James , F. Thornhill Nina
Published: March 2024

Irregularly spaced measurements are a common quality problem in real data and preclude the use of several feature ex- traction methods, which were developed for measurements with constant sampling intervals. Feature extraction methods based on nearest neighbors of embedded vectors are an example of such methods. This paper proposes the use of a time- based construction of embedded vectors and a weighted similarity metric within nearest neighbor-based methods in order to extend their applicability to irregularly sampled measurements. The proposed idea is demonstrated within a method of univariate detection of transient or spiky disturbances. The result obtained with an irregularly sampled measurement is benchmarked by the original regularly sampled measurement. Although the method was originally implemented for off-line analysis, the paper also discusses modifications to enable its online implementation.
Remaining Useful Life Prediction through Failure Probability Computation for Condition-based Prognostics
Authors: Sankararaman Shankar
Published: March 2024

The key goal in prognostics is to predict the remaining use- ful life (RUL) of engineering systems in order to guide dif- ferent types of decision-making activities such as path plan- ning, fault mitigation, etc. The remaining useful life of an engineering component/system is defined as the first future time-instant in which a set of safety threshold conditions are violated. The violation of these conditions may render the system inoperable or even lead to catastrophic failure. This paper develops a computational methodology to analyze the aforementioned set of safety threshold conditions, calculate the probability of failure, and in turn, proposes a new hy- pothesis to mathematically connect such probability to the re- maining useful life prediction. A significant advantage of the proposed methodology is that it is possible to learn important properties of the remaining useful life, without simulating the system until the occurrence of failure; this feature renders the proposed approach unique in comparison with existing direct- RUL-prediction approaches. The methodology also provides a systematic way of treating the different sources of uncer- tainty that may arise from imprecisely known future operating conditions, inaccurate state-of-health state estimates, use of imperfect models, etc. The proposed approach is developed using a model-based framework prognostics using principles of probability, and illustrated using a numerical example.
Model-based Prognostics of Hybrid Systems
Authors: Daigle Matthew , Roychoudhury Indranil , Bregon Anibal
Published: March 2024

Model-based prognostics has become a popular approach to solving the prognostics problem. However, almost all work has focused on prognostics of systems with continuous dynamics. In this paper, we extend the model-based prognostics framework to hybrid systems models that combine both continuous and discrete dynamics. In general, most systems are hybrid in nature, including those that combine physical processes with software. We generalize the model-based prognostics formulation to hybrid systems, and describe the challenges involved. We present a general approach for modeling hybrid systems and overview methods for solving estimation and prediction in hybrid systems. As a case study, we consider the problem of conflict (i.e., loss of separation) pre- diction in the National Airspace System, in which the aircraft models are hybrid dynamical systems.
Separated two-phase flow model of cryogenic loading operation
Authors: G Luchinskiy Dmitry , Ponizovskaya-Devine Ekaterina , Khasin Michael , Timucin Dogan , Sass Jarred , Perotti Jose , Brown Barbara
Published: March 2024

We present results of development of separated two-phase cryogenic flow model motivated by NASA plans to mature technology of autonomous cryogenic management on the ground and in space. The solution algorithm is based on the nearly-implicit scheme. We discuss the stability, speed, and accuracy of the algorithm in the context of applications to online health management of cryogenic loading operation. We present the results of validation of the model by comparison with the experimental data obtained during chilldown of the horizontal transfer line obtained at National Bureau of Standards and at the cryogenic testbed in Kennedy Space Center. We demonstrate a good agreement of the model predictions with the experimental data.
Experimental Approach for Estimating Mesh Stiffness in Faulty States of Rotating Gear
Authors: Park Jungho , moon Ha Jong , Oh Hyunseok , D. Youn Byeng , Park Sungho , Choi Joo-Ho
Published: March 2024

Gear mesh stiffness (GMS) is a principal factor in understanding a dynamic behavior and estimating a health condition of the gear system. Lots of methodologies have been proposed to estimate GMS in normal and abnormal states. However, most of them are performed in an analytical way, therefore experimental studies are limited. Moreover, previous experimental studies have limitations that they were only performed either in a static state or for a normal gear. In this study, we develop a methodology to estimate GMS of a rotating gear in faulty states, root crack and spalling. In the procedures, we employ transmission error (TE) which is defined as the difference between rotation of input and output gear. The methodology proposes the concepts of relative stiffness to remove the effect of low frequency component from shaft motion and variability of individual teeth, and corrected stiffness to exactly estimate GMS of cracked gear. Meanwhile, the study proposes a differentiating algorithm of gear faults between root crack and spalling considering the failure mechanisms of each fault. The developed algorithm is validated measuring the TE from a test-bed of a spur gear. Consequently, the algorithm has differentiated the gear in root crack and surface failure, and estimated the GMS of the gear in faulty states.
Early Concepts for the Coupling of a Nuclear Plant Computer to a Computerized Maintenance Management System for Autonomous Prognostic Model Development
Authors: Welz Zachary , Wesley Hines J. , R. Upadhyaya Belle
Published: March 2024

Every day large amounts of process data are recorded in a variety of industries. For nuclear power plants, these data are stored within the Plant Computer (PC). As parts begin to degrade and components fail, maintenance personnel are responsible for making repairs and recording these repairs in a Computerized Maintenance Management System (CMMS). By coupling the information in the PC and CMMS, failure data can be extracted and repurposed for lifecycle prognostic models. Existing prognostic methods can be utilized to develop lifecycle models and predict the Remaining Useful Life (RUL). These efforts are currently done manually and require substantial amounts of time to develop. This results in offline predictions, which can drastically reduce response time for preventative maintenance. This paper outlines an early concept that uses data mining based on Big Data efforts in order to couple the plant computer data with the CMMS so that prognostic information can be gathered, sorted, and analyzed automatically. The extracted failure data can be used to autonomously update or build prognostic models based on component failure times, stressor information, and signal/residual values. An effective future implementation of this concept means that the results could be used as a priori prognostic information in lifecycle prognostic models, and the updating and/or development of such models can be automated for improved response time.
Aircraft Line Maintenance Planning Based on PHM Data and Resources Availability Using Large Neighborhood Search
Authors: Olivares Loesch Vianna Wlamir , Ramos Rodrigues Leonardo , Yoneyama Takashi
Published: March 2024

Maintenance planning has become a topic of great interest among researchers and industry practitioners in recent years, since it directly impacts the availability and the lifecycle cost of systems. In the aviation industry, maintenance planning becomes even more relevant due to the high availability expectations from aircraft operators and the high costs incurred when an aircraft becomes out of service. For this reason, some minor maintenance activities are carried out near the gate, between two consecutive flight legs. These activities are referred to as aircraft line maintenance. Planning line maintenance activities is critical because a problem in the execution of line maintenance may lead to flight delays and even flight cancellations. This paper presents a methodology for aircraft line maintenance planning including both the troubleshooting tasks and the repair activities. The proposed methodology uses a Large Neighborhood Search (LNS) algorithm in order to find the most appropriated time and location to perform line maintenance activities. The algorithm considers the precedence relation between a troubleshooting task and its respective repair activity, as well as the dispachability constraints included in the MEL (Minimum Equipment List). Resources availability such as spare parts, equipments and personnel are taken into account, as well as the risk of occurrence of an AOG (Aircraft on Ground) event, estimated from PHM (Prognostics and Health Monitoring) data. An AOG event is an event that leads to a flight cancelation. The optimization goal is to minimize the Expected Cost of Repair (ECR) considering both delay and AOG expenses. A numerical example is presented to illustrate the application of the proposed methodology.
Modeling and Prediction of Criminal Activity Based on Spatio-Temporal Probabilistic Risk Functions
Authors: Flores Paulina , Vergara Mario , Fuentes Pablo , Jaramillo Francisco , Acun ̃a David , Perez Aramis , Orchard Marcos
Published: March 2024

Security forces need to model risk patterns associated with criminal activity to study cause-effect relationships and predict new crimes. In this regard, criminal risk models are important to obtain relevant information for better resource al- location and prevention of future crime activity. This paper proposes a method to model and predict future criminal activity based on spatial probabilistic risk functions and a characterization of their temporal evolution as new data become available. This method uses geo-referenced information of public services (e.g., shopping centers, banks) and criminal incidents to approximate the prior risk function as a Gaussian Mixture Model (GMM). Temporal evolution of crime activity is characterized using an algorithm that is based on Sequential Monte Carlo Methods and Importance Sampling. This algorithm incorporates information from new measurements, in a recursive manner, to approximate the posterior spatial probabilistic risk function by updating particle positions in the map. Finally, we propose a novel prediction scheme for criminal activity that uses Gaussian fields centered on hypothetical future criminal events, which are sampled from a GMM that characterizes the spatial distribution associated with recent crime activity. The optimum number of centroids for each Gaussian kernel is evaluated using Silhouette algorithm. The time index related to each hypothetical future crime event is probabilistically characterized using an exponential distribution. Results using real data show that the majority of future events occur within risk modeled zones, information which can be used for resource allocation and improvement of intervention plans.
System Interdependency Modeling in the Design of Prognostic and Health Management Systems in Smart Manufacturing
Authors: Malinowski M.L. , A. Beling P. , Y. Haimes Y. , LaViers A. , A. Marvel J. , A. Weiss B.
Published: March 2024

The fields of risk analysis and prognostics and health management (PHM) have developed in a largely independent fashion. However, both fields share a common core goal. They aspire to manage future adverse consequences associated with prospective dysfunctions of the systems under consideration due to internal or external forces. This paper describes how two prominent risk analysis theories and methodologies – Hierarchical Holographic Modeling (HHM) and Risk Filtering, Ranking, and Management (RFRM) – can be adapted to support the design of PHM systems in the context of smart manufacturing processes. Specifically, the proposed methodologies will be used to identify targets – components, subsystems, or systems – that would most benefit from a PHM system in regards to achieving the following objectives: minimizing cost, minimizing production/maintenance time, maximizing system remaining usable life (RUL), maximizing product quality, and maximizing product output.HHM is a comprehensive modeling theory and methodology that is grounded on the premise that no system can be modeled effectively from a single perspective. It can also be used as an inductive method for scenario structuring to identify emergent forced changes (EFCs) in a system. EFCs connote trends in external or internal sources of risk to a system that may adversely affect specific states of the system. An important aspect of proactive risk management includes bolstering the resilience of the system for specific EFCs by appropriately controlling the states. Risk scenarios for specific EFCs can be the basis for the design of prognostic and diagnostic systems that provide real-time predictions and recognition of scenario changes. The HHM methodology includes visual modeling techniques that can enhance stakeholders’ understanding of shared states, resources, objectives and constraints among the interdependent and interconnected subsystems of smart manufacturing systems. In risk analysis, HHM is often paired with Risk Filtering, Ranking, and Management (RFRM). The RFRM process provides the users, (e.g., technology developers, original equipment manufacturers (OEMs), technology integrators, manufacturers), with the most critical risks to the objectives, which can be used to identify the most critical components and subsystems that would most benefit from a PHM system.

A case study is presented in which HHM and RFRM are adapted for PHM in the context of an active manufacturing facility located in the United States. The methodologies help to identify the critical risks to the manufacturing process, and the major components and subsystems that would most benefit from a developed PHM system.
Direct analysis of non-quadratic phase coupling for detection of linearly modulated signals
Authors: Hernández Fidel , Iturrospe Aitzol
Published: March 2024

The detection of a linearly modulated signal is currently accomplished by applying the Bispectrum. This technique is capable of detecting quadratic phase coupled spectral components, and consequently, can be used in order to reveal a linearly modulated signal presence. However, a linear modulation by itself does not exhibit quadratic phase coupled spectral analysis. Then, the application of the Bispectrum for detecting linearly modulated signals could be unsuccessful. In this paper a general method for detection of linearly modulated signals, which can be applied whether the signals comprise quadratic phase coupled spectral components or not, is proposed. This method is evaluated through numerical simulations and it is applied for detecting a local fault in rolling element bearings. The achieved results are compared with those obtained by the traditional spectral analysis and the Bispectrum, revealing the effectiveness obtained by the application of the proposed method.
Analyzing high-dimensional thresholds for fault detection and diagnosis using active learning and Bayesian statistical modeling
Authors: He Yuning
Published: March 2024

Many Fault Detection and Diagnosis (FDD) systems use dis- crete models for fault detection and analysis. Complex indus- trial systems generally have hundreds of sensors, which are used to provide data to the FDD system. Usually, the FDD wrapper code discretizes each sensor value individually and ignores any non-linearities as well as correlations between different sensor signals. This can easily lead to overly con- servative threshold settings potentially resulting in many false alarms.

In this paper, we describe an advanced statistical framework that uses Bayesian dynamic modeling and active learning techniques to detect and characterize a threshold surface and shape in a high-dimensional space. The use of active learning techniques can drastically reduce the effort to study threshold surfaces. Automated Bayesian modeling of complex thres- hold surfaces has the potential to improve quality and perfor- mance of traditional wrapper code, which often uses hyper- cube thresholds.
Model-based Reasoning Approach for Automated Failure Analysis: An Industrial Gas Turbine Application
Authors: Mehdi Gulnar , Naderi Davood , Ceschini Giuseppe , Roshchin Mikhail
Published: March 2024

Keeping up with the technological advances, turbo- machinery industry aspires to integrate manufacturing, servicing and maintenance of their plants. Typically, these objectives may be accomplished by adoption of condition monitoring services and diagnostic solutions, resulting in improved plant operations, lower maintenance cost, and impart safety and reliability. Specifically, failure analysis, within systematic diagnostics, is a fundamental feature of design and maintenance phase, as it allows fault identification, and its causes and effects that propagate at different system levels. With the large number of subsystems and process flows, failure analysis for industrial gas turbines is non-trivial, and requires expertise of system mechanics, aerodynamics, thermodynamics, etc. Consequently, in order to realize an efficient system analysis, we device an automated model-based approach to failure analysis for industrial gas turbine applications. This paper presents context-independent qualitative models of key turbine components, which are most error-prone, together with their potential failure mode descriptions, and their impact at different system levels. Using an existing reasoning engine, we present behavior models and results for two most vulnerable turbine subsystems i.e. Lubrication Oil System and the Core Gas Turbine Engine. Finally, we evaluate the practical use-cases of this model-based solution implemented for diagnostic services at Siemens AG.
A Systems Approach To PHM
Authors: D. Mooney Thomas
Published: March 2024

The operational objectives of today’s complex, software intensive high technology platforms requires taking a systems approach to PHM. A systems approach to PHM includes the complete techno-social system involved in the equipment’s operations and maintenance - its full cycle of care. It takes a broad view of health based on wellness and is focused on optimizing the performance of all elements of the system. It provides sustaining value by enabling enterprise wide enhancements in capability. By providing timely and relevant health information for all elements in the equipment cycle of care, a well-designed PHM system can become major facilitator in maximizing the performance and resilience of all elements in the system. This systems approach has major implications for PHM system design, architecture selection, data collection, sensor selection, control system software integration, algorithm development and analytics. PHM systems that encompass the health of the full socio-technical system in which the equipment is developed, operates and supported will maximize the effectiveness of the enterprise.
Distributed Real Time Compressor Blade Health Monitoring System
Authors: Yu LiJie , Shrivastava Sachin
Published: March 2024

Compressor blades of a heavy duty industrial gas turbine need to sustain long period mechanical stress and vibration induced by high speed rotation and high pressure mass flow. High stress coupled with erosion and corrosion damage during operation is the main driver for blade cracking. Material separation of cracked rotating blade is a serious safety and reliability concern, which not only affects compressor health, but may also cause costly secondary damage at downstream. Early detection of blade anomaly and incipient crack is critical to ensure blade and compressor health and minimize service disruption. In this paper we will introduce a blade health monitoring (BHM) system developed by GE Power. BHM adopts distributed system architecture and operates continuously 24x7 to provide real time rotor blade health assessment. BHM sensors and data acquisition (DAQ) system are installed on the gas turbine to capture blade passing signals (BPS) and assess time of arrival (TOA) for each blade. Advanced signal processing algorithms process the signals locally to calculate key features that associated with blade health. Then finally, a central anomaly detection module, which is fully integrated with GE Power monitoring system, is developed to assess blade health condition and generate anomaly alarms to alert diagnostic engineer.
A Review of Photovoltaic DC Systems Prognostics and Health Management: Challenges and Opportunities
Authors: Chokor Abbas , El Asmar Mounir , Lokanath Sumanth V.
Published: March 2024

The surge in renewable electricity generation using photovoltaic (PV) systems was accompanied by an increased awareness of the fault conditions developing during the operational lifetime. Fault detection, diagnostics, and prognostics are such efforts to detect and classify a fault so the system operational expectations can be managed. Trending of the faults and prognostics also aid to evaluate expected remaining useful life so that mitigation actions can be evaluated and implemented. This paper aims to review the state of the art and practice of prognostics and health management (PHM) for the DC side of PV systems. Following a review of the PV industry current status, the study describes and classifies the different failure modes. Next, it summarizes the PV faults detection, diagnostics and prognostics approaches. A review of the PHM applications for PV systems paves the way to emphasize the key research gaps and challenges in the current practice. The available opportunities are also highlighted through a comprehensive understanding of the PV systems current performance, from where scholars and decision makers can integrate improvement strategies with promising directions for future research and practices.
An Application of Data Driven Anomaly Identification to Spacecraft Telemetry Data
Authors: Biswas Gautam , Khorasgani Hamed , Stanje Gerald , Dubey Abhishek , Deb Somnath , Ghoshal Sudipto
Published: March 2024

In this paper, we propose a mixed method for analyzing telemetry data from a robotic space mission. The idea is to first apply unsupervised learning methods to the telemetry data divided into temporal segments. The large clusters that ensue typically represent the nominal operations of the spacecraft and are not of interest from an anomaly detection viewpoint. However, the smaller clusters and outliers that result from this analysis may represent specialized modes of operation, e.g., conduct of a specialized experiment on board the spacecraft, or they may represent true anomalous or unexpected behaviors. To differentiate between specialized modes and anomalies, we employ a supervised method of consultinghuman mission experts in the approach presented in this paper. Our longer term goal is to develop more automated methods for detecting anomalies in time series data, and once anomalies are identified, use feature selection methods to build online detectors that can be used in future missions, thus contributing to making operations more effective and improving overall safety of the mission.
Bayesian Approach for the Lethargy Coefficient Estimation in the Probabilistic Creep-Fatigue Life Model
Authors: Doh Jaehyeok , Byun Junhwan , Lee Jongsoo
Published: March 2024

The researches of Prognostics and Health Management (PHM) have been important in the field of engineering. The crack is propagated by high temperature and stress in power plants, vehicle engines and etc. The defect and damage are also accumulated. Therefore, it is necessary for design of creep-fatigue life about various structures and etc. In this study, probabilistic life design based on Zhurkov life model was performed using the lethargy coefficient under the variety of temperatures and stress conditions. For this work, the integration life equation was derived using Zhurkov life model. The deterministic lethargy coefficient is calculated to using the reference of the Small Punch (SP)-Creep test and tensile-shear test data about steel material (rupture stress and rupture time). Markov Chain Monte Carlo (MCMC) sampling method based on Bayesian framework is employed for estimating the lethargy coefficient and considering its uncertainties. As a result, predicted creep-fatigue life was observed that it was considerably decreased in accordance with increasing temperature and stress conditions relatively. This life model is reasonable through comparing with conventional creep-fatigue life data.
Condition Based Monitoring for a Hydraulic Actuator
Authors: Adams Stephen , Beling Peter A. , Farinholt Kevin , Brown Nathan , Polter Sherwood , Dong Qing
Published: March 2024

In some environments where prognostics and health management would be beneficial, for example on board U.S. naval vessels, installation location and accessibility to power system must be considered. In this study, we investigate condition based maintenance and fault diagnosis for hydraulic actuators in power constrained environments. The experimental setup for collecting data is outlined, and a data set replicating multiple types of faults is collected. Several types of machine learning classifiers, including random forest and classification trees, are tested on the data set. Prediction accuracy as well as training and testing times are compared, which are used as a surrogate for power consumption in this study. We find that the random forest algorithm provides the lowest error rate of the tested classifiers but has some of the highest training and testing times. Classification trees, on the other hand, provide a better tradeoff between accuracy and computation time.
Inferential framework for autonomous cryogenic loading operations
Authors: Luchinskiy Dmitry G , Khasin Michael , Timucin Dogan , Sass Jarred , Perotti Jose , Brown Barbara
Published: March 2024

We address the problem of autonomous cryogenic management of loading operations on the ground and in space. As a step towards the solution of this problem we develop a probabilistic framework for inferring correlations parameters of two-fluid cryogenic flow. The simulation of two-phase cryogenic flow is performed using nearly-implicit scheme. A concise set of cryogenic correlations is introduced. The proposed approach is applied to an analysis of the cryogenic flow in experimental Propellant Loading System built at NASA Kennedy Space Center. An efficient simultaneous optimization of a large number of model parameters is demonstrated and a good agreement with the experimental data is obtained.
Deep Feature Learning Network for Fault Detection and Isolation
Authors: Michau Gabriel , Palm´ Thomas , Fink Olga
Published: March 2024

Prognostics and Health Management (PHM) approaches typically involve several signal processing and feature engineering steps. The state of the art on feature engineering, comprising feature extraction and feature dimensionality reduction, often only provides specific solutions for specific problems, but rarely supports transferability or generalization: it often requires expert knowledge and extensive intervention. In this paper, we propose a new integrated feature learning approach for jointly achieving fault detection and fault isolation in high-dimensional condition monitoring data. The proposed approach, based on Hierarchical Extreme Learning Machines (HELM) demonstrates a good ability to detect and isolate faults in large datasets comprising signals of different natures, non-informative signals, non-linear relationships and noise. The method includes stacked auto-encoders that are able to learn the underlying high-level features, and a one-class classifier to combine the learned features in an indicator that represents the deviation from the normal system behavior. Once a deviation is identified, features are used to isolate the most deviating signal components. Two case studies highlight the benefits of the approach: First, a synthetic dataset with the typical characteristics of condition monitoring data and different types of faults is applied to evaluate the performance with objective metrics. Second, the approach is tested on data stemming from a power plant generator interturn failure. In both cases, the results are compared to other commonly applied approaches for fault isolation.
Combination of Data-driven Feature Selection Methods with Domain Knowledge for Diagnosis of Railway Vehicles
Authors: Girstmair Bernhard , Haigermoser Andreas , Rosca Justinian
Published: March 2024

Railway vehicles are generally maintained preventively within certain time periods. Condition based predictive maintenance strategies have a great economic potential so that modern trains are equipped with many sensors in order to perform diagnostics and prognostics of components.Methods for fault detection need appropriate feature subsets in order to achieve small in-sample and out-sample errors. In our case the typical feature selection approach using pure data-driven methods is difficult, as the number of possible feature sets is very large. On the other hand there exists rich domain knowledge and detailed physical models of the mechanical system. The aim is to combine this knowledge with the often used mathematical methods for feature selection for improving classification of cases when a faulty damper is present. Based on the dynamic equations of motion, this paper presents heuristic feature selection via the analysis of transfer functions. We describe several wellknown methods of automated feature selection and a workflow which combines domain knowledge with automated methods. Results show that it is difficult to definefeatures based only on domain-knowledge, but in combination with data-driven techniques good classification performance can be achieved.
Unsupervised Deep Learning for Gear Health Monitoring
Authors: Cody Tyler , Adams Stephen , Beling Peter A.
Published: March 2024

Deep learning has revolutionized many fields in recent years by replacing expert-designed, handcrafted features with learned representations. Gear health monitoring is a field where expert-designed features are heavily used for predictive modeling. This paper investigates how unsuperviseddeep learning can be applied to gear health monitoring to make predictions on low frequency scales using high frequency data given small, sparsely labeled data sets. Deep convolutional autoencoders are trained and used to generate learned features. The learned features are compared with relevant handcrafted features via their performance in training machine learning models to predict discrete gear fatigue states. The learned features performed poorly against the handcrafted features, however models trained on feature sets tended to outperform those exclusively trained on handcrafted features. The top performing model was a multi-layer perceptron trained on both feature sets that leveraged the ability of the condition indicators to represent healthy and failure states and the ability of the learned features to represent the intermediate worn state. This work demonstrates that unsupervised deep learning techniques can be used to bolster the performance of handcrafted features in small, sparsely labeled data sets in gear health monitoring.
GPU Accelerated Prognostics
Authors: Gorospe Jr. George E. , Daigle Matthew J. , Sankararaman Shankar , Kulkarni Chetan S. , Ng Eley
Published: March 2024

Prognostic methods enable operators and maintainers to predict the future performance for critical systems. However, these methods can be computationally expensive and may need to be performed each time new information about the system becomes available. In light of these computational requirements, we have investigated the application of graphics processing units (GPUs) as a computational platform for real-time prognostics. Recent advances in GPU technology have reduced cost and increased the computational capability of these highly parallel processing units, making them more attractive for the deployment of prognostic software. We present a survey of model-based prognostic algorithms with considerations for leveraging the parallel architecture of the GPU and a case study of GPU-accelerated battery prognostics with computational performance results.
Prospective Architectures for Onboard vs Cloud-based Decision Making for Unmanned Aerial Systems
Authors: Sankararaman Shankar , Teubert Christopher
Published: March 2024

This paper investigates propsective architectures for decisionmaking in unmanned aerial systems. When these unmanned vehicles operate in urban environments, there are several sources of uncertainty that affect their behavior, and decisionmaking algorithms need to be robust to account for these different sources of uncertainty. It is important to account for several risk-factors that affect the flight of these unmanned systems, and facilitate decision-making by taking into consideration these various risk-factors. In addition, there are several technical challenges related to autonomous flight of unmanned aerial systems; these challenges include sensing, obstacle detection, path planning and navigation, trajectory generation and selection, etc. Many of these activities require significant computational power and in many situations, all of these activities need to be performed in real-time. In order to efficiently integrate these activities, it is important to develop a systematic architecture that can facilitate realtime decision-making. Four prospective architectures are discussed in this paper; on one end of the spectrum, the first architecture considers all activities/computations being performed onboard the vehicle whereas on the other end of the spectrum, the fourth and final architecture considers all activities/computations being performed in the cloud, using a new service known as “Prognostics as a Service” that is being developed at NASA Ames Research Center. The four different architectures are compared, their advantages and disadvantages are explained and conclusions are presented.
Impact of Modulation Frequencies on the Lifetime of Power Semiconductor Modules for EV Applications
Authors: Gestes Quentin , Degrenne Nicolas
Published: March 2024

Accurate lifetime estimation of power semiconductor modules is necessary in applications such as Electrical Vehicles (EV). This paper presents a new Modulation Frequency (MF) model to estimate the thermo-mechanical cycles caused by drive profiles and motor rotating speed. The paper compares lifetime estimations with conventional Low Frequency (LF) models and shows that when using conventional damage law established by power cycling experiments, the numerous lowamplitude cycles at the modulation frequency play a major role in the degradation of the power semiconductor modules.
PFsuper: Simulation-Based Prognostics to Monitor and Predict Sparse Time Series
Authors: Echauz Javier , Gardner Andrew , Curtin Ryan R. , Vasiloglou Nikolaos , Vachtsevanos George
Published: March 2024

Commercial systems for predicting remaining useful life (RUL) of serviceable parts like engine oil tend to use either generic regression models (practical, e.g., widely deployed in the automotive industry), or dynamic models for which software lags behind theory (impractical, e.g., ‘one-trick’hardcodings). We describe an arguably more realistic framework using both generic and vehicle-specific dynamic models of time-series for simulation-based condition monitoring and RUL forecasting, suitable in situations where: (a) measured time-series are sparse or slowly sampled, and (b) health condition signals tend to follow relatively simple paths (low-degree polynomial stationary trends, unit-root stochastic trends, exponential growths, quasiperiodic oscillations). This combination unlocks affordability of PFsuper, a prognostics algorithm that implements online Bayesian learning with particle filters to jointly estimate hidden condition state and optionally a handful of unknown parameters, coupled withsubsimulations characterizing failure progression and RUL probability density function. The overall method converts a generic static time-as-a-regressor model into a stochastic differential equation, then has PFsuper adapt the initially generic model into a vehicle-specific one as datameasurements arrive.
ATTENUATION OF HAEMODYNAMIC RESPONSE FOR LARYNGOSCOPY AND ENDO TRACHEAL INTUBATION WITH 2%LIGNOCAINE VISCOUS VERSUS 10% LIGNOCAINE SPRAY A COMPARITIVE STUDY
Authors: Manjusha J. Rani , Padmaja J. , Kusuma T. , Saraiah I. Bala
Published: March 2024

To compare the efcacy of 10 ml of lignocaine 2% viscous and 5 puffs of oral lignocaine10% spray in attenuation of
haemo dynamic response in terms of blood pressure and heart rate also oxygen saturation after laryngo scopy and
endotracheal intubation in 60 patients divided into two groups aged between 20 to 60 years ,ASA grade 1 and 2 undergoing various surgical
procedures under general anesthesia .experimental study reveals that both 2% lignocaine viscous and 10% lignocaine spray were effective in
attenuating the haemodynamic response but oral lignocaine viscous 2% was more effective in blunting the haemodynamic response to laryngo
scopy and endo tracheal intubation compared to 10% lignocaine spray.
ELEVATION OF SERUM CREATINE PHOSPHOKINASE LEVEL AS A MARKER FOR SEVERITY IN ACUTE ORGANOPHOSPHOROUS POISONING.
Authors: Sravanthi B Ragha , M Madhavi , G Rajini , M Bheemasenaachari
Published: March 2024

Background: Organophosphorous (OP) poisoning has reached magnitude of epidemic in developing countries like India.
Estimation of serum creatine phosphokinase (CPK) level is inexpensive. CPK levels are elevated both in acute as well as
in intermediate syndrome. Serum CPK level can be used as an economical and easily available prognostic marker for acute OP poisoning. The aim
and objective of the study is to nd out correlation between serum CPK level and severity of OP poisoning. Materials And Methods : This is a
prospective observational study conducted on 100 patients admitted with history of OP poisoning in Department of General medicine,
Government medical college and general hospital, Anantapur over a period of 6 months from February 2023 to July 2023. Serum CPK level was
measured at time of admission and correlation was studied with various outcomes. Results: This study showed statistically signicant positive
correlation between initial CPK level and severity of OP poisoning as per POP scale and atropine requirement on day 1. The cases with higher
initial CPK levels had poor outcome. Conclusion : Serum CPK level can be used as a marker in stratifying severity of acute OP poisoning.