bluuebunny/crossref_metadata_2025 huggingface dataset:
PHM for Astronauts – A New Application
Authors: Popov Alexandre , Fink Wolfgang , Hess Andrew
Published: March 2024

This paper introduces a concept and approach on bridging Prognostics and Health Management (PHM), an engineering discipline, to Space Medicine (SM) in order to mitigate the Human Health and Performance (HH&P) risks of exploration-class space missions by focusing on efforts to reduce countermeasure mass and volume and drive the risks down to an acceptable level. The paper also discusses main risks of missions such as autonomous medical care risk (i.e., mission and long-term health risk due to the inability to provide adequate medical care throughout the mission) and Behavioral Health and Performance (BH&P) risk (i.e., mission and long-term behavioral health risk). The main objective of the HH&P technologies being developed for exploration-class missions is to maintain the health of the crew and support optimal and sustained performance throughout the duration of a mission. A PHM-based technology solution augmented with predictive diagnostics capability could be the one that meets the main objective. In discussing the similarities of and differences between the PHM and SM domains, the paper explores available solutions on crew health maintenance in terms of predictive diagnostics providing early and actionable real-time warnings of impending health problems that otherwise would have gone undetected. The paper discusses the use of PHM principles and techniques with data mining capabilities to assess the value of Electronic Health Records (EHR) augmented with real-time monitoring of data for accurate predictive diagnostics on manned space exploration programs. The proposed technology concept with predictive diagnostics capability and a pilot implementation of the technology on the International Space Station (ISS) includes evaluation and augmented research/testing of the technology, which will regularly and efficiently provide advancements during the development phases.
Fatigue Crack Detection at Gearbox Spline Component using Acoustic Emission Method
Authors: Ozevin Didem , Cox Justin , Hardman William , Kessler Seth , Timmons Alan
Published: March 2024

The spline section of helicopter gearbox structure is susceptible to fatigue crack, and non-redundant characteristic leads to the need for early flaw detection strategies. Acoustic Emission (AE) method relies on propagating elastic waves due to release of energy from active flaws. The initiation of damage is identified using the features of AE waveforms such as energy, amplitude and frequency centroid. The characteristics of the AE features are influenced by sensor type, sensor location and gearbox operational conditions. In this study, the AE data was collected from a helicopter gearbox with a notched spline section and realistic operational conditions using two different AE sensors located at two different positions. The data collection was conducted over one year under various operational conditions. The AE features were extracted from long duration waveforms (100 milliseconds) at every pre- defined time step (every 5 seconds). The frequency domain features of frequency centroid and energy distribution in various frequency bands were compared with gearbox operational conditions such as torque, lift, gyroscopic moment, and temperature. The influences of sensor location, sensor type and operational conditions on the AE features are presented in order to decouple their influences from the AE features due to damage. The comparison between the predicted crack growth time using the AE data and the observed crack initiation shows that the AE method using frequency domain features of streamed waveforms has great potential to identify the crack initiation when the sensor type and location are preserved.
Case Study: Models for Detecting Low Oil Pressure Anomalies on Commercial Vehicles
Authors: E. Bussey Howard , G. Nenadic Nenad , A. Ardis Paul , G. Thurston Michael
Published: March 2024

We present a case study of anomaly detection using commercial vehicle data (from a single vehicle collected over a six-month interval) and propose a failure-event analysis. Our analysis allows performance comparison of anomaly detection models in the absence of sufficient anomalies to compute the Receiver Operating Characteristic curve.

Several heuristically-guided data-driven models were considered to capture the relationship among three main engine signals (oil pressure, temperature, and speed). These models include regression-based approaches and distance-based approaches; the former use the residual’s z-score as the detection metric, while the latter use a Mahalanobis distance or similar measure as the metric. The selected regression-based models (Boosted Regression Trees, Feed-Forward Neural Networks, and Gridded Regression tables) outperformed the selected distance-based approaches (Gaussian Mixtures and Replicator Neural Networks). Both groups of models were superior to existing Diagnostic Trouble Codes. The Gridded Regression tables and Boosted Regression Trees exhibited the best overall metric performance.

We report a surprising behavior of one of the models: locally- optimal Gaussian Mixture Models often had zero detection performance, with such models occurring in at least 25% of the iterations with seven or more Gaussians in the mixture. To overcome the problem, we propose a regularization method that employs a heuristic filter for rejecting Gaussian Mixtures with non-discriminative components.
Efficient Dependency Computation for Dynamic Hybrid Bayesian Network in On-line System Health Management Applications
Authors: Iamsumang Chonlagarn , Mosleh Ali , Modarres Mohammad
Published: March 2024

This paper presents a new dependency computational algorithm for reliability inference with dynamic hybrid Bayesian network. It features a component-based algorithm and structure to represent complex engineering systems characterized by discrete functional states (including degraded states), and models of underlying physics of failure, with continuous variables. The methodology is designed to be flexible and intuitive, and scalable from small localized functionality to large complex dynamic systems. Markov Chain Monte Carlo (MCMC) inference is optimized using pre-computation and dynamic programming for real-time monitoring of system health. The scope of this research includes new modeling approach, computation algorithm, and an example application for on- line System Health Management.
Uncertainty in Steady-State Diagnostics of a Current-Pressure Transducer: How Confident are We in Diagnosing Faults?
Authors: Sankararaman Shankar , Teubert Christopher , Goebel Kai
Published: March 2024

Current-Pressure (I/P) transducers are effective pressure regulators that can vary the output pressure depending on the supplied electrical current signal, and are commonly used in pneumatic actuators and valves. Faults in current-pressure transducers have a significant impact on the regulation mechanism, and therefore, it is important to perform diagnosis to identify such faults. However, there are different sources of uncertainty that significantly affect the diagnostics procedure, and therefore, it may not be possible to perform fault diagnosis and prognosis accurately, with complete confidence. These sources of uncertainty include natural variability, sensor errors (gain, bias, noise), model uncertainty, etc. This paper presents a computational methodology to quantify the uncertainty and thereby estimate the confidence in the fault diagnosis of a current-pressure transducer. First, experiments are conducted to study the nominal and off-nominal behavior of the I/P transducer; however, sensor measurements are not fast enough to capture brief transient states that are indicative of wear, and hence, steady-state measurements are directly used for fault diagnosis. Second, the results of these experiments are used to train a Gaussian process model using machine learning principles. Finally, a Bayesian inference methodology is developed to quantify the uncertainty and assess the confidence in fault diagnosis by systematically accounting for the aforementioned sources of uncertainty.
Integrated Multivariate Health Monitoring System for Helicopters Main Rotor Drives: Development and Validation with In-Service Data
Authors: Bellazzi Alberto , Jacazio Giovanni , Maino Bruno , Mihaylov Gueorgui , Pellerey Franco , Sorli Massimo
Published: March 2024

The implementation into service of accelerometric health monitoring systems of mechanical power drives on helicopters has shown that the generation of false failure alarms is a critical issue. The paper presents a combined application of several multivariate statistical techniques and shows how a monitoring method which integrates these tools can be successfully exploited in order to improve the reliability of the diagnos- tic systems. The first phase of the research activity was ad- dressed to exploring the potential advantages of using multi- variate classification/discrimination/anomaly detection methods on real world accelerometric condition monitoring data. The second phase consisted of an implementation into actual service of an innovative integrated multivariate health monitoring system.
Fault Diagnosis and Prognosis Based on Lebesgue Sampling
Authors: Zhang Bin , Wang Xiaofeng
Published: March 2024

Traditional fault diagnosis and prognosis (FDP) approaches are based on periodic sampling, i.e. samples are taken and algorithms are executed both in a periodic manner. As the volume of sensor data and complexity of algorithms keep increasing, the bottleneck of FDP is mainly the limited computational resources, which is especially true for distributed applications where FDP functions are deployed on microcontrollers and embedded systems with limited computation re- sources. This paper introduces the concept of Lebesgue sampling in FDP and proposes a Lebesgue sampling based fault diagnosis and prognosis (LS-FDP) framework. In the proposed LS-FDP, a novel diagnostic philosophy of “execution only when necessary” is developed in computation cost re- duction and uncertainty management. For prognosis, different from traditional approaches in which the prognostic horizon is on the time axis, the proposed approach defines prognostic horizon on the state axis. With a reduced prognostic horizon, the LS-FDP naturally benefits the uncertainty management. The goal is to create the fundamental knowledge for LS-FDP solutions that are cost-efficient, capable for the de- ployment on systems with limited computation sources, and supportive to the trend of distributed FDP schemes in complex systems. The design and implementation of LS-FDP based on particle filtering algorithms are presented with experimental results to verify the effectiveness of the proposed approaches.
Analysis Quality Index - What confidence do you have in your risk analysis?
Authors: Salhi Leila , Stecki Jacek , Stecki Chris
Published: March 2024

OEMs and operators of complex mission/safety critical systems are faced with the requirement to mitigate design and performance risks and their economic consequences. A key issue for any engineering organization is the integrity of the analysis that is used to support significant commercial decisions. Analysis outputs used to establish or validate performance criteria should have an appropriately high level of confidence associated with them when entering into significant financial contracts. While risk assessment methods and techniques for analysis are well defined and understood and are captured in various international military and commercial standards, the issue of analysis quality has traditionally been neglected and is not adequately covered in most commercially available engineering analysis tools. The quality of data inputs determines the quality of analysis outputs. A key factor is the source of the parameters used in an analysis. For example input data may be sourced from operational data, or may be based on the engineering judgement of an individual or a third party organization. This paper outlines an approach to analysis quality assessment in a model based engineering environment, focusing on the sources of data and ancillary information to generate an Analysis Quality Index (AQI) for the analysis. The AQI is generated as a dashboard reporting function for the engineering model that is used to provide a confidence rating on the analysis outputs. Analysis Quality Index capability was incorporated into Maintenance Aware Design environment (MADe) software, an integrated tool-set that combines engineering risk analysis capabilities to support systems engineering, design and through-life support.
Challenges in Concrete Structures Health Monitoring
Authors: Mahadevan Sankaran , Adams Douglas , Kosson David
Published: March 2024

Structural health monitoring needs to produce actionable information regarding structural integrity that supports operational and maintenance decision making that is individualized for a given structure and its performance objectives. An effective Prognostics and Health Management (PHM) framework for aging structures (subjected to physical, chemical, environmental, and mechanical degradation) needs to integrate four elements – damage modeling, monitoring, data analytics, and uncertainty quantification. This paper briefly discusses available techniques and ongoing challenges in each of these four elements of PHM, in the context of concrete structures. A Bayesian network approach is discussed for integrating heterogeneous information from multi-physics computational models of degradation processes, full-field measurement techniques, big data analytics, and various data and model uncertainty sources. Such a comprehensive framework can quantitatively support decisions regarding appropriate risk management actions.
Enhanced Trajectory Based Similarity Prediction with Uncertainty Quantification
Authors: Lam Jack , Sankararaman Shankar , Stewart Bryan
Published: March 2024

Today, data driven prognostics acquires historic data to generate degradation path and estimate the Remaining Useful Life (RUL) of a system. A successful methodology, Trajectory Similarity Based Prediction (TSBP) that details the process of predicting the system RUL and evaluating the performance metrics of the estimate was proposed in 2008. Two essential components of TSBP identified for potential improvement include 1) a distance or similarity measure that is capable of determining which degradation model the testing data is most similar to and 2) computation of uncertainty in the remaining useful life prediction, instead of a point estimate. In this paper, the Trajectory Based Similarity Prediction approach is evaluated to include Similarity Linear Regression (SLR) based on Pearson Correlation and Dynamic Time Warping (DTW) for determining the degradation models that are most similar to the testing data. A computational approach for uncertainty quantification is implemented using the principle of weighted kernel density estimation in order to quantify the uncertainty in the remaining useful life prediction. The revised approach is measured against the same dataset and performance metrics evaluation method used in the original TBSP approach. The result is documented and discussed in the paper. Future research is expected to augment TSBP methodology with higher accuracy and stronger anticipation of uncertainty quantification.
Aircraft Hydraulic System Leakage Detection and Servicing Recommendations Method
Authors: O. L. Vianna Wlamir , Pedro P. Malere João
Published: March 2024

Aircraft hydraulic systems are composed of several components connected and distributed along the aircraft. Monitoring leakage of these components are time-consuming tasks, and often cover only some parts of the system. The objective of this work is to present a method to estimate hydraulic leakage and recommend maintenance and servicing tasks using aircraft standard sensors such as fluid temperature and reservoir level.The proposed method was tested using several aircraft operating data with different levels of degradation (external leakages) and the results were analyzed in order to evaluate its precision on estimating leakage. Results showed the capability to detect leakage although uncertainties must be considered when evaluating maintenance interventions.
Speed Estimation in Geared Wind Turbines Using the Maximum Correlation Coefficient
Authors: Alexandros Skrimpas Georgios , S. Marhadi Kun , Bech Jensen Bogi , Walsted Sweeney Christian , Mijatovic Nenad , Holboell Joachim
Published: March 2024

Valid speed signal is essential for proper condition monitoring of modern variable speed wind turbines. Traditionally, a tachometer mounted on the high speed shaft provides reference for tracking speed dependant frequency components, such as generator speed harmonics and gearbox tooth mesh frequencies. The health assessment of drive train components is limited to broadband measurements when the speed signal is invalid. This condition results in reduced fault detection capabilities and consequently decreased lead time. In this work, a new speed estimation algorithm is presented in order to overcome the above mentioned issues. The high speed stage shaft angular velocity is calculated based on the maximum correlation coefficient between the 1st gear mesh frequency of the last gearbox stage and a pure sinus tone of known frequency and phase. The proposed algorithm utilizes vibration signals from two accelerometers for cross-referencing purposes. The method is tested in three drive train configurations, where 720 sets of vibration signals of 10.24s length, sampled at 25.6kHz are analysed. Consistent speed estimation reaches approximately 98% when two vibration sources are utilized, whereas it is lower when only one source is taken into account. No apparent patterns arise between speed variation levels or power production and the number of invalid outputs, showing the independence of the method from operational parameters.
From Theory to Practice: Model-Based Diagnosis in Industrial Applications
Authors: Koitz Roxane , Wotawa Franz
Published: March 2024

Due to the increasing complexity of technical systems, accurate fault identification is crucial in order to reduce maintenance costs and system downtime. Model-based diagnosis has been proposed as an approach to improve fault localization. By utilizing a system model, possible causes, i.e. defects, for observable anomalies can be computed. Even though model-based diagnosis rests on solid theoretical background, it has not been widely adopted in practice. The reasons are twofold: on the one hand it requires an initial modeling effort and on the other hand a high computational complexity is associated with the diagnosis task in general. In this paper we address these issues by proposing a process for abductive model-based diagnosis in an industrial setting. Suitable models are created automatically from failure assessments available. Further, the compiled system descriptions reside within a tractable space of abductive diagnosis. In or- der to convey the feasibility of the approach we present results of an empirical evaluation based on several failure assessments.
Cost-Wise Readiness Enabled Through Condition Based Maintenance Plus (CBM+)
Authors: Kennedy Josh , Carter Jim , Carter Matt
Published: March 2024

As Department of Defense (DoD) budgets continue to decrease through automatic spending cuts, Army Commands are pressured to develop, implement and manage new ways to reduce spending. The high cost of operation and sustainment (O&S) associated with the helicopters required to support the US Army’s global presence significantly increases this pressure. Reducing costs within O&S activities, while managing operational readiness is achieved through Cost Wise Readiness (CWR) initiatives. Goals and objectives are to increase efficiencies, thereby increasing the value of each budgeted dollar. Even as the budgetary environment becomes more challenging, the purpose of Army maintenance remains unchanged—to generate combat power. In support of continuing this capability, Army Aviation is leading the way with ongoing efforts to implement, measure and communicate efficiencies leading to benefits. The AMCOM Logistics Center (ALC) functions as the logistics component of the US Army’s Aviation and Missile Life Cycle Management Command (AMCOM) headquartered at Redstone Arsenal, Alabama. The ALC develops, acquires, fields and sustains logistics support for Army Aviation and Missile systems and associated support equipment to ensure weapon system readiness in any operation worldwide. The ALC, in support of Program Executive Offices, Project Managers, Army Depots, and partnering with industry are dedicated to provide real-time logistics support to the Soldier, Airman and Marine in training and combat. The ALC is dedicated to the development and implementation of CWR initiatives through the identification and pursuit of opportunities and investment in projects focused on reducing cost. Multiple Army offices have been instrumental in the development of technological capabilities in support of the CWR mission. One such high-tech capability includes the integration of systems which incorporate Condition Based Maintenance Plus (CBM+) into the management of logistics and airworthiness aspects of the Army’s helicopter fleets. Managing costs within O&S activities is achievable through the remediation of maintenance enabled through CBM+ initiatives.
Linear Temporal Logic (LTL) Based Monitoring of Smart Manufacturing Systems
Authors: Heddy Gerald , Huzaifa Umer , Beling Peter , Haimes Yacov , Marvel Jeremy , Weiss Brian , LaViers Amy
Published: March 2024

The vision of Smart Manufacturing Systems (SMS) includes collaborative robots that can adapt to a range of scenarios. This vision requires a classification of multiple system behaviors, or sequences of movement, that can achieve the same high-level tasks. Likewise, this vision presents unique challenges regarding the management of environmental variables in concert with discrete, logic-based programming. Overcoming these challenges requires targeted performance and health monitoring of both the logical controller and the physical components of the robotic system. Prognostics and health management (PHM) defines a field of techniques and methods that enable condition-monitoring, diagnostics, and prognostics of physical elements, functional processes, overall systems, etc. PHM is warranted in this effort given that the controller is vulnerable to program changes, which propagate in unexpected ways, logical runtime exceptions, sensor failure, and even bit rot. The physical component’s health is affected by the wear and tear experienced by machines constantly in motion. The controller’s source of faults is inherently discrete, while the latter occurs in a manner that builds up continuously over time. Such a disconnect poses unique challenges for PHM. This paper presents a robotic monitoring system that captures and resolves this disconnect. This effort leverages supervisory robotic control and model checking with linear temporal logic (LTL), presenting them as a novel monitoring system for PHM. This methodology has been demonstrated in a MATLAB-based simulator for an industry inspired use-case in the context of PHM. Future work will use the methodology to develop adaptive, intelligent con- trol strategies to evenly distribute wear on the joints of the robotic arms, maximizing the life of the system.
Analysis of Electrolytic Capacitor Degradation under Electrical Overstress for Prognostic Studies
Authors: Renwick Jason , S. Kulkarni Chetan , R. Celaya Jose ́
Published: March 2024

The implementation of prognostics methodologies to electrical and electronics components and systems has become essential and critical as these systems find more prominence recently as they replace traditional systems in several critical applications. There are several challenges due to the great variety of components used in a system, the continuous development of new electronics technologies, and a general lack of understanding of how electronics fail. Traditional reliability techniques in electronics tend to focus on understanding the time to failure for a batch of components of the same type. In this work, we discuss degradation in electrolytic capacitors which are part of power supplies and are very crucial in their operation. We discuss our experimental setup and further present our findings related to the degradation observed in these capacitors under accelerated electrical aging under different operating conditions. The understanding of the time-dependent degradation process is critical for the development of model-based prognostics algorithms which provide a continuous condition-based estimation of the remaining useful life of the device under test
Quadrotor Accelerometer and Gyroscope Sensor Fault Diagnosis with Experimental Results
Authors: C Avram Remus , Zhang Xiaodong , Muse Jonathan
Published: March 2024

This paper presents the design and real-time experimental results of a fault diagnosis scheme for inertial measurement unit (IMU) measurements of quadrotor unmanned air vehicles (UAVs). The objective is to detect, isolate, and estimate sensor bias fault in accelerometer and gyroscope measurements. Based on the quadrotor dynamics and sensor models under consideration, the effects of sensor faults are represented as virtual actuator faults in the quadrotor state equations. Two nonlinear diagnostic estimators are designed to provide structured residuals enabling the simultaneous detection and isolation of the sensor faults. Additionally, based on the detection and isolation scheme, two nonlinear adaptive estimators are employed for the estimation of the fault magnitude. The performance of the diagnosis method is evaluated and demonstrated through real-time flight experiments.
Vibration-based Fault Diagnostics of Planetary Gearbox Using Time Synchronous Averaging with Multiple Window Functions
Authors: M. Ha Jong , Park Jungho , Oh Hyunseok , D. Youn Byeng
Published: March 2024

Recently, numerous number of studies have been made to advance TSA for vibration-based diagnostics of planet gears of a planetary gearbox. To increase signal-to-noise ratio of the vibration signals, various narrow-range window functions centered on sensor’s position have been developed to capture the instances when the planet gears are adjacent to the sensor. However, TSA with such narrow-range window functions is unable to detect the abnormal signal if it is amplified at outside of the sensor’s position due to an unexpected vibration modulation characteristics of the gearbox. This paper proposes a TSA which is robust toward the unexpected vibration modulation characteristics of the gearbox. Multiple narrow-range window functions were employed to perform multiple TSAs rather than employing the sole window function centered on the sensor’s position. Condition indicators with regard to every ring gear’s teeth were derived, and accumulated for the purpose of condition monitoring. Then, optimal position of the window function was determined to maximize capability to detect signals from the faulty gear. For demonstration of the proposed TSA, test was performed with a 2kW testbed having one-stage planetary gearbox within which a planet gear with artificial fault was assembled.
Prognostics and Health Management of an Electro-Hydraulic Servo Actuator
Authors: Mornacchi Andrea , Jacazio Giovanni , Jacazio Giovanni
Published: March 2024

Electro-Hydraulic Servo Actuators (EHSA) is the principal technology used for primary flight control in new aircrafts and legacy platforms. The development of Prognostic and Health Management technologies and their application to EHSA systems is of great interest in both the aerospace industry and the air fleet operators.

This paper presents the results of an ongoing research activity focused on the development of a PHM system for fly-by-wire primary flight EHSA. One of the key features of the research is the implementation of a PHM system without the addition of new sensors, taking advantage of sensing and information already available. This choice allows extending the PHM capability to the EHSAs of legacy platforms and not only to new aircrafts. The enabling technologies borrow from the area of Bayesian estimation theory and specifically particle filtering and the information acquired from EHSA during pre-flight check is processed by appropriate algorithms in order to obtain relevant features, detect the degradation and estimate the Remaining Useful Life (RUL). The results are evaluated through appropriate metrics in order to assess the performance and effectiveness of the implemented PHM system
Failure Prognostics with Missing Data Using Extended Kalman Filter
Authors: Vianna Wlamir Olivares Loesch , Yoneyama Takashi
Published: March 2024

Failure prognostics can provide benefits in operation and maintenance of equipments by predicting when the component is going to fail and consequently acting at the most appropriate time. In several situations degradation estimations are sparse or missing estimations are present at collected data. Considering these situations, a failure prognostics method was proposed considering the usage of the extended version of the Kalman filter. This method was analyzed with hydraulic system reservoir levels indication collected from four different aircrafts. In this study a prognostic model was estimated by the filter and then future values of hydraulic level as well as the remaining useful life interval were obtained considering a set of Monte Carlo simulations and a failure probability distribution approximation. Results evidenced the benefit of this method to properly prognose the system.
Reciprocating compressor valve condition monitoring using image-based pattern recognition
Authors: Trout John N. , Kolodziej Jason R.
Published: March 2024

This work presents the development of a vibration-based condition monitoring method for early detection and classification of valve wear within industrial reciprocating compressors through the combined use of time-frequency analysis with image-based pattern recognition techniques. A common valve related fault condition is valve seat wear that is caused by repeated impact and accentuated by chatter. Seeded faults consistent with valve seat wear are introduced on the crankside discharge valves of a Dresser-Rand ESH-1 industrial compressor. A variety of operational data including vibration, cylinder pressure, and crank shaft position are collected and processed using a time-frequency domain approach. The resulting diagrams are processed as images with features extracted using 1st and 2nd order image statistics. A Bayesian classification strategy is employed with accuracy rates greater than 90% achieved using two and three-dimensional features spaces.
Leakage Detection of Steam Boiler Tube in Thermal Power Plant Using Principal Component Analysis
Authors: Yu Jungwon , Jang Jaeyel , Yoo Jaeyeong , Park June Ho , Kim Sungshin
Published: March 2024

Tube leakage of steam boiler can decrease the whole efficiency of power plant cycle, and eventually cause an unscheduled shutdown. In this paper, we propose a leakage detection method for steam boiler tubes in thermal power plant (TPP) using principal component analysis and exponentially weighted moving average (EWMA). To determine the number of principal components, the cumulative percent variance technique is employed, and the Q statistic is used as the detection index. If the Q statistic of an unseen sample is larger than a predefined threshold value, the sample is detected as a fault sample and an alarm signal is generated. EWMA is used to reduce false alarms. To demonstrate the performance, we apply the proposed method to an unplanned shutdown case due to boiler tube leakage, which is collected from distributed control systems of 250 MW coal-fired TPP. The experiment results show that the proposed method can detect failure symptoms of the case successfully.
Probabilistic Prognosis of Non-Planar Fatigue Crack Growth
Authors: Leser Patrick E. , Newman John A. , Warner James E. , Leser William P. , Hochhalter Jacob D. , Yuan Fuh-Gwo
Published: March 2024

Quantifying the uncertainty in model parameters for the purpose of damage prognosis can be accomplished utilizing Bayesian inference and damage diagnosis techniques such as non-destructive evaluation or structural health monitoring. The number of samples required to solve the Bayesian inverse problem through common sampling techniques (e.g., Markov chain Monte Carlo) renders high-fidelity finite element-baseddamage growth models unusable due to prohibitive computation times. However, these types of models are often the only option when attempting to model complex damage growth in real-world structures. Here, a recently developed highfidelity fatigue crack growth model is used which, when compared to finite element-based modeling, has demonstrated reductions in computation times of three orders of magnitude through the use of surrogate models and machine learning. A probabilistic prognosis framework incorporating this model is developed and demonstrated for non-planar crack growth in a modified edge-notched aluminum tensile specimen. Predictions of remaining useful life are made over time for fiveupdates of the damage diagnosis data, and prognostic metrics are utilized to evaluate the performance of the prognostic framework. Challenges specific to the probabilistic prognosis of non-planar fatigue crack growth are highlighted and discussed in the context of the experimental results.
Critical Components Selection for a Prognostics and Health Management System Design: an Application to an Overhead Contact System
Authors: Brahimi Mehdi , Medjaher Kamal , Leouatni Mohammed , Zerhouni Noureddine
Published: March 2024

In recent years, improving quality of rail services by increasing availability, saving energy, and cutting the costs of infrastructure and rolling stock maintenance has become a central concern in the railway industry. Furthermore, considerable research efforts have been devoted to developmonitoring and health management solutions for the rail transportation systems. Streaming data from trains, infrastructure and signaling systems became a key subject for an implementation of a predictive maintenance. Prognostics and Health Management (PHM) is an approach that aims to support a predictive maintenance program. Basically, the first step to develop a PHM system is to identify critical components. This paper emphasizes on the critical components selection step. It presents a methodology to identify the critical components for the design of a PHM solution. The proposed methodology is based on objectives definition for PHM and it is applied to an Overhead Contact System (OCS).
A New Prognostics Approach for Bearing based on Entropy Decrease and Comparison with existing Methods
Authors: Kim Seokgoo , Park Sungho , Kim Ju-Won , Han Junghwa , An Dawn , Kim Nam Ho , Choi Joo-Ho
Published: March 2024

In this paper, a new method is proposed for the bearing prognosis based on the energy entropy, in which the normalized energy in the frequency spectrum is calculated over the cycles, frequency band is selected that shows greater decrease relative to the others, and entropy is computed as a trending feature. As opposed to the traditional features, which exhibit noisy fluctuation, non-monotonic change or only an abrupt increase near the end of life, the proposed energy entropy shows the smooth and constant decrease over the cycles which may represent the degree of fault progression. In order to illustrate the advantage, four traditional features - RMS, kurtosis, MAS kurtosis and envelope and the new feature - energy entropy are examined and compared using the three cases of bearing data named FEMTO, IMS and LOCAL, all from the bearing life test.
Inertial Measurement Unit for On-Machine Diagnostics of Machine Tool Linear Axes
Authors: Vogl Gregory W. , Donmez M. Alkan , Archenti Andreas , Weiss Brian A.
Published: March 2024

Machine tools degrade during operations, yet knowledge of degradation is elusive; accurately detecting degradation of machines’ components such as linear axes is typically a manual and time-consuming process. Thus, manufacturers need automated, efficient, and robust methods to diagnose the condition of their machine tool linear axes with minimal disruptions to production. Towards this end, a method was developed to use data from an inertial measurement unit (IMU) for identification of changes in the translational and angular errors due to axis degradation. The IMU-based method uses data from accelerometers and rate gyroscopes to identify changes in linear and angular errors due to axis degradation. A linear axis testbed, established for the purpose of verification and validation, revealed that the IMU-based method was capable of measuring geometric errors with acceptable test uncertainty ratios. Specifically, comparison of the IMU-based and laser-based results demonstrate that the IMU-based method is capable of detecting micrometer-level and microradian-level degradation of linear axes. Consequently, an IMU was created for application of the IMU-based method on a machine tool as a proof of concept for detection of linear axis error motions. If the data collection and analysis are integrated within a machine controller, the process may be streamlined for the optimization of maintenance activities and scheduling, supporting more intelligent decision-making by manufacturing personnel and the development of self-diagnosing smart machine tools.
Anomaly Detection and Fault Disambiguation in Large Flight Data: A Multi-modal Deep Auto-encoder Approach
Authors: Reddy Kishore K. , Sarkar Soumalya , Venugopalan Vivek , Giering Michael
Published: March 2024

Flight data recorders provide large volumes of heterogeneous data from arrays of sensors on-board to perform fault diagnosis. Challenges such as large data volumes, lack of labeled data, and increasing numbers of sensors (multiple modalities) exacerbate the challenges of being able to hand-craft the features needed for state-of-the-art PHM algorithms to effectively perform system diagnosis. In this paper, the authors propose leveraging existing unsupervised learning methods based on Deep Auto-encoders (DAE) on raw time series data from multiple sensors to build a robust model for anomaly detection. The anomaly detection algorithm analyzes the reconstruction error of a DAE trained on nominal data scenarios. The reconstruction error of individual sensors is examined to perform fault disambiguation. Training and validation are conducted in a laboratory setting for various operating conditions. The proposed framework does not need any hand-crafted features and uses raw time series data. Our approach is tested on data from the NASA open database and demonstrates high fault detection rates (~ 97:8%) with zero false alarms. Our paper also demonstrates robust fault disambiguation on two different fault scenarios. Moreover, the paper provides a strong rationale for utilizing deep architecture (multi-hidden-layer neural network) via thorough comparison with a single hidden-layer DAE.
Case Study of a Faulted Planet Bearing
Authors: Bechhoefer Eric , He Dave
Published: March 2024

Fault detection in planet bearings is difficult. This is particularly true in wind turbines, where the main rotor shaft is under 20 rpm, such that the planet fault frequency can be sub 1 Hz. This papers analyzes a missed fault on a wind turbine planet bearing, and discuses how changes in the analysis configuration then allowed this type of fault to be detected. Raw data from ten machines was collected. From this, a strategy for fault feature identification was developed, to include: the evaluation of window selection, biasing of the data set with faulted components, and the use of improve analysis techniques. This allowed meaningful and appropriate thresholds to be set.
Remaining Useful Life Predictions in Lithium-ion Battery under Composite Condition
Authors: Kim Yejin , Lee Jongsoo
Published: March 2024

In these days, there is a tendency that research of Prognostics and Health Management (PHM) of lithium-ion battery that prevent accidents in advance by predicting the Remaining Useful Life (RUL). However, there is a difficulty in battery evaluation for composite condition of an operating conditions and a storage conditions, due to the time consuming. Research on the RUL of lithium-ion battery in composite condition are progressing by combining an operating condition and a storage condition. Conventional method such as Miner’s Rule may not fully meet the needs of battery evaluation for RUL. Because it does not take into account overloads caused by a variable amplitude loading history. In order to solve the problem of accurately predicting the RUL of lithium-ion battery, two approaches applied to predicting the RUL of lithium-ion battery. We demonstrate the usefulness of two proposed methods by comparing with real-data of composite condition.
Deep Learning Based Diagnostics of Orbit Patterns in Rotating Machinery
Authors: Jeong Haedong , Woo Sunhee , Kim Suhyun , Park Seungtae , Kim Heechang , Lee Seungchul
Published: March 2024

Vibration-based orbit analysis has been employed as a powerful tool in diagnosing the operating state for rotating machinery in power plants. However, due to the difficulties of extracting mathematical features for data-driven approaches in the orbit analysis, it heavily depends on the expert knowledge or experience. In this paper, the deep learning algorithm in machine learning is used to develop autonomous orbit pattern recognition. In details, the convolutional neural network is implemented to build up weights between convolution kernels and pixels, and to construct the entire structure of the neural networks. Finally, the trained network enables us to classify the shapes of the orbit via orbit shape images and its result can estimate fault modes of the rotating machinery. The proposed framework is demonstrated with a rotating testbed.
Autonomous Operations System: Development and Application
Authors: Medina Jaime A. Toro , Wilkins Kim N. , Walker Mark , Stahl Gerald M.
Published: March 2024

Autonomous control systems provide the ability of self-governance beyond the conventional control system. As the complexity of systems increases, there is a natural drive for developing robust control systems to manage complicated and emergency operations. By closing the bridge between conventional automated systems and knowledge based self-aware systems, nominal control of operations can evolve into relying on safety critical mitigation processes to support any off-nominal behavior. Current research and development efforts lead by the Autonomous Propellant Loading (APL) project at NASA Kennedy Space Center aims to improve cryogenic propellant transfer operations by developing an automated control and health monitoring system. As an integrated system, the center aims to produce an Autonomous Operations System (AOS) capable of integrating health management operations with automated control to produce a fully autonomous system.
Low Computation Acoustic Emissions Structural Health Monitoring Through Analog Signal Pre-Processing
Authors: Schlanbusch Rune , Bechhoefer Eric , Meyer Thomas J. J.
Published: March 2024

In this research an innovative acoustic emission sensing system has been developed intended for structural fatigue crack monitoring. The innovation lies in analog pre-processing of the detected acoustic emissions for signal enveloping, thus relying on cheap high bandwidth components. The technique is based on heterodyning the amplified acoustic emission signal with a carrier signal of a chosen frequency. Next, the signal is filtered and the signal envelope is obtained by phase shifting the signal by pi/2 creating an analytic signal that is digitally sampled. This results in need of low sampling rate digital acquisition equipment giving relatively small amounts of data to be processed and stored, considerably reducing the system cost. This is particularly suitable for applications involving large or complex structures to be monitored, where a multitude of sensors are needed. The system was built and tested on aluminum test coupons during tension-tension fatigue. The envelope signal is filtered for background noise through thresholding based on statistical knowledge of the noise distribution. The accumulation of acoustic activity shows promising results with an early period of high acoustic activity during settling of the material that asymptotically converges at a certain level. At the next stage, there is no activity until a certain point is reached where a sudden ramp up of AE is detected close to the end of the experiment. Throughextensometer measurement, the change in coupon length at this point in time strongly indicates that the ramp up of AE activity is due to crack initiation and propagation.
Steps toward prognostics of faults in bearings
Authors: Madar Eyal , Kogan Gideon , Gazizulin Dmitri , Klein Renata , Bortman Jacob
Published: March 2024

Assessment of the remaining useful life of a rolling-element bearing is a key element in rotating machines prognostics. Evaluation of the bearing remaining useful life (RUL) requires diagnosis of the fault existence, estimation of its size and estimation of the time interval until it reaches a critical size. A concept for bearing RUL estimation is proposed. The main insights which led to the concept development are reviewed. The study focuses on estimation of spall size located in one of the bearing races. A new approach for estimation of spall size in bearing races is developed based on physical insights obtained from results of a general bearing dynamic model. Analytical modeling of the interaction between the spall and the rolling element enables the development of an autonomous generic method for spall size estimation. In this paper the principles for spall size estimation are described. The new method was applied to experimental data including different spall sizes on inner and outer races. The estimation shows satisfactory results with errors up to 20%.
Health-Informed Uncertainty Quantifications via Bayesian Filters with Markov Chain Monte Carlo Simulations for Fatigue Critical Rotorcraft Components
Authors: Shiao Michael , Ghoshal Anindya
Published: March 2024

This paper presents the applications of Bayesian-filters (BF) with Markov chain Monte Carlo (MCMC) simulations for probabilistic lifing assessment of aircraft fatigue critical components. Uncertainties in damage growth parameters are updated with new information obtained through structuralhealth monitoring (SHM) systems and the remaining useful life (RUL) are predicted. State transition function representing virtual damage growth of a component and measurement function representing the SHM measurements of the component are defined. State transition function is described by a typical Paris equation for fatigue crack propagation. In the equation, the initial crack size and crack growth rate are updated by incoming SHM measurements. Measurement functions are assumed in this study which describe the relationship between the damage features derived from SHM signals and the damage sizes. Damage tolerance (DT) and risk-based remaining useful life of fatigue critical structural components are determined at various reliability levels. The variabilities of RUL are also quantified for various magnitudes of random measurement noise and various measurement frequencies. It is found that the variability of the RUL is proportional to that of the measurement noise. In addition, more frequent measurements will result in less variability in RUL.
Early Warnings for failing Train Axle Bearings based on Temperature
Authors: Peters M.F.E.
Published: March 2024

By studying the temperature behavior of axle bearings both statistically and physically, the research and development (R&D) department of Netherlands Railways (NS) has successfully developed and implemented a health monitoring system for bearings. In an early stage of degradation, temperature deviations are detected and the level of severity of the degradation is identified through a decision tree. This method enables us to detect bearing failures one to three months earlier than any other method in use, in more than half of the cases. Different handling scenarios per type of temperature behavior have been designed in a way that minimizes impact on train service.