bluuebunny/crossref_metadata_2025 huggingface dataset:
Complex System Fault Detection Using Factor Analysis
Authors: Zhang Yilu
Published: March 2024

In this paper, we propose to use a data modeling technology, Factor Analysis, in the application of complex system fault diagnosis and failure prognosis. Factor Analysis captures the dominant dependency underlying observable measurements of physical systems, and is sensitive to their changes, as demonstrated by the preliminary experimental results on two real-world datasets. Comparison studies show that Factor Analysis has advantages over two related techniques,Principal Component Analysis and K-Means.
An Autonomous Wireless Sensing Module for Long-Term Structural Health Monitoring Applications
Authors: Belov Nickolai , Tchelepi Ghassan
Published: March 2024

Structural health monitoring (SHM) of civil infrastructure is among many areas with a need for low-cost autonomous wireless sensors capable of operating without maintenance for extended periods of time. This paper describes the 3AT-09 wireless sensing module developed by Green SHM Systems. The module incorporates a microcontroller, memory, digital accelerometer, tilt and temperature sensors, and a transceiver. It is powered by a combination of batteries and an energy harvesting device. The module can operate in two modes – active mode and sleep mode. Low power consumption in sleep mode, along with power management capabilities and the use of energy harvesting allows for a very long service life of the module. It is estimated that the module can be self-sufficient for at least 25 years when used as part of a typical SHM application with short active mode sessions. The autonomous wireless sensing module is designed as a platform for long- term monitoring applications. Different sensors and energy harvesting devices can be integrated with the module. Experimental results on the use of sensing modules for vibration monitoring of bridges and tall buildings are provided.
Detecting Corrosion of Steel Prestressing Strands Using Acoustic Emission
Authors: K. ElBatanouny Mohamed , Mangual Jesé , Ziehl Paul , Matta Fabio
Published: March 2024

America’s transportation infrastructure has been receiving intensive public and private attention in recent years, particularly highway bridges. Corrosion of reinforcement steel is a main durability issue especially for concrete structures present in coastal areas and in areas where de-icing salts are routinely used.Acoustic emission (AE) is a promising method for detecting corrosion in steel reinforced concrete members. This type of non-destructive testing (NDT) method primarily measures the magnitude of energy released within a material when physically strained. The expansive ferrous product resulting from corrosion induces pressure at the steel-concrete interface creating micro-cracks which can be detected by AE sensors. In this study, five concrete blocks with embedded prestressing steel strands were built and tested under accelerated corrosion conditions to evaluate possible correlations between AE activity and the onset and progression of corrosion. AE data along with half-cell potential measurements were recorded during the test to determine the stages and the overall deterioration process. Afterwards, the steel strands were removed from the specimens, cleaned and weighed; then the results were evaluated vis-à-vis Faraday’s law with respect to the degree of corrosion present in each block.
Reliability Based Design Recommendations for an Electromechanical Actuator Test Stand
Authors: T. Koopmans Michael , C. Hooven Rudolph , Y. Tumer Irem
Published: March 2024

The quality and robustness of data sets of faulted electromechanical actuators (EMAs) are necessary to strengthen aircraft prognostic data analysis of such systems. Primary flight surface control actuators are of particular interest because the lack of known failure data  erodes the confidence of the component and subsequently sub-system health predictions. To aid in this research, an EMA test stand has been designed and built to help in predicting the life and wear characteristics of faulted actuators with respect to their nominal counterparts. Faults are injected into the actuator during in-flight experiments while actuator parameters are recorded and then post-processed on the ground. This paper provides an assessment of the availability and reliability of the current EMA test stand design. Using the performance history of similar components in the field, this paper specifically demonstrates design aspects of the test stand that affect test system design and fault data quality. The study has been conducted to validate the test stand design, as well as offer design recommendations to increase test stand availability and ability to supply quality and robust fault to failure data sets.
Robustness of a Structural Health Monitoring System under Drop-weight Impact Loading in Composites
Authors: Ostiguy Pierre-Claude , R. Mulligan Kyle , Masson Patrice , Elkoun Saïd
Published: March 2024

In this study, the robustness of a structural health monitoring system is tested on fiber glass composite coupons under impact testing using a drop-weight impact. The composite coupons are fitted with leadzirconate-titanate (PZT) transducers to induce Lamb waves into the specimens. Robustness of the structural health monitoring system is assessed. The electrical admittance defined by the inverse of the impedance is chosen as the robustness metric and is measured using an LCR analyzer prior to, and following an impact event. Detachment of the PZT transducer is monitored through comparison of the measured electrical admittances. An average minimum composite coupon thickness of 7 mm is defined for impacting fiber glass composite coupons with pre-attached PZT transducers. A 1.5 % drop of electrical admittance was observed for that thickness for one impact. The chosen metric is related to the capability of the structural health monitoring system to provide accurate damage detection results following an impact.
Uncertainty Identification of Damage Growth Parameters using Health Monitoring Data and Nonlinear Regression
Authors: Coppe Alexandra , T. Haftka Raphael , Ho Kim Nam
Published: March 2024

When it comes to identifying model parameters such as damage growth parameters in Paris law for example, Bayesian inference is a popular method. However, it involves substantial computational cost, especially with increasing number of parameters. When the prior distribution for the parameters is not narrow, non-linear regression may provide almost all the benefits of Bayesian updating at a small fraction of the computational cost. In this paper we apply this approach to the identification of damage growth parameters. As a first step we simplify the problem to a single parameter in order to compare it with the same problem solved using Bayesian inference. We then discuss the issues related to uncertainty quantification in the case of a highly non-linear problem.
Bayesian Reliability Prognosis for Systems with Heterogeneous Information
Authors: Bartram Gregory , Mahadevan Sankaran
Published: March 2024

A Bayesian methodology for prognosis of system reliability with heterogeneous reliability information is presented. Available information may be in the form of physics-based or experiment-based mathematical models, historical reliability data, or expert opinion. Such information typically describes the failure rates of individual components of the system and does not provide information on dependencies between them. The Bayesian methodology presented in this paper addresses this concern by learning the conditional probabilities in the Bayes network as observations about the system are made. First, the component and system faults are defined and the failure event tree is established. Bayesian priors for probabilities of both individual failure events and the conditional probabilities between them are established using various types of experimental data, expert opinion, or simulation data. Both the priors and conditional probabilities are updated as new data is collected, leading to an updated prognosis of system reliability. The methodology is demonstrated on an automobile startup system.
Effects of Personnel Availability and Competency on Fleet Readiness
Authors: Colosi Lindsey , Rothrock Ling , Barton Russell , Banks Jeffrey , Reichard Karl
Published: March 2024

The planning of future operations is a complex process that requires knowledge and understanding of many different factors and resources. Although there is much literature on maintenance planning, existing work lacks the integration of robust personnel work schedules into scheduling algorithms. Thus the objective of this research is to develop a procedure that aids in the short- term planning of operations by predicting the future readiness level of a fleet of vehicles that are subjected to various personnel factors. This research presents a procedure that combines two different models to appropriately predict readiness levels at the end of a seven-period horizon. This first model is a Monte Carlo simulation that determines different personnel availability scenarios based on three different factors that affect the net resource pool of workers of a maintenance unit. These scenarios are then entered into a binary integer linear program (BILP) which literatively optimizes fleet maintenance schedules on a daily basis. An overall fleet readiness level with a certain degree of probability is determined which serves as an extremely useful tool for operations planning. In addition, sensitivity analysis is presented on the different factors affecting personnel availability that can serve as useful aids in operational decision-making.
Gear and bearing fault detection under variable operating conditions
Authors: Boškoski Pavle , Juričić Dani , Stankovski Mile
Published: March 2024

The majority of the approaches to the fault detection in rotational machines assume constant and known operating conditions. These assumptions are often violated in practice. Therefore, in this paper we propose a set of features that can be utilized to reveal faults in gearboxes while being robust to fluctuations in operating speed and load. The proposed feature set comprises values of two information cost functions calculated from the coefficients of the wavelet packet transform accompanied by the maximal value of the spectral kurtosis. The fault detection capabilities of the proposed feature set were evaluated on a two-stage gearbox operating under different rotational speeds and loads with different types of mechanical faults.
Ensemble of Data-Driven Prognostic Algorithms with Weight Optimization and K-Fold Cross Validation
Authors: Hu Chao , D. Youn Byeng , Wang Pingfeng
Published: March 2024

The traditional data-driven prognostic approach is to construct multiple candidate algorithms using a training data set, evaluate their respective performance using a testing data set, and select the one with the best performance while discarding all the others. This approach has three shortcomings: (i) the selected standalone algorithm may not be robust, i.e., it may be less accurate when the real data acquired after the deployment differs from the testing data; (ii) it wastes the resources for constructing the algorithms that are discarded in the deployment; (iii) it requires the testing data in addition to the training data, which increases the overall expenses for the algorithm selection. To overcome these drawbacks, this paper proposes an ensemble data-driven prognostic approach which combines multiple member algorithms with a weighted- sum formulation. Three weighting schemes, namely, the accuracy-based weighting, diversity-based weighting and optimization-based weighting, are proposed to determine the weights of member algorithms for data-driven prognostics. The k-fold cross validation (CV) is employed to estimate the prediction error required by the weighting schemes. Two case studies were employed to demonstrate the effectiveness of the proposed prognostic approach. The results suggest that the ensemble approach with any weighting scheme gives more accurate RUL predictions compared to any sole algorithm and that the optimization-based weighting scheme gives the best overall performance among the three weighting schemes.
Detection of Precursors to Component Failure in a Spur Gear Drive-Train by Means of a Torque Transducer
Authors: Yutzy Joseph , Bruns Christopher , Yoder Nathanael , E. Adams Douglas , Gul Kamran , Calhoun Keith
Published: March 2024

In the wind energy industry, gearbox failures are among the most costly and the most frequent, adding significantly to the operation and maintenance costs over the life cycle of the turbine. Despite significant improvements in the understanding of gear loads and dynamics, even to the point of establishing international standards for design and specifications of wind turbine gearboxes, these components generally fall short of reaching their 20 year design life. In a significant number of gearbox failures, the primary bearing on the low speed shaft experiences faults in its operation, such as misalignment and movement on the mounts. To investigate the topic of gear health management, a fault detection approach is applied to a test bed involving a spur gear double-reduction transmission, outfitted with a torque transducer and triaxial accelerometers on the bearing cases. The test bed is not a wind turbine gearbox – the gear arrangement is different and the gears are smaller compared to that of a typical wind turbine gearbox – but it does serve to test the modeling and fault detection methods proposed in this paper. Both baseline and faulted measurements are taken from the experimental set-up for data analysis. It is shown that the torque sensor provides an early indication of fault precursors, such as misalignment and decreased lubrication, while also maintaining the capacity to identify mature faults, such as chipped and missing gear teeth. The measurements are analyzed using statistical based methods – the Mahalanobis distance and Parzen discriminant analysis. These features for fault detection are then characterized at various operating speeds for each of the geartrain conditions of interest. An analytical model is created from first principles for verification of results and for simulation of the free and forced dynamics of the system.
Remaining Life Prognostics for an Army Ground Vehicle System
Authors: Heine Richard , Barker Donald
Published: March 2024

Reliability is a key parameter for the development of safe and effective military vehicles with a reasonable life cycle cost. One innovative technology that is being promoted in the Department of Defense is the use of Health and Usage Monitoring Systems and remaining life prognostics to improve reliability and availability. The feasibility of using data collected from a limited set of existing and simple add-on sensors to make fatigue damage estimations on a complexly loaded component within a military wheeled vehicle system was investigated. Methods for identifying the critical inputs for fatigue estimation are evaluated and compared. A baseline physics of failure analysis was performed on an example component to evaluate the proposed HUMS algorithms and demonstrate the accuracy of resulting fatigue predictions.
Gear Fault Location Detection for Split Torque Gearbox using AE Sensors
Authors: He David , Manon Praneet , Li Ruoyu , Seçkiner Serap , Bechhoefer Eric
Published: March 2024

In comparison with a traditional planetary gearbox, the split torque gearbox (STG) potentially offers lower weight, increased reliability, and improved efficiency. These benefits have driven helicopter OEMs to develop products using STG. However, the effect of multiple gears meshing simultaneously with the central gear and a large number of synchronous components (gears or bearing) in close proximity creates a problem on how to detect the gear fault location in a STG. As of today, only limited research on STG fault detection using vibration sensors and acoustic emission sensors has been conducted.In this paper, an effective methodology on gear fault location detection using AE sensors for STG is presented. The methodology uses wavelet transform to process the AE sensor signals to determine the arrival time of the AE bursts at different locations. By analyzing the arrival times of the AE bursts, the gear fault location can be determined. The parameters of the wavelet transform are optimized by using an ant colony optimization algorithm. Real seeded gear fault experimental tests on a notational STG are conducted. AE sensor signals at the locations of healthy and damaged output driving gears are collected simultaneously to determine the location of the damaged gear. Experimental results have shown the effectiveness of the presented methodology.
Combination of analytical and statistical models for dynamic systems fault diagnosis
Authors: Bregon Anibal , Garcia-Alvarez Diego , Pulido Belarmino , Jesus Fuente Maria
Published: March 2024

Complex industrial and aerospatial systems require efﬁcient monitoring and fault detection schemes to ease prognosis and health monitoring tasks. In this work we rely upon the model-based approach to perform robust fault detection and isolation using analytical and statistical models. We have combined Principal Component Analysis (PCA) together with Possible Conﬂicts (PCs), to improve the overall diagnosis process for complex system. Our proposal uses residuals computed using PCs as the input for the PCA tool. The PCA tool is able to accurately determine signiﬁcant deviations in the residuals, that will be identiﬁed as faults. The integration of both techniques provides more robust results for fault detection, while avoiding false alarms in PCAs due to changes in operation modes. Moreover, it provides the PCA approach with the necessary mechanisms to perform fault isolation. This approach has been tested on a laboratory plant with real data, obtaining promising results.
Bayesian Framework Approach for Prognostic Studies in Electrolytic Capacitor under Thermal Overstress Conditions
Authors: S. Kulkarni Chetan , R. Celaya Jose ́ , Goebel Kai , Biswas Gautam
Published: March 2024

Electrolytic capacitors are used in several applications ranging from power supplies for safety critical avionics equipment to power drivers for electro-mechanical actuators. Past experiences show that capacitors tend to degrade and fail faster when subjected to high electrical or thermal stress conditions during operations. This makes them good candidates for prognostics and health management. Model-based prognostics captures system knowledge in the form of physics-based models of components in order to obtain accurate predictions of end of life based on their current state of health and their anticipated future use and operational conditions. The focus of this paper is on deriving first principles degradation models for thermal stress conditions and implementing Bayesian framework for making remaining useful life predictions. Data collected from simultaneous experiments are used to validate the models. Our overall goal is to derive accurate models of capacitor degradation, and use them to remaining useful life in DC-DC converters.
A Tutorial for Model-based Prognostics Algorithms based on Matlab Code
Authors: An Dawn , Choi Joo-Ho , Ho Kim Nam
Published: March 2024

This paper presents a Matlab-based tutorial for model-based prognostics, which combines a physical model with observed data to identify model parameters, from which the remaining useful life (RUL) can be predicted. Among many model-based prognostics algorithms, the particle filter is used in this tutorial for parameter estimation of damage or a degradation model in model-based prognostics. The tutorial is presented using a Matlab script with 62 lines, including detailed explanations. As examples, a battery degradation model and a crack growth model are used to explain the updating process of model parameters, damage progression, and RUL prediction. In order to illustrate the results, the RUL at an arbitrary cycle are predicted in the form of distribution along with the median and 90% prediction interval.
Fault Diagnostics Using Network Motif Signature
Authors: Lu Tsai-Ching , L. Allen David , Zhang Yilu , A. Salman Mutasim
Published: March 2024

In modern vehicles, controls are distributed over multiple Electronic Control Units (ECUs) that are connected through in-vehicle communication networks. Fault diagnostics for such a distributed control system is very challenging, which has resulted in many no-trouble-found (NTF) cases during warranty repairs. To address this problem, we propose a novel network-theoretic approach that detects, identifies, and localizes faults using both the structure of the communication network (topological information) and message flow information. The proposed method not only enables the characterization of normal operation and a-priori known faults across communication networks, which is already beyond the current practice of individual ECU centric diagnostics, but also the diagnostics of unknown or cascading failures emerging from unexpected operational environments.
Condition-Based Maintenance with both Perfect and Imperfect Maintenance Actions
Authors: Do Van Phuc , Voisin Alexandre , Levrat Eric , Iung Benoit
Published: March 2024

This paper deals with a condition-based maintenance (CBM) model considering both perfect and imperfect maintenance actions for a deteriorating system whose condition is aperiod- ically monitored according to a remaining useful life (RUL) based-inspection policy. Perfect maintenance actions restore completely the system to the ’as good as new’ state. Their related cost are however often high. Imperfect preventive maintenance restores partially the system with reduced main- tenance cost. Nevertheless, it may however make the system more susceptible to future deterioration. The aim of the pa- per is to propose a CBM model which can help to construct optimal maintenance policies when both perfect and imper- fect maintenance actions are possible. To illustrate the use of the proposed CBM model, a numerical example finally is introduced.
A Multi-resolution Experimental Methodology for Fatigue Mechanism Verification of Physics-based Prognostics
Authors: Yang Jian , Zhang Wei , Liu Yongming
Published: March 2024

An experimental methodology is proposed in this paper for mechanism verification of physics-based prognosis of mechanical damage, such as fatigue. The proposed experimental methodology includes multi-resolution in-situ mechanical testing, advanced imaging analysis, and mechanism analysis based on digital measurements. A case study is presented for fatigue crack growth mechanism investigation. In-situ fatigue testing at lower resolutions, i.e., optical microscopy, and digital image correlation is used to analyze the plastic deformation behavior and strain distribution near crack tips. In-situ fatigue testing under higher resolutions, i.e., scanning electron microscopy, and automatic image tracking is used to obtain detailed crack tip deformation and crack growth kinetics at the nanometer scales. Following this, the proposed experimental methodology is applied to two different metallic materials, aluminum alloys and steels. Very different experimental observations are observed and the underlying mechanisms are discussed in detail. The impact on the prognosis algorithm development is also discussed. Finally, the potential application of the proposed experimental methodology to other materials systems and to other types of mechanical damage is discussed.
Prognostics Method for Analog Electronic Circuits
Authors: Sai Sarathi Vasan Arvind , Long Bing , Pecht Michael
Published: March 2024

Analog electronic circuits are an integral part of many industrial systems. Failure in such analog circuits during field operation can have severe economic implications. The presence of an expert system that can provide advance warnings on circuit failures can minimize the downtime and improve the reliability of electrical systems. Through successive refinement of circuit’s response to a sweep signal, features are extracted for fault prognosis. From the extracted features, a fault indicator is developed. An empirical model is developed based on the degradation trend exhibited by the fault indicator. Particle filtering approach is used for model adaptation and remaining useful performance estimation. This framework is completely automated and has the merit of implementation simplicity. The proposed framework is demonstrated on two analog filter circuits.
Transducer Development for Nonintrusive Load Monitoring of Rotating Machinery
Authors: Putic Mateja , K. Brown Nathan , Muskopf Paul
Published: March 2024

Monitoring machine runtime health parameters through nonintrusive means can greatly reduce the up-front time and resource barriers to entry of adding instrumentation to existing plant infrastructure. This work presents the design and evaluation of three transducers as part of a nonintrusive load monitoring system for rotating machinery. Data collected using a custom designed, small-scale induction motor test stand shows the dependence of a large air core RF coil, small RF coil array, and Hall effect sensor outputs on applied motor speeds and mechanical loads (estimated based on generator power). Analysis indicates that the large air core RF coil transducer and the presented method for using nonintrusive collection of induction motor speed and stray flux can statistically measure the difference between any two load points with 95% confidence, if their values differ by 6.6% full scale or greater (±2). Additionally, areas of further research toward generalization of the approach are identified.
Decision Layer by Fusion of Diagnostic Algorithms
Authors: Lacaille Jérôme , Rabenoro Tsirizo
Published: March 2024

For manufacturers systems monitoring or production equipment optimization solutions are founded on specific algorithms that produce low level local information about risk of degradation or production loss. In either case local results are combined in synthetic reports aimed to help decision taking at higher level. This work is about the description of an automatic fusion mechanism able to build expert output with direct understanding of the system behavior and help to infer causes of efficiency loss. An example application was built and tested in a semiconductor fab. The algorithms diagnosed yield degradation in different subsystems or work-area and were digested in a weekly report that highlighted the main production problems. We deployed the same methodology for condition based maintenance of aircraft engines on a test platform. The first part of this document sketches out some notations, the second part describes the semiconductor application and the conclusion is dedicated to the transfer in the aeronautic domain for the decision level of an engine fleet health monitoring system.
Wear Prognostic on Turbofan Engines
Authors: Lacaille Jérôme , Gouby Aurélie , Piol Olivier
Published: March 2024

One of the most evident characteristic of wear for a turbofan engine is the exhaust gas temperature (EGT). It seems clear that this temperature increases when some carbon deposits on the turbine, when the compressor efficiency diminishes so the fuel flow should increase to produce the same amount of thrust, or even when some unbalance opens the spaces between the turbine and the casing. In any cases, an increase of the EGT should be analyzed because it is a wear symptom of the engine. It is mostly concluded by a water wash in the best case or a shop visit inspection and repair in the worst case. The engine manufacturer defines a schedule plan with its customer based on consumption of the EGT margin. This margin is the amount of available increase of the exhaust temperature before an inspection. Contractually, the engine is restored with a minimum EGT margin after each repair. Thus it is up to the manufacturer to understand how this margin is used to plan shop visits and to the company to estimate the current state of its engine. However, the EGT measurement is subject to a lot of noise and the company regularly washes their engines to increase randomly the margin and their capabilities. In this article we present a simple, automatic and embeddable algorithmic method to transform the successive EGT measurements in a delay indicator computed after each flight giving the amount of available use time. One challenge is to take care of the random wash or repair executed by the user. Finally this indicator may be transmitted automatically with the other data broadcasted by the aircraft computer (ACMS/ACARS) and it is used by the manufacturer to prepare his shop logistic.
System Level RUL Estimation for Multiple-Component Systems
Authors: Pordeus Gomes João Paulo , Ramos Rodrigues Leonardo , Kawakami Harrop Galvão Roberto , Yoneyama Takashi
Published: March 2024

Aircraft are highly valuable assets and large budgets are spent in predictive maintenance programs in order to maximize fleet availability. The application of PHM (Prognostics and Health Monitoring) technologies can be a powerful decision support tool to help maintenance planners. The estimated RUL (Remaining Useful Life) for each monitored component, obtained from a PHM system, can be used to plan in advance for the repair of components before a failure occurs. However, when system architecture is not taken into account, the use of PHM information may lead the operator to replace a component that would not immediately affect the availability of the system under consideration. In this paper, a methodology that combines fault tree information and individual components RUL estimations into a system level RUL (S-RUL) estimation is applied in a real life case study. The results showed that the methodology could have been successfully used in order to anticipate the failure of an aircraft ECS (Environmental Control System) and prevent an AOG (Aircraft on Ground) event from happening.
A Cryogenic Fluid System Simulation in Support of Integrated Systems Health Management
Authors: P. Barber John , B. Johnston Kyle , Daigle Matthew
Published: March 2024

Simulations serve as important tools throughout the design and operation of engineering systems. In the context of systems health management, simulations serve many uses. For one, the underlying physical models can be used by model- based health management tools to develop diagnostic and prognostic models. These simulations should incorporate both nominal and faulty behavior with the ability to inject various faults into the system. Such simulations can therefore be used for operator training, for both nominal and faulty situations, as well as for developing and prototyping health management algorithms. In this paper, we describe a methodology for building such simulations. We discuss the design decisions and tools used to build a simulation of a cryogenic fluid test bed, and how it serves as a core technology for systems health management development and maturation.
Probabilistic delamination diagnosis of composite materials using a novel Bayesian Imaging Method
Authors: Peng Tishun , Saxena Abhinav , Goebel Kai , Sankararaman Shankar , Xiang Yibing , Liu Yongming
Published: March 2024

In this paper, a framework for probabilistic delamination location and size detection is proposed. A delamination probability image using Lamb wave-based damage detection is constructed using the Bayesian updating technique. First, the algorithm for the probabilistic delamination detection framework using Bayesian updating (Bayesian Imaging Method - BIM) is presented. Following this, a fatigue testing setup for carbon-carbon composite coupons is introduced and the corresponding lamb wave based diagnostic signal is collected and interpreted. Next, the obtained signal features are incorporated in the Bayesian Imaging Method to detect delamination size and location, as along with corresponding uncertainty bounds. The damage detection results using the proposed methodology are compared with X-ray images for verification and validation. Finally, some conclusions and future works are drawn based on the proposed study.
Online Monitoring of Plant Assets in the Nuclear Industry
Authors: Agarwal Vivek , J. Lybeck Nancy , T. Pham Binh , Rusaw Richard , Bickford Randall
Published: March 2024

Today’s online monitoring technologies provide opportunities to perform predictive and proactive health management of assets within many different industries, in particular the defense and aerospace industries. The nuclear industry can leverage these technologies to enhance safety, productivity, and reliability of the aging fleet of existing nuclear power plants. The U.S. Department of Energy’s Light Water Reactor Sustainability Program is collaborating with the Electric Power Research Institute’s (EPRI’s) Long- Term Operations program to implement online monitoring in existing nuclear power plants.

Proactive online monitoring in the nuclear industry is being explored using EPRI’s Fleet-Wide Prognostic and Health Management (FW-PHM) Suite software, a set of web-based diagnostic and prognostic tools and databases that serves as an integrated health monitoring architecture. This paper focuses on development of asset fault signatures used to assess the health status of generator step-up transformers and emergency diesel generators in nuclear power plants. Asset fault signatures describe the distinctive features based on technical examinations that can be used to detect a specific fault type. Fault signatures are developed based on the results of detailed technical research and on the knowledge and experience of technical experts. The Diagnostic Advisor of the FW-PHM Suite software matches developed fault signatures with operational data to provide early identification of critical faults and troubleshooting advice that could be used to distinguish between faults with similar symptoms. This research is important as it will support the automation of predictive online monitoring
Health Monitoring and Prognosis of Hybrid Systems
Authors: Zabi Said , Ribot Pauline , Chanthery Elodie
Published: March 2024

Maintenance and repair of complex systems are an increasing part of the total cost of final product. Efficient diagnosis and prognosis techniques have to be adopted to detect, isolate and anticipate faults. Moreover the recent industrial systems are naturally hybrid: their dynamic behavior is both continuous and discrete. This paper presents an architecture of health monitoring and prognosis for hybrid systems. By using model and experience-based approach we propose an implementation of an integrated diagnosis/prognosis process based on Weibull probabilistic model. This article focuses, particularly on the prognosis algorithm description. The pro-cess has been implemented and tested on Matlab. Simulation results on a water tank system show how prognosis and diagnosis interact into the architecture.
Degredation Modeling and Remaining Useful Life Prediction of Electrolytic Capacitors under Thermal Overstress Condition Using Particle Filters
Authors: Khorasgani Hamed , Kulkarni Chetan , Biswas Gautam , R. Celaya Jose ́ , Goebel Kai
Published: March 2024

Prognostic and remaining useful life (RUL) predictions for electrolytic capacitors under thermal overstress condition are investigated in this paper. In the first step, the degradation process is modeled as a physics of failure process. All of the relevant parameters and states of the capacitor are considered during the degradation process. A particle filter approach is utilized to derive the dynamic form of the degradation model and estimate the current state of capacitor health. This model is then used to get more accurate estimation of the Remaining Useful Life (RUL) of the capacitors as they are subjected to the thermal stress conditions. The paper includes an experimental study, where the degradation of a set of identical capacitors under thermal overstress conditions is studied to demonstrate and validate the performance of the degradation modeling approach.
Prognostic Algorithm Verification
Authors: Clements N. Scott , S. Bodden David
Published: March 2024

A rigorous methodology is presented for both specification and verification of prognostic algorithm performance. The prognostic algorithm specification statement takes the form, “The prognostic algorithm shall provide a minimum of  hours time-to-maintenance such that between % and % of failures of component ABC will be avoided with % confidence.” The methodology is developed first for a single failure mode case and then extended to the multiple failure mode case. The case of non-prognosable failure modes is also considered. Finally, implications of this approach are presented, including pre- tabulation of confidence bounds, estimation of the minimum amount of data required to reach a given verification confidence, and a method for using a minimum confidence growth curve to account for initial low confidence in a prognostic algorithm.
A Model-Based Approach for Predicting the Remaining Driving Range in Electric Vehicles
Authors: A. Oliva Javier , Weihrauch Christoph , Bertram Torsten
Published: March 2024

The limited driving range has been pointed out as one of the main technical factors affecting the acceptance of electric vehicles. Offering the driver accurate information about the remaining driving range (RDR) reduces the range anxiety and increases the acceptance of the driver. The integration of electric vehicles into future transportation systems demands advanced driving assistance systems that offer reliable information regarding the RDR. Unfortunately, the RDR is, due to many sources of uncertainty, difficult to predict. The driving style, the road conditions or the traffic situation are some of these uncertain factors. A model-based approach for predicting the RDR by combining unscented filtering and Markov chains is introduced in this paper. Detailed models are implemented for representing the electric vehicle and its energy storage system. The RDR prediction is validated by a set of simulation based experiments for different driving scenarios. Whereas traditional approaches consider the RDR as a deterministic quantity, to our knowledge, this approach is the first to represent the RDR by a probability density function. We aim to provide initial steps towards a solution for generating reliable information regarding the RDR which can be used by driving assistance systems in electric vehicles.
A New Prognostic Approach for Hydro-generator Stator Windings
Authors: Amyot Normand , Hudon Claude , Lévesque Mélanie , Bélec Mario , Brabant France , Frenette François-Xavier
Published: March 2024

Significant improvements in hydro-generator diagnostics were achieved, in the past decades, by using continuous online measurements and a number of periodic tests. In recent years, the diagnostic raw data has been converted into more useful information by way of integrated diagnostic systems that used expert knowledge. For example, an integrated methodology for hydro-generator diagnostics was developed at Hydro-Québec’ s research institute (IREQ) using a Web-based application. This comprehensive diagnostic system gives the degradation state of generator stator winding insulation by using a portfolio of diagnostic tools. Combining the results leads to a health index ranging from 1 (good condition) to 5 (worst condition). This system is used by Hydro-Québec’s power plant managers as well as technical support and maintenance engineers in the context of condition-based maintenance (CBM). The next step of development is to add new prognostic-related features. This involves automatic identification of active failure mechanisms, root cause analysis and estimation of the stage of advancement of any active mechanism. These characteristics form the basis of predictive maintenance and support the optimization of maintenance strategies.

The approach is based on a number of causal trees (the failure mechanisms) formed by the combination of sequential physical degradation states that ultimately lead to a failure mode. Each combination of sequential physical states is unique and defines a particular failure mechanism. Failure mechanism analysis was followed by identification of all symptoms (diagnostics measurements, observations) with their respective thresholds defining each physical state.

This paper presents the development of a prognostic approach where the modeling of failure mechanisms is combined with observable symptoms from our diagnostic system for the identification of active failure mechanisms.
Determination of Primary Chemical Constituents of PBX(AF)-108 Warhead Explosive Using microPHAZIRTM Near Infrared (NIR) Handheld Platform
Authors: Daoud Sami , J. Villeburn Michal , D. Bailey Kevin , Kinloch Gordon , Biegert Lydia , Gardner Craig
Published: March 2024

An innovative prognostics and health management (PHM) technique for quantifying and characterizing health status of Plastic Bonded Explosive (PBX) Air force (AF) formula PBX (AF)-108 warhead explosive was developed using Near Infrared (NIR) spectra emitted by microPHAZIRTM NIR, a handheld platform developed by Thermo Fisher Scientific. Benchtop High Performance Liquid Chromatography (HPLC) was used as a reference technique for correlation to microPHAZIRTM NIR measurements.

Near infrared spectra were acquired from twenty freshly manufactured mixes of PBX (AF)-108 explosive formulae, which were used in setting up a D-Optimal full-factorial design of experiment (DOE). Three-hundred and sixty measurements were recorded and analyzed using Partial Least Squares (PLS) regression analysis for model building and method development. Results were correlated to spectra, which were measured using HPLC reference technique. All recorded measurements performed with microPHAZIRTM handheld platform were successfully validated with HPLC measurements. An algorithm was developed for microPHAZIRTM NIR thus qualifying the platform as a real-time nondestructive test (NDT)/

nondestructive evaluation (NDE) tool for quantification of primary chemical constituents of PBX (AF)-108. Primary chemical constituents of PBX(AF)-108 are Polyurethane (PU) binder, Royal Demolition Explosive (RDX) oxidizer/ fuel, Isodecyl Pelargonate (IDP) plasticiser, and E-702 (4, 4’-methylenebis(2,6-di-tert-butyl-phenol) [MBDTBP] anti- oxidant/stabilizer.

This teaming effort between Raytheon Missile Systems (RMS), United Kingdom Ministry of Defence (UK MoD), Alliant Techsystems Launch systems (ATK LS), and Thermo Fisher Scientific demonstrated outstanding ability to utilize miniature cutting edge technology to perform real- time NDT of PBX (AF)-108 warhead explosive without generating chemical waste and/or residue. The new technique will further be adapted for use to measure primary chemical constituents of other warhead explosives and solid rocket propellants. The new technique will significantly reduce costs associated with performing ordnance surveillance and Service Life Extension Program (SLEP) assessment, which is often destructive and requires use of lengthy and expensive test techniques described in North Atlantic Treaty Organization (NATO) Standardization Agreement (STANAG)-4170 and Allied Ordnance Publication (AOP)-7 manuals.
A Framework for Model-Based Diagnostics and Prognostics of Switched-Mode Power Supplies
Authors: Li Honglei , Ye Xuerong , Chen Cen , Vachtsevanos George
Published: March 2024

With electrical power supplies playing an important role in the operation of aircraft systems and sub-systems, flight and ground crews need health state awareness and prediction tools that accurately diagnose faults, predict failures, and project remaining life of these onboard power supplies. Among onboard power supplies, switch-mode power supplies are commonly used where their weight, size, and efficiency make them preferable to conventional transformer-based power supplies. In this paper, we present a framework of diagnostics and prognostics methodology based on an equivalent circuit system simulation model developed from a commercially available switch-mode power supply, and empirical component degradation models. In industrial applications, case-specified modifications can be made according to specific experimental or service conditions of different commercial products. First, the developed simulation model is validated through experimental testing. Then, a series of data are collected from simulation to build the baseline and fault databases under a fixed load profile. Next, promising features are extracted from sensed parameters, and further data analysis are conducted to estimate the current health condition and to predict the remaining useful life of the target system. Some highlights of the work are included but not only limited to the following aspects: first, the methodology is based on electronic system simulation instead of traditional accelerated testing by employing a high-fidelity system simulation model and empirical critical component degradation models; second, efforts are made in this preliminary work to adapt proven prognostics and health management techniques from machinery to electronic health management, with the goal of expanding the realm of electronic diagnostics and prognostics.
Modeling Degradation Using Thermodynamic Entropy
Authors: D. Bryant Michael
Published: March 2024

Manufacture transforms raw materials into finished components. Ageing and degradation of components, driven by dissipative processes, irreversibly alter material structures. The second and third laws of thermodynamics assert that these dissipative processes must generate entropy. This entropy is a fundamental quantity to describe ageing and degradation.

This recognition led to a Thermodynamic Degradation Paradigm encapsulated in a Degradation Entropy Generation (DEG) Theorem, wherein the rate of degradation was related to the irreversible entropies produced by the underlying dissipative physical processes that age and degrade components. This paradigm and theorem permit a structured approach to modeling degradation of any kind. If properly applied, the DEG Theorem leads to a differential equation in a variable that describes the degradation. The equation depends on the operational and environmental variables that characterize the system. Integration of the equation accumulates the degradation over time. This approach has led to accurate models for progression of and failure by wear, fatigue, and battery degradation that are consistent with prior models.

This article will review the Thermodynamic Degradation Paradigm and Degradation Entropy Generation Theorem, and apply these to formulate predictive models of wear, fatigue, and battery degradation, i.e., differential equations that govern the degradation or ageing. The article will conclude with a discussion on how to use these governing degradation equations for machine prognosis.