bluuebunny/crossref_metadata_2025 huggingface dataset:
Planetary Gearbox Fault Diagnosis Using a Single Piezoelectric Strain Sensor
Authors: Yoon Jae , He David , Van Hecke Brandon , J. Nostrand Thomas , Zhu Junda , Bechhoefer Eric
Published: March 2024

Planetary gearboxes are widely used in the drivetrain of helicopters and wind turbines. Any planetary gearbox failure could lead to breakdown of the whole drivetrain and major loss of helicopters and wind turbines. Therefore, planetary gearbox fault diagnosis is an important topic in prognostics and health management (PHM). Planetary gearbox fault diagnosis has been done mostly through vibration analysis over the past years. Vibration signals theoretically have the amplitude modulation effect caused by time variant vibration transfer paths due to the rotation of planet carrier and sun gear, and therefore their spectral structure is complex. It is difficult to diagnose planetary gearbox faults via vibration analysis. Strain sensor signals on the other hand have less amplitude modulation effect. Thus, it is potentially easy and effective to diagnose planetary gearbox faults via stain sensor signal analysis. In this paper, a research investigation on planetary gearbox fault diagnosis via strain sensor signal analysis is reported. The investigation involves using time synchronous average technique to process signals acquired from a single piezoelectric strain sensor mounted on the housing of a planetary gearbox and extracting condition indicators for fault diagnosis. The reported investigation includes analysis results on a set of seeded fault tests performed on a planetary gearbox test rig in a laboratory. The results have showed a satisfactory planetary gearbox fault diagnostic performance using strain sensor signal analysis.
Towards Accreditation of Diagnostic Models for Improved Performance
Authors: Kodali Anuradha , Robinson Peter
Published: March 2024

The research community mainly concentrates on developing new and updated diagnostic algorithms to achieve high diagnostic performance which is necessary but not sufficient for the diagnostic models that are embedded in software. The focus of this paper is to understand the requirements for accrediting diagnostic system models to meet high performance and safety criticality in case of both models and embedded system (model + software). For embedded systems, models need to be accredited first to allow a more accurate distinction of whether the model or the code within which the model is embedded is the cause of degraded performance. This is because, neither standards for models and simulations (NASA-STD-7009) nor software engineering requirements (NPR 7150.2A) are sufficient to accredit the models in embedded systems. NASA-STD- 7009 assesses the correctness of the physics in models and simulations and NPR 7150.2A lists software engineering requirements for NASA systems. Thus, it is important to understand the accreditation standards in terms of performance requirements of models in embedded systems that can smoothly transit from NASA-STD-7009 to NPR 7150.2A. We will discuss interactive diagnostic modeling evaluator (i-DME) as an accreditation tool that provides the performance requirements or limitations imposed while accrediting embedded systems. This process is done automatically, making accreditation feasible for larger diagnostic systems.
On the Use of Particle Flow to Enhance the Computational Performance of Particle-Filtering-based Prognostics
Authors: A. Oliva Javier , Bertram Torsten
Published: March 2024

Prognostic approaches based on particle filtering employ physical models in order to estimate the remaining useful life (RUL) of systems. To this aim a set of particles is used to first estimate the degradation state of the system and then to predict the distribution of the RUL through simulation. The computational complexity of this approach is a function of the number of particles used in the state estimation and of the time each particle needs to simulate the RUL. It is therefore clear that enhancing the computational performance of this approach requires reducing the number of particles. In this paper we investigate the applicability and suitability of the particle flow particle filter for particle-filtering-based prognostics. The estimation of the remaining driving range (RDR) of an electric vehicle is used as the case study to illustrate the improvement in computational performance of the proposed approach in comparison to the standard particle filter.
Multiple-imputation-particle-filtering scheme for Uncertainty Characterization in Battery State-of-Charge Estimation Problems with Missing Measurement Data
Authors: E. Acun ̃a David , E. Orchard Marcos , F. Silva Jorge , Pe ́rez Aramis
Published: March 2024

The design of particle-filtering-based algorithms for estimation often has to deal with the problem of missing observations. This requires the implementation of an appropriate methodology for real-time uncertainty characterization, within the estimation process, incorporating knowledge from other available sources of information. This article presents preliminary results of a multiple imputation strategy used to improve the performance of a particle-filtering-based state- of-charge (SOC) estimator for lithium-ion (Li-Ion) battery cells. The proposed uncertainty characterization scheme is tested and validated in a case study where the state-space model requires both voltage and discharge current measurements to estimate the SOC. A sudden disconnection of the battery’s voltage sensor is assumed to cause significant loss of data. The results show that the multiple-imputation particle filter enables reasonable uncertainty characterization for the state estimate as long as the voltage sensor disconnection continues. Furthermore, when the voltage measurements are once more available, the level of uncertainty adjusts to levels that are comparable to the case where data was not lost.
Model-Based Fault Diagnosis of a Planetary Gear Using Transmission Error
Authors: Park Jungho , Moon Ha Jong , D. Youn Byeng , Hyuck Leem Sang , Choi Joo-Ho , Ho Kim Nam
Published: March 2024

A Planetary gear can transmit high torque ratio stably and, therefore, the gear is widely used in industrial applications, i.e., wind turbines, automobiles, helicopters. Unexpected failure of the planetary gear results in substantial economic loss and human casualties. Extensive efforts have been made to develop the fault diagnostic techniques of gears; however, the techniques are mostly concerned about spur gears. This is mainly because understanding of complex dynamic behaviors of a planetary gear is lacking, such as multiple gear contacts, non-stationary axis of rotation, etc. This study thus proposes model-based fault diagnostics for a planetary gear that is based upon its dynamic analysis. Instead of vibration signals, this study uses transmission error (TE) signals for fault diagnostics of the planetary gear because TE signals (a) are directly related to the dynamic behaviors of gear mesh stiffness and (b) increase as damages on a gear mesh reduce the gear mesh stiffness. A lumped parameter model was used for modeling dynamic behaviors of the planetary gear. For more precise modeling, mesh phase difference–between sun, ring, and planet gear– and contact ratio were taken into account in the lumped parameter model. After acquiring transmission error signals from the model, order analysis and data processing were executed to generate health related data for the planetary gear. Consequently, it is concluded that the use of transmission error signals helps gain understanding of complex dynamic behaviors of the planetary gear and diagnose its potential faults.
A Novel Human-Machine Interface Framework for Improved System Performance and Conflict Resolution
Authors: Li Jiaming , Vachtsevanos George
Published: March 2024

This paper introduces a framework for the conceptualization and design of novel operator-aircraft/unmanned system automated interface concepts that will assist to enhance operator reliance on automated advisories. There is a need to explore new human-machine interface strategies stemming from the proliferation over the past years of accidents due to system complexity, failure modes and human errors. Concepts of autonomy establish the foundational elements of the work. We pursue a rigorous systems engineering process to analyze and design the tools and techniques for automated vehicle health monitoring, human-automation interface and conflict resolution enabled by innovative methods from Dempster- Shafer theory and reasoning algorithms. The emphasis in this contribution is on conflict resolution arising between the human operator (pilot) and on-system automated apparatus. The enabling technologies for conflict resolution borrow from Dempster-Shafer evidential theory, probabilistic and Game Theory for improved system autonomy and reasoning paradigms. The efficacy of the approach is demonstrated via an application to major drive subsystems of a helicopter and an autonomous hovercraft laboratory prototype.
A Novel Feature Extraction Method for Monitoring (Vehicular) Fuel Storage System Leaks
Authors: Tseng Fling , H. Makki Imad , P. Filev Dimitar , Babu Chinnam Ratna
Published: March 2024

System state determination with incomplete sensory information set proved to be a technically challenging problem. In this paper, authors tackle a problem of this type associated with vehicle fuel storage systems and proposed a novel feature extraction method. Federal and state regulations require fuel storage leak detection mechanism to be conducted periodically and regulate its execution rate and performance to ensure effective emission controls. Being able to robustly determine a fuel storage system’s state in terms of its effectiveness of fuel containment is therefore of great importance to all vehicle original equipment manufacturers (OEM). Prevailing practice in the industry utilizes a method relevant to natural vacuum phenomenon and is loosely associated with ideal gas law. Commonly referred to as “Entry Conditions” in in-vehicle monitoring design literature, major noise factors go through stringent pre-monitoring evaluations before monitoring program execution to ensure ideal test conditions. Differences in ambient conditions compounded with varying customer drive cycle patterns present great challenge to existing monitor designs for the purpose of leak detection. In addition, prevailing practices of evaluation in-tank fuel pressure and temperature information are generally conducted with surrogate or estmiated temperature information due to the absence of in-tank temperature sensor. All this calls for an alternative feature calculation and detection method that are less sensitive to known noise factors, can operate with incomplete sensory information yet being able provide similar or improved detection capability. In this paper, we put the main focus on the derivation of a novel method of feature calculation for the purpose of detecting presence of a leak in a fuel storage tank.
Survey of Condition Indicators for Condition Monitoring Systems
Authors: Zhu Junda , Nostrand Tom , Spiegel Cody , Morton Brogan
Published: March 2024

Currently, the wind energy industry is swiftly changing its maintenance strategy from schedule based maintenance to predictive based maintenance. Condition monitoring systems (CMS) play an important role in the predictive maintenance cycle. As condition monitoring systems are being adopted by more and more OEM and O&M service providers from the wind energy industry, it is crucial to effectively interpret the data generated by the CMS and initiate proactive processes to efficiently reduce the risk of potential component or system failure which often leads to down tower repair or gearbox replacement. The majority of CMS are designed and constructed based on vibration analysis which has been refined over the years by researchers and scientists. This paper provides detailed description and mathematical interpretation of a comprehensive selection of condition indicators for gears, bearings and shafts. Since different condition indicators are sensitive to different kind of failure modes, the application for each condition indicators were also discussed. The Time Synchronous Averaging (TSA) algorithm was applied as the signal processing method before the extraction of condition indicators for gears and shafts. Time Synchronous Resampling algorithm was applied to stabilize the shaft speed before the extraction of bearing condition indicators. Several case studies of real world wind turbine component failure detection using condition indicators were presented to demonstrate the effectiveness of certain condition indicators.
Building a Data-Driven Vital Sign Indicator for an Economically Optimized Component Replacement Policy
Authors: Ahn Hyung-il , Tat Leung Ying , Hochstein Axel
Published: March 2024

In asset-intensive services, a well-known challenge is to maintain high availability of the physical assets while keeping the total maintenance cost low. In applications of high-value machinery such as heavy industrial equipment, a traditional approach is to perform periodic maintenance according to a runtime-based schedule. Most equipment vendors publish a maintenance schedule based on a “standard” or “average” working environment. In addition, it is a common practice that maintenance schedules from equipment vendors are highly conservative in order to reduce in-field failures which gives an adverse perception of a vendor’s reputation. Therefore, such a schedule may not result in satisfactory performance as measured according to the owner’s business objectives. Also, the assumption of normal operating condition may not apply in some situations. For example, stresses due to frequent overloading, continuous usage of engine at a high rate in tough environments, machine usage beyond its designed capacity can serve as good contributors to excessive wear and premature failures. In this paper we propose a novel computational framework to build a data-driven economically optimized vital sign indicator for a given component type and an economic criterion (e.g., average maintenance cost per unit runtime) by combining different sources of historical data such as total runtime hours, load carried, fuel consumed and event information from sensors. This new vital sign indicator can be viewed as a transformed time scale and used to find the optimal threshold value (or “scheduled replacement time equivalent”) for a component replacement policy. Our case study was based on the collected data from 50 mining haul trucks over about 6 years in one of the largest mining service companies in the world. We present that the new vital sign indicator-based replacement policy for a critical component type largely improves on the traditional runtime-based schedule in terms of a given economic criterion, achieving a lower total maintenance cost of the enterprise.
Model based Online Fault Diagnosis of Automotive Engines using Joint State and Parameter Estimation
Authors: E P Nadeer , Patra Amit , Mukhopadhyay Siddhartha
Published: March 2024

In this work, an Extended Kalman Filter (EKF) based tunable diagnoser, which uses a minimal hybrid nonlinear state space model of a spark ignition (SI) four stroke engine, is used for the detection and isolation of a variety of engine system faults including intake manifold leak, injector fault and exhaust manifold leak. The state estimates and innovation sequences from the EKF based estimator are shown to be adequate for the detection and isolation of the faults under consideration. Once a fault is detected and isolated, the diagnoser could be tuned online to perform fault identification by redefining a model/fault parameter as an additional state to be estimated and then performing a joint state and parameter estimation. The engine model and diagnoser are implemented in SimulinkTM and are validated against an AMESimTM model of the engine. For the nominal engine model, the performance of the EKF estimator is compared with two other computationally more expensive nonlinear estimators, namely the Unscented Kalman Filter (UKF) and Rao-Blackwell Particle Filter (RBPF).
Model-based Approach to Automated Calculation of Key Performance Indicators for Industrial Turbines
Authors: Mehdi Gulnar , Naderi Davood , Ceschini Giuseppe , Fishkin Alexey , Brandt Sebastian , Watson Stuart , Roshchin Mikhail
Published: March 2024

In recent years, the service business of the global turbo- machinery industry has undergone important changes. Many of these changes have been motivated by an increased demand for dedicated and systematic approaches to process safety, reliability, asset integrity and the overall health of the system. This has strengthened the role of key performance indicators (KPIs) as a means of providing guidance for the system’s health state and improve risk management. In order to provide trustable and accurate calculations of these performance indicators in an automated fashion, we argue for a model-based solution that deals with the complexity of diverse configurations and interdependences between system components. This paper presents a solution for calculating KPIs by a semi-automated process based on post-data processing from the site and specific system models. The models consist of a combination of system descriptions in terms of ontologies and complex event processing models. By virtue of our models, state indicator rules for KPI calculations can be formulated at different levels, identifying performance gaps and indicating precisely where action should be taken by the service engineers. With the adopted solution, we discuss the practical implementation and present results of our success story at Siemens AG for the Industrial Gas Turbines.
Online Monitoring and Fault Diagnosis of Hybrid Systems Using Switched Dynamic Bayesian Networks
Authors: Zhou Gan , Biswas Gautam , Feng Wenquan , Guan Xiumei
Published: March 2024

Modern real-world engineering systems typically have hybrid dynamic behaviors that can be modeled by continuous behaviors with discrete mode transitions. These complex systems present many significant challenges for online monitoring and diagnosis, including tracking system behavior, dealing with noisy measurements and disturbances, and diagnosing different types of faults. In this paper, we propose an integrated model-based diagnosis approach that extends the traditional Dynamic Bayesian Network-based particle filter approach for tracking continuous system dynamics. A novel mode diagnoser is presented that discriminates between residuals generated by inaccurate system tracking, discrete faults, and parametric faults. An extended quantitative fault isolation and identification scheme is combined with a qualitative fault isolation scheme to identify the abrupt parametric faults. We demonstrate the effectiveness of our approach by applying it to Reverse Osmosis (RO) subsystem of the Water Recovery System (WRS) developed at the NASA Johnson Space Center for long duration human missions.
Variable Selection and Indices Proposal for the Determination of an Aeronautic Valve Degradation
Authors: S. Ribeiro Ananda , Yoneyama Takashi , F. Souto Rodrigo , Turcio Wallace
Published: March 2024

This work presents a method to select parameters and use them in an index to identify the degradation level of an aeronautic valve of the type PRSOV (Pressure Regulator and Shutoff Valve), a valve of the bleed air system of aircrafts. This index function is to be used as an input to the valve controller reconfiguration as the valve presents degradation while ages. The monitoring of degradation rates can provide information to modify the controller parameters in order to increase the valve useful life, to allow maintenance services planning or even to prevent the system performance being meaningfully impacted by the valve aging. The sorts of degradation considered were: friction increase, charging orifice obstruction and venting orifice obstruction. The study was made based on the valve mathematical model, in which was possible to vary those degradations levels and verify how each one influences the model response. The hysteresis graphic morphology was analyzed and also the sensibility and robustness after disturbances in the inlet air pressure with the system in closed loop, where the objective was to identify a monitoring that could be realized with the aircraft in air. The results allowed the identification of the most recommended parameters to monitor and the evaluation of the valve degradation through them.
On Accurate and Reliable Anomaly Detection for Gas Turbine Combustors: A Deep Learning Approach
Authors: Yan Weizhong , Yu Lijie
Published: March 2024

Monitoring gas turbine combustors' health, in particular, early detecting abnormal behaviors and incipient faults, is critical in ensuring gas turbines operating efficiently and in preventing costly unplanned maintenance. One popular means of detecting combustors’ abnormalities is through continuously monitoring exhaust gas temperature profiles. Over the years many anomaly detection technologies have been explored for detecting combustor faults, however, the performance (detection rate) of anomaly detection solutions fielded is still inadequate. Advanced technologies that can improve detection performance are in great need. Aiming for improving anomaly detection performance, in this paper we introduce recently-developed deep learning (DL) in machine learning into the combustors’ anomaly detection application. Specifically, we use deep learning to hierarchically learn features from the sensor measurements of exhaust gas temperatures. And we then use the learned features as the input to a neural network classifier for performing combustor anomaly detection. Since such deep learned features potentially better capture complex relations among all sensor measurements and the underlying combustors’ behavior than handcrafted features do, we expect the learned features can lead to a more accurate and robust anomaly detection. Using the data collected from a real-world gas turbine combustion system, we demonstrated that the proposed deep learning based anomaly detection significantly indeed improved combustors’ anomaly detection performance.Deep learning, one of the breakthrough technologies in machine learning, has attracted tremendous research interests in recent years in the domains such as computer vision, speech recognition and natural language processing.Deep learning, to the best of our knowledge, has not been used for any PHM applications, however. It is our hope that our initial work presented in this paper would shed some light on how deep learning as an advanced machine learning technology can benefit PHM applications and, more importantly, can stimulate more research interests in our PHM community.
Application of Health Management and Diagnostics for Synthetic Aperture Radar (SAR) Payloads
Authors: Bower Gregory , Wrable Curtis , Bird Ross , Woodford Paul
Published: March 2024

Synthetic aperture radars are radar platforms that generate detailed images through radio frequency transmission and receiving. These systems can be high peak power, complex systems that can suffer from internal component or subsystem degradation. In addition, the operational environment can also affect the final image of the radar due to scene-based radio frequency interference (RFI). Because of these effects, it is ideal to be able to identify, classify, and quantify the degradation of these systems in order to optimize their performance and life. The work presented in this paper is an extension of QorTek’s previous work using Symbolic Analysis to detect degradation using the radar’s phase history data. In conjunction with the KEYW, Corp., QorTek has acquired field data to train and test its algorithm. To test the trained algorithm, a prototype hardware/software system integrating the SA approach was designed, built and flown on a test flight piggybacking on a radar system provided by KEYW. The initial results were very positive and also identified areas of improvement. The training and test results as well as the flight-test plan and results are presented in this paper. The paper concludes with specific improvements to be made to the algorithm for the next round of radar integration and flight-testing.
Railcar Bogie Performance Monitoring using Mutual Information and Support Vector Machines
Authors: Shahidi Parham , Maraini Daniel , Hopkins Brad , Seidel Andrew
Published: March 2024

Railcar condition monitoring is an area of high importance and global relevance. The economic and safety concerns of equipment maintenance in North America mandate efforts in prognostics and health management. This paper presents the results from the development of a vibration based condition monitoring algorithm for freight rail, utilizing mutual information feature selection and support vector machine classification of bogie component faults. The algorithm is an implementation of a previously proposed railcar condition monitoring solution by the authors. The proposed monitoring solution is a data-driven method which was developed with measurements taken at a railroad test laboratory under controlled conditions. Vibration data was collected from multiple locations on a railcar over several test runs, each utilizing wheelsets with different levels of wear. The input of controlled wheel wear levels was aimed at varying the system outputs to resemble those of cars with different levels of mileage in revenue service. The generated data sets were processed and a feature set was extracted from the acceleration signals. The data was divided into training and validation partitions using a cross validation scheme to preserve the sequence for both sets. A mutual information (MI) estimation algorithm was used to rank the features based on their similarity to the classified fault state. Both the optimized feature set from the MI feature selection algorithm as well as the full, non-discriminate feature set were used as inputs to the support vector machine to assess classification accuracy. The results of this assessment are presented in the paper along with a presentation of the methods. The paper concludes with a proposal for a monitoring strategy aimed at specifically detecting faulty components and practicing predictive maintenance.
Parameters Adaption of Lebesgue Sampling-based Diagnosis and Prognosis for Li-ion Batteries
Authors: Yan Wuzhao , Dou Wanchun , Liu Datong , Peng Yu , Zhang Bin
Published: March 2024

Traditional fault diagnosis and prognosis (FDP) approaches are based on Riemann sampling (RS), in which samples are taken and algorithms are executed in a periodic time interval. With the increase of system complexity, the real-time implementation of this Riemann sampling-based FDP (RS- FDP) becomes a bottleneck, especially for distributed applications. To overcome this problem, a Lebesgue sampling- based FDP (LS-FDP) is proposed. LS-FDP takes samples on the fault dimension axis and provides a need-based diagnostic philosophy in which the algorithm is executed only when necessary. In previous Lebesgue sampling-based FDP, the Lebesgue length is a constant. To accommodate the change of fault dynamics, it is desirable to execute FDP algorithm more frequently when the fault growth is fast while less frequently when fault growth is slow. This requires to change the Lebesgue length adaptively. The goal of this paper is to deliver an improved LS-FDP method with varying Lebesgue length, which enables the FDP to be executed according to needs. The design and implementation of varying Lebesgue length LS-FDP based on a particle filtering algorithm are illustrated with experimental results on Li-ion batteries to verify the performances of the proposed approach. The experimental results show that the new varying LS-FDP is accurate and time-efficient on long term prognosis and also keeps a closer monitoring on the fast increase of fault size.
A V-Diagram for the Design of Integrated Health Management for Unmanned Aerial Systems
Authors: E Heaton Andrew , Fan Ip-Shing , K Jennions Ian , Lawso Craig , McFeat Jim
Published: March 2024

Designing Integrated Vehicle Health Management (IVHM) for Unmanned Aerial Systems (UAS) is inherently complex. UAS are a system of systems (SoS) and IVHM is a product- service, thus the designer has to take into account many factors, such as: the design of the other systems of the UAS (e.g. engines, structure, communications), the split of functions between elements of the UAS, the intended operation/mission of the UAS, the cost verses benefit of monitoring a system/component/part, different techniques for monitoring the health of the UAS, optimizing the health of the fleet and not just the individual UAS, amongst others. The design of IVHM cannot sit alongside, or after, the design of UAS, but itself be integrated into the overall design to maximize IVHM’s potential.

Many different methods exist to help design complex products and manage the process. One method used is the V- diagram which is based on three concepts: decomposition & definition; integration & testing; and verification & validation. This paper adapts the V-diagram so that it can be used for designing IVHM for UAS. The adapted v-diagram splits into different tracks for the different system elements of the UAS and responses to health states (decomposition and definition). These tracks are then combined into an overall IVHM provision for the UAS (integration and testing), which can be verified and validated. The stages of the adapted V-

diagram can easily be aligned with the stages of the V- diagram being used to design the UAS bringing the design of the IVHM in step with the overall design process. The adapted V-diagram also allows the design IVHM for a UAS to be broken down in to smaller tasks which can be assigned to people/teams with the relevant competencies. The adapted V-diagram could also be used to design IVHM for other SoS and other vehicles or products
A data pipeline for PHM data-driven analytics in large-scale smart manufacturing facilities
Authors: Odonovan Peter , Leahy K. , Cusac D. Og , Bruton K. , J. O’Sullivan D. T.
Published: March 2024

The term smart manufacturing refers to a future-state of manufacturing, where the real-time transmission and processing of information across the factory will be used to produce advanced manufacturing intelligence that can optimize every aspect of its operation. In recent years, initiatives and groups such as the Smart Manufacturing Leadership Coalition (SMLC), Industry 4.0, and the Industrial Internet Consortium (IIC), have led the way by bringing together industry, academia and government to establish policies, roadmaps and platforms to support smart manufacturing. Although there are many characteristics that can be associated with smart manufacturing across these initiatives, a common theme is the emphasis on transitioning operations from reactive and responsive, to predictive and preventative. The research presented in this paper focuses on the development of a data pipeline that supports the development of data-driven Prognostics and Health Management (PHM) applications. In the context of smart manufacturing, PHM enables facilities to transition from preventative and reactive maintenance strategies, to predictive, preventative and condition-based strategies. The benefits that can be derived by PHM are aligned with those of smart manufacturing, which include the opportunity to decrease costs, increase machine availability, reduce energy consumption, and improve production yield.

However, the process of ingesting, cleaning and transforming real-time data streams for data-driven PHM is a difficult, complex and time-consuming task, with estimates from business intelligence projects ranging from 80% to 90% of total project effort. This effort may only be exacerbated further in manufacturing environments due to additional technology challenges, such as low levels of standardization, disparate protocols and interfaces, and ad hoc data management. While emerging technologies such as Cyber Physical Systems (CPS) and Internet of Things (IoT) can overcome many of these challenges and provide an open platform for transmitting data, existing large-scale manufacturing facilities that are subject to compliance, regulation, and stringent quality

assurance policies may not be able to adopt these technologies in the short-term due to the associated cost, risk and effort. Therefore, PHM applications that need to access data streams in large-scale manufacturing facilities must do so using transparent data integration that does not discriminate between emerging and legacy technologies in the factory. To this end, this research presents a real- time, scalable, robust, and fault tolerant data pipeline for ingesting, cleaning, transforming, processing and contextualizing time-series data from a wide-range of sources in the factory.
Quadrotor Actuator Fault Diagnosis with Real-Time Experimental Results
Authors: Avram Remus C , Zhang Xiaodong , Khalili Mohsen
Published: March 2024

Quadrotor Unmanned Aerial Vehicles (UAVs) have attracted significant attention in recent years due to their potentials in various military and commercial applications. Four propellers mounted on the shafts of four brushless DC motors generate the required thrust and torques needed for altitude and attitude control of quadrotors. However, structural damage to the propellers and rotor degradation can lead to actuator faults in the form of a partial loss of effectiveness in a rotor, which may result in degraded tracking performance or even loss of stability of the control system. This paper presents the systematic design, analysis, and real-time experimental results of a fault detection and isolation algorithm for quadrotor actuator faults using nonlinear adaptive estimation techniques. The fault diagnosis architecture consists of a nonlinear fault detection estimator and a bank of nonlinear adaptive fault isolation estimators designed based on the functional structures of the faults under consideration. Adaptivethresholds for fault detection and isolation are systematically designed to enhance the robustness and fault sensitivity of the diagnostic algorithm. Using an indoor quadrotor test environment, real-time experimental flight test results are shown to illustrate the effectiveness of the algorithms.
Engine Health Management in Safran Aircraft Engines
Authors: Bastard Guillaume , Lacaille Jérome , Coupard Josselin , Stouky Yacine
Published: March 2024

Engine Health Management (EHM) is the up to date solution that is used by Aircraft Engine Manufacturers in order to maintain an engine operative through a reduction of operational events that impact its availability for end customers. The aim of EHM systems is to monitor and forecast the health status of an engine based on operational data in order to reduce the interruption of the clients operations and contribute to provide the best affordable maintenance of an engine. This paper describes the architecture of an EHM system designed to monitor Safran Aircraft Engines products.
Smart Diagnosis of Journal Bearing Rotor Systems: Unsupervised Feature Extraction Scheme by Deep Learning
Authors: Oh Hyunseok , Jeon Byung Chul , Jung Joon Ha , Youn Byeng D.
Published: March 2024

In most approaches for journal bearing rotor system diagnosis, dominant features are manually extracted based on expert’s experience and domain knowledge. With the adoption of advanced journal bearings and the limited knowledge about physics-of-failure, the current practice for feature extraction showed limitations for real applications in the power plant industry. To this end, this paper proposes an unsupervised scheme to extract features from correlated vibration signals. First, raw vibration signals from a pair of sensors are preprocessed by generating two-dimensional images. Second, the vibration images are characterized with a HOG (histogram of original gradients) descriptor. Then, deep learning is used to extract relevant features for journal bearing rotor system diagnosis. To demonstrate the validity of the proposed unsupervised-feature-extraction scheme, a case study was conducted with data from the RK4 rotor kit. The results showed that the proposed scheme outperformed existing methods in terms of fault classification accuracy. We anticipate that the proposed scheme is promising as it can minimize the reliance of expert’s experience and domain knowledge.
Failure Precursor Identification and Degradation Modeling for Insulated Gate Bipolar Transistors Subjected to Electrical Stress
Authors: Lee Junmin , Oh Hyunseok , Park Chan Hee , Youn Byeng D. , Kim Deog Hyeon , Kim Byung Hwa , Cho Yong Un
Published: March 2024

In driving equipment of smart factories, unexpected failures of insulated gate bipolar transistors (IGBTs) are often observed. Electrical stresses are one of the dominant causes for the IGBT failures in the field. However, there is little study about the effect of electrical stresses on the degradation of IGBTs. In this paper, we attempt to identify a key failure precursor for IGBTs subjected to electrical stresses and to model the evolution of the failure precursor. To achieve the goals, first, the main causes of IGBT failures are identified based on maintenance history, filed failure data, and experts’ opinions. Second, an artificial fault injection method, i.e., electrostatic discharge (ESD), is employed to produce partially degraded (but not failed) IGBTs. The proper levels of the intensity of electrical loads (i.e., magnitude and number of the ESDs) are also determined. Finally, artificial ESD faults are seeded to IGBTs and potential candidates of failure precursors are measured. The steps are repeated until the failure of the IGBTs is observed. A relevant failure precursor is determined based on the results. A degradation model for the precursor is then built. It is expected that the key failure precursor determined in this study and the proposed degradation model can help avoid unexpected failure of IGBTs in driving equipment of smart factories.
An Overview of Useful Data and Analyzing Techniques for Improved Multivariate Diagnostics and Prognostics in Condition-Based Maintenance
Authors: Wagner Carolin , Saalmann Philipp , Hellingrath Bernd
Published: March 2024

The reliability of production machines gains in importance in today’s optimized and highly productive business environments. Unexpected machine breakdowns do not only lead to loss of production time and production outages but also to diminishing customer satisfaction due to deterioration in quality and declining availability of products. The condition-based maintenance (CBM) strategy aims at preventing these machine breakdowns through real-time monitoring of machine conditions. Sensor data are collected and analyzed using diagnostic and prognostic approaches to identify the type of fault and the remaining useful life. Identifying the reasons and time of breakdowns fosters improved planning of maintenance and spare parts demand, leading to higher machine reliability. In general, machine sensor data are regarded as a useful source of information to assess the machine’s operating condition. However, in some specific cases, the machine sensors lack the ability to correctly represent the health of the machine or the specific component under consideration. Therefore, additional information by further available data is required to improve diagnostic and prognostic techniques for more accurate and precise analysis. Current research focuses on the analysis of sensor data for condition-based maintenance, while other data like the operating history and environment temperature have only been considered to a limited extend so far. Hence, this paper gives an overview on potential data for machine health assessment and remaining useful life prediction in condition-based maintenance. Furthermore, corresponding approaches and techniques for fault diagnostics and prognostics are presented targeting the analysis of individual data sources as well as of multivariate settings featuring multiple integrated data sources.
Data-Driven Prognostics of Lithium-Ion Rechargeable Battery using Bilinear Kernel Regression
Authors: Hubbard Charlie , Bavlsik John , Hegde Chinmay , Hu Chao
Published: March 2024

Reliability of lithium-ion (Li-ion) rechargeable batteries has been recognized as of high importance from a broad range of stakeholders, including battery manufacturers, manufacturers of battery-powered devices, regulatory agencies, researchers, and the public. Assessing the current and future health of Li-ion batteries is essential to ensure the batteries operate safely and reliably throughout their lifetime. This paper presents a new data-driven approach for prediction of battery remaining useful life (RUL) in the presence of corruptions (or errors) in capacity features. The approach leverages bilinear kernel regression to build a nonlinear mapping between the capacity feature space and the RUL state space. Specific innovations of the approach include: i) a general framework for robust sparse prognostics that effectively incorporates sparsity into kernel regression and implicitly compensates for errors in capacity features; and ii) two numerical procedures for error estimation that efficiently derives optimal values of the regression model parameters. Results of 10 years’ continuous cycling test on Li-ion prismatic cells suggest that the proposed approach achieves robust RUL prediction despite random noise in the capacity features.
Detection of Cracks in Shafts via Analysis of Vibrations and Orbital Paths
Authors: Peretz R. , Rogel L. , Bortman J. , Klein R.
Published: March 2024

Shafts are often subjected to difficult operating conditions in high-performance rotating equipment such as compressors, steam and gas turbines, generators and pumps. As a result, shafts are susceptible to fatigue failures due to transverse cracks. In this study, vibration monitoring and orbital paths observation were used to detect the presence of a flaw in a shaft. Two types of flaws were tested: a straight slot, and a fatigue crack. For both flaw types, specimens of different depths were examined in order to assess the detection capability. A new approach to examine vibrations at the critical speed is proposed; this speed is chosen because of the strong connection to the basics of the physical problem. Orbital paths are suggested as means for fault detection as well. The presence of a straight slot in the shaft was found to be related to a decrease in the natural frequency and to a decrease in amplitude of the first order at critical speed. For the fatigue crack, a consistent trend in critical speed and in amplitude was not seen as crack depth grew. A new method to detect the change in the shaft natural frequency is proposed. The combination of two indicators, change in critical speed and change in amplitude at critical speed, are suggested for classification of flaw size. For the straight slot case, the method proposed was able to distinguish between different fault depths.
A Fusion Method Based on Unscented Particle Filter and Minimum Sampling Variance Resampling for Lithium-ion Battery Remaining Useful Life Prediction
Authors: Chen Jiayu , Zhou Dong , Lu Chuan
Published: March 2024

It is important to predict the capacity of lithium-ion battery for future cycles to assess its health condition and to estimate remaining useful life (RUL). Particle filter approaches are widely applied into the estimation of battery capacity. However, after several iterations, the degeneracy and impoverishment of particles can cause unreliable and inaccurate prediction results in particle filter (PF). In this paper, a fusion method is proposed by integrating unscented Kalman filter (UKF) and minimum sampling variance resampling (MSVR) into the standard PF for RUL prediction of batteries. The UKF is employed to generate the proposal distribution of particles, which is used by PF to calculate the weights of particles. Next, the MSVR algorithm is introduced for performing resampling procedure to improve the performance. Finally, the performance of the proposed method is validated and compared to other predictors with four different battery datasets from NASA. According to the results, the integrated method has high reliability and prediction accuracy.
Flight Anomaly Tracking for Improved Situational Awareness: Case Study of Germanwings Flight 9525
Authors: Yasar Murat
Published: March 2024

Surveillance technologies play an important role in civil aviation safety by providing situational awareness to air traffic controllers and pilots. Tens of thousands of aircrafts operate daily, and each one of them needs to be tracked by combining data from several data sources including ADS-B and radar data for maintaining the safety of airspace. These heterogeneous data sources are aggregated together with schedule and flight status data from airlines and airports. This aggregate data is used to provide appropriate flight tracking of individual aircrafts and helps ensuring that the air traffic operates with maximum safety and minimum delays. This is achieved by a complex system of command centers, control towers, radar ground stations, and automated surveillance equipment. As air travel grows each year, global aviation safety continues to improve thanks to these sophisticated systems. Yet, it is unrealistic to expect that the system would detect, identify and respond to all flight anomalies. As was the case in Germanwings flight 9525, the flight anomalies that are not detected in time may result in catastrophes. This paper analyzes the unfortunate case of Germanwings flight 9525 and proposes an automated flight anomaly detection technology to improve situational awareness for air traffic controllers and pilots, and enhance aviation safety.
PHM Decision Support under Uncertainty
Authors: Yasar Murat , Lovett Teems E.
Published: March 2024

Decision support systems aim to improve the quality of services and help operators perform their duties faster, more accurately and more efficiently by providing an immense amount of knowledge. As human operators cannot convey their complete understanding of the situation to the system, decision support systems face the challenge of interpreting human intent based on operator inputs, which introduces a high level of uncertainty into the system. In this paper, a decision support system is used for determining system health status as a decision aid to the operator. The goal of this work is to supplement sensor data with human inputs in a prognostic health management environment while minimizing the effects of uncertainty and to provide situational awareness for the operator.
Reducing Tachometer Jitter to Improve Gear Fault Detection
Authors: Bechhoefer Eric , He Dave
Published: March 2024

Much research has been published on gear fault analysis techniques, almost all based on the time synchronous average (TSA). These include and are not limited to: residual analysis, energy operator, energy ratio, amplitude modulation, frequency modulation, sideband index, zeroorder figure of merit, etc. Because the TSA is based on tachometer zero cross times for a key phasor, the performance of these analyses is dependent on the quality of the tachometer data. The tachometer signal always has jitter, due to electrical noise, magnetic noise, or manufacturing spacing error of the tachometer target (e.g. gear tooth spacing). By implementing a novel zero phase filter to  reduce tachometer jitter, large improvements in the fault detection were observed. For a known gear fault, the separability, related to fault detection, increased from 10 to 25%.