bluuebunny/crossref_metadata_2025 huggingface dataset:
Fault Diagnosis of Gas Turbine Engine LRUs Using the Startup Characteristics
Authors: Kim Kyusung , Uluyol Onder , Parthasarathy Girija , Mylaraswamy Dinkar
Published: March 2024

This paper introduces a feature-extraction method to characterize gas turbine engine dynamics. The extracted features are used to develop a fault diagnosis and prognosis method for startup related sub-systems in gas turbine engines - the starter system, the ignition system and the fuel delivery system.
The startup of a gas turbine engine from ignition to idle speed is very critical not only for achieving a fast and efficient startup without incurring stall, but also for health monitoring of many subsystems involved. During startup, an engine goes through a number of phases during which various components become dominant. The proposed approach physically monitors the relevant phases of a startup by detecting distinct changes in engine behavior as it manifests in such critical variables as the core speed and the gas temperature. The startup process includes several known milestones, such as starter-on, light-off, peak gas temperature, and idle. As each of these is achieved, different engine components come into play and the dynamic response of the engine changes. Monitoring engine speed and exhaust gas temperature and their derivatives provides valuable insights into engine behavior.
The approach of the fault diagnosis system is as follows. The engine startup profiles of the core speed (N2) and the gas temperature are obtained and processed into a compact data set by identifying critical-to-characterization instances. The principal component analysis is applied to a number of parameters, and the fault is detected and mapped into three engine component failures which are the starter system failure, the ignition system failure, and the fuel delivery system failure.
Analyzing Imbalance in a 24 MW Steam Turbine
Authors: DaghighiAsli Afshin , Rezaie Vahid , Hayati Leila
Published: March 2024

Imbalance in critical rotary equipment is one of the most important factors, which should be controlled to prevent great damages. In this case study we are discussing about a 24 MW steam turbine, which drives a propane compressor. The radial vibration on the DE side of the turbine was growing gradually to a high level close to the alarm's value. Using FFTs, time signals, orbit diagrams, and phase measurement led us to believe that the rotor became imbalanced. After tripping and disassembling the turbine, we found out, some blades of the impulse stage of HP section got broken. Changing the rotor with the spare one, and repair the damaged rotor, worked out. It was concluded that using the vibration analysis technique is an effective method to find critical rotating equipment’s faults at the earliest levels. And performing the essential correcting tasks to prevent secondary damages and specially decrease of production.
Economic reasoning for Asset Health Management Systems in volatile markets
Authors: Gutsche Katja
Published: March 2024

With respect to the growing demand in asset reliability, availability, maintainability, safety and productivity (RAMS-LCC) diagnosis and prognostic asset health management (PHM) systems provide more detailed asset health information which allows improved maintenance decision-making. This gives the opportunity for a more efficient, safer system operation (e.g. aircraft, production facilities) and therefore a more competitive enterprise. Of course, the implementation and use of PHM causes recurring and non-recurring costs, which have to be at least covered by the savings due to benefits achieved by cost avoidance through better asset health knowledge. The economic justification is essential for a positive decision upon the installation of PHM. This becomes more complex as the benefits depend on the operation circumstances which then are strongly influenced by the market situation. The market situation is strongly determined by the market demand, number of competitors and speed of technological changes. As these parameters are especially relevant in the producing industry, this shall be the system of choice in this paper. The question to be raised is how much the economical attractiveness of PHM systems correlates with an increase in market impermanence as to be seen globally in most market segments.
Design for Availability – Flexible System Evaluation with a Model Library of Generic RAMST Blocks
Authors: Fasol Dipl.-Ing. Dieter , Münker Dr.-Ing. Burkhard
Published: March 2024

Tailoring a complex system to meet given availability requirements is a challenging task in the design process. Besides compiling the applicable mtbf and mttr figures of the required components, the specific set of algebraic rules has to be identified and applied to compute the overall predicted availability of the designated system functions for the individual architecture and for individual usage profiles. While on the other hand model based methods have meanwhile become established in the system development process, such time- and effort-saving simulation-based approachesare by far not so common in the field of RAMSTanalyses. This contribution reports about an approach of amending a reusable library of functional component models - originally designed to explore the effect of assumed failures in complex networks by simulation - and applying it to compute the availability of a generic launcher system. Here the design engineer is faced with the complex task to find an architecture to guarantee a specified availability of the firing function with the given resource items onboard. Developed within the tool environment RODON, thedeveloped prototypical library enables a quick evaluation of structural design alternatives of the selected launcher application. On top it supports the full range of RAMSTanalyses - like computation of cause-effect-relationships for FMEA, automatic drawing of FTAs for hazards of loosing designated system functions or systematic evaluation of the Diagnostic Coverage resp. potentially monitor or reconfiguration strat1egies - based on the same single model. Further generalization activities are going on. Since the model is composed of generic qualitative buildingblocks, it took much less time to develop than full quantitative or physical model descriptions usually need. Although this qualitative representation implies certain limitations, as the authors experienced this is compensated by the advantage of an easier understanding by the modeler and quick adaption and to new principal architectures, while still providing sufficient system insight and result. Driven by requirements from the industrial application, ideas for further library extension are being discussed to support also questions regarding repair procedures and time, resource allocation or cover even economical aspects. The paper sections focus on the description of the launcher design task, a short introduction to the applied tool, the library development and application and finally the results and outlook.
Knowledge-Based System to Support Plug Load Management
Authors: Da Silva Jonny Carlos , Poll Scott
Published: March 2024

Electrical plug loads comprise an increasingly larger share of building energy consumption as improvements have been made to Heating, Ventilation, and Air Conditioning (HVAC) and lighting systems. It is anticipated that plug loads will account for a significant portion of the energy consumption of Sustainability Base, a recently constructed high-performance office building at NASA Ames Research Center. Consequently, monitoring plug loads will be critical to achieve energy efficient operations. In this paper we describe the development of a knowledge-based system to analyze data collected from a plug load management system that allows for metering and control of individual loads. Since Sustainability Base was not yet occupied at the time of this investigation, the study was conducted in another building on the Ames campus to prototype the system. The paper focuses on the knowledge engineering and verification of a modular software system that promotes efficient use of office building plug loads. The knowledge-based system generates summary usage reports and alerts building personnel of malfunctioning equipment and unexpected plug load consumption. The system is planned to be applied to Sustainability Base and is expected to identify malfunctioning loads and reduce building energy consumption.
Major Challenges in Prognostics: Study on Benchmarking Prognostics Datasets
Authors: Eker O. F. , Camci F. , Jennions I. K.
Published: March 2024

Even though prognostics has been defined to be one of the most difficult tasks in Condition Based Maintenance (CBM), many studies have reported promising results in recent years. The nature of the prognostics problem is different from diagnostics with its own challenges. There exist two major approaches to prognostics: data-driven and physics-based models. This paper aims to present the major challenges in both of these approaches by examining a number of published datasets for their suitability for analysis. Data-driven methods require sufficient samples that were run until failure whereas physics-based methods need physics of failure progression.
Finite Element based Bayesian Particle Filtering for the estimation of crack damage evolution on metallic panels
Authors: C. Sbarufatti , M. Corbetta , A. Manes , M. Giglio
Published: March 2024

A lot of studies are nowadays devoted to structural health monitoring, especially inside the aeronautical environment. In particular, focusing the attention on metallic structures, fatigue cracks represent both a design and maintenance issue. The disposal of real time diagnostic technique for the assessment of structural health has led the attention also toward the prognostic assessment of the residual useful life, trying to develop robust prognostic health management systems to assist the operators in scheduling maintenance actions. The work reported inside this paper is about the development of a Bayesian particle filter to be used to refine the posterior probability density functions of both the damage condition and the residual useful life, given a prior knowledge on damage evolution is available from NASGRO material characterization. The prognostic algorithm has been applied to two cases. The former consists in an off-line application, receiving diagnostic inputs retrieved with manual structure scanning for fault identification. The latter is used on-line to filter the input coming from a real-time automatic diagnostic system. A massive usage of FEM simulations is used in order to enhance the algorithm performances.
Data Management Backbone for Embedded and PC-based Systems Using OSA-CBM and OSA-EAI
Authors: Löhr Andreas , Haines Conor , Buderath Matthias
Published: March 2024

Cassidian is in the process of developing a comprehensive simulation framework for integrated system health monitoring and management research and development. One significant building block is to invite 1st class technology providers, e.g. Universities and SMIs, to provide innovative technologies and support their integration into the simulation framework. This paper is a joint presentation of Cassidian and Linova Software GmbH, a Cassidian preferred software provider.Prognostic Health Management (PHM) systems are commonly composed of disparate and distributed hard- and software components. Further, these components exchange vast amounts of data over a heterogeneous collection of communication channels. Any such system’s success depends upon an open, uniform, and performance-optimized solution for data management. A solution that includes: data definition, data communication, and data storage. The Open System Architecture for Condition-based Maintenance (OSA-CBM) and Open System Architecture for Enterprise Application Integration (OSA-EAI) are complementary reference architectures and represent an emerging standard for application domain-independent asset and condition data management. Herein, we will report on our experiences while implementing a data management backbone based on OSA-CBM and OSA-EAI for a simulation environment supporting PHM systems in the aerospace domain. Our work encompasses both airborne embedded systems and ground-based PC systems. While we can generally confirm the feasibility of OSA-CBM and OSA-EAI, we found several implementation recommendations unsuited to real-time operating conditions. To address these issues, we propose work towards standardizing non-XML-based transportation formats for OSA-CBM data packets. Further, we discovered issues specific to implementing the OSA-EAI data model in the aerospace domain. These issues drove our proposal to extend the OSA-EAI database model, where we seek to optimize its usability for analytical tasks. To underline the feasibility of our solutions, we provide empirical evidence drawn from our work. The conclusion is a summary of our experience and the direction of future work in the area of PHM system design for aircraft maintenance. In total, our contribution to the community is best seen from a practitioner’s perspective. We aim to establish best practices for and contribute to the evolution of OSA-CBM and OSA-EAI.
An Approach to the Health Monitoring of a Pumping Unit in an Aircraft Engine Fuel System
Authors: Lamoureux Benjamin , Massé Jean-Rémi , Mechbal Nazih
Published: March 2024

This paper provides an approach for health monitoring through an early detection of failure modes premises. It is a physics-based model approach that captures the knowledge of the system and its degradations. The component under study is the pumping unit such as those found in aircraft engines fuel systems. First, a complete component analysis is performed to determine its potential degradation and a physics-based relevant component health indicator (CHI) is defined. Then, degradations are modelled and their impacts on the CHI are quantified using an AMESim® physics-based model. Assuming that in-flight measures are available, a model updating is performed and a healthy distribution of the CHI is computed. Eventually, a fault detection algorithm is developed and statistical validation is performed through the computation of key performance indicators (KPI). In parallel, a degradation severity indicator (DSI) is defined and prognostic is performed based on the monitoring of this DSI.
Feature Extraction and Evaluation for Health Assessment and Failure Prognostics
Authors: Medjaher K. , Camci F. , Zerhouni N.
Published: March 2024

The estimation of Remaining Useful Life (RUL) of industrial equipments can be realized on their most critical components. Based on this assumption, the identified critical component must be monitored to track its health state during its operation. Then, the acquired data are processed to extract relevant features, which are used for RUL estimation. This paper presents an evaluation method for the goodness of the features, extracted from raw monitoring signals, for health assessment and prognostics of critical industrial components. The evaluation method is applied to several simulated datasets as well as features obtained from a particular application on bearings.
Synthetic Data for Hybrid Prognosis
Authors: Mishra Madhav , Leturiondo Urko , Galar Diego
Published: March 2024

Using condition-based maintenance (CBM) to assess machinery health is a popular technique in many industries, especially those using rotating machines. CBM is relevant in environments where the prediction of a failure and the prevention and mitigation of its consequences increase both profit and safety. Prognosis is the most critical part of this process and the estimation of Remaining Useful Life (RUL) is essential once failure is identified. This paper presents a method of synthetic data generation for hybrid model-based prognosis. In this approach, physical and data-driven models are combined to relate process features to damage accumulation in time-varying service equipment. It uses parametric models and observer-based approaches to Fault Detection and Identification (FDI). A nominal set of parameters is chosen for the simulated system, and a sensitivity analysis is performed using a general-purpose simulation package. Synthetic data sets are then generated to compensate for information missing in the acquired data sets. Information fusion techniques are proposed to merge real and synthetic data to create training data sets which reproduce all identified failure modes, even those that do not occur in the asset, such as Reliability Centered Maintenance (RCM), Failure Mode and Effect Analysis (FMEA). This new technology can lead to better prediction of remaining useful life of rotating machinery and minimizing and mitigating the costly effects of unplanned maintenance actions.
Health Management System for the Hydraulic Servoactuators of Fly-by-wire Primary Flight Control Systems
Authors: Mornacchi Andrea , Vignolo Matteo
Published: March 2024

Aircraft maintenance is one of the most important cost items faced by the operators of air fleets and is a major contributor to the aircraft life cycle cost. An aircraft fly-by-wire flight control system has a total of primary flight control actuators ranging from 10 to 20 depending on the aircraft type, with a failure rate of 1/1000 flight-hours; therefore, a health monitoring system for primary flight control actuators, able to recognize an actuator degradation in its early stage could greatly contribute to optimize the maintenance operations, reduce the airplane downtime and prevent missions interruptions.This note presents the initial part of an ongoing research project aimed at developing a prognostic and health management system for fly-by-wire primary flight control actuators. A key feature of the project is to develop a PHM system for these actuators suitable for the flight control actuators of legacy airplanes, which are poised to operate for still a long time, and not only for those of new aircraft. The primary flight control actuators of fly-by-wire flight control systems of existing aircraft are electrohydraulic servoactuators with a typical configuration and complement of transducers, and there is no practical possibility of introducing additional sensors. For this reason, the research activity was directed towards the study of algorithms able to identify faults only by using the already available information of the servoactuators state variables.The implemented algorithms are a combination of mathematical and neural network based ones, and the identification of degradations was performed by the analysis of the response of the servoactuators to a sequence of selected stimuli provided in preflight or postflight. The servovalve current and the feedback position are processed by dedicated algorithms in order to obtain significant indicators of the servocatuator health condition. The values of the indicators obtained during the sequence of stimuli are analyzed in combination with those obtained in the past.This is performed by the neural network part of the algorithm which allows a reliable identification of presence and of type of a degradation.The results obtained from the initial part of the research activity are interesting and encouraging. Individual degradations of the servoactuator parameters have so far been addressed and the algorithms for identifying them have been developed. All that makes up the foundations of the future research activity which will be focused on analyzing the effects of simultaneous multiple degradations and to the estimation of the remaining useful life.
Some Diagnostic and Prognostic Methods for Components Supporting Electrical Energy Management in a Military Vehicle
Authors: Bastard Guillaume
Published: March 2024

This work investigates the field of Integrated Vehicle Health Management (IVHM) and more specifically on the components which are producing or consuming electricity. Firstly, diagnostic and prognostic characteristics are defined. This allows later, from the mapped characteristics, to sort the most relevant methods for critical components. The mapping leads finally to define some scientific issues to be solved in order to improve the diagnostic and prognostic of such components.
Duplex ball bearing outer ring deformation- Simulation and experiments
Authors: Battat Mor , Kogan Gideon , Kushnirsky Alex , Klein Renata , Bortman Jacob
Published: March 2024

This paper presents a research of deformations influence on duplex ball bearings dynamic behavior. Despite the common use of duplex ball bearings, bearings subcomponents deformations are not thoroughly investigated. In order to investigate these effects, this study integrates the outcome of a 3D dynamic model, developed for assessment of the defect pattern and experimental results from a full scale CH-53 Swashplate test rig. The ability to withstand high radial and bi-directional axial loads makes duplex bearings common in aircraft applications and specifically in helicopter rotors. The swashplate of the CH-53 is constructed of duplex angular contact ball bearings. Two spacers, internal between the static inner rings and external, between the rotating outer rings support the bearing rings. A structural defect is formed by a faulty external spacer, thus causing a lack of support to the top bearing and deformation of the outer rings. Model results indicate that the lack of support has a defect pattern in both radial and axial directions. Test rig data acquired by accelerometers was analyzed by several diagnostic techniques including order tracking, envelope analysis and dephased algorithm in order to recognize the simulated pattern.
Evaluation of the Training Process of three different Prognostic Approaches based on the Gaussian Process
Authors: Preusche Christian , Anger Christoph , Klingauf Uwe
Published: March 2024

Data-driven prognostic approaches like Gaussian Process combined with Unscented Kalman Filter (GPUKF) are promising methods for predicting the Remaining Useful Lifetime (RUL) of a degrading component. Whereas the Gaussian Process (GP) is appropriate to derive a suitable degradation model by means of a set of training data, the Unscented Kalman Filter (UKF) employs this model to determine the prediction and its uncertainty.Since a degradation process is highly stochastic, it is assumed that by applying more sets of training data the accuracy and precision of the GPUKF is increased. In order to examine the performance enhancement two different approaches are investigated in this paper: First, a single GP is trained with all available data sets. The second approach combines several GPs (each created with a data set of one degradation process) by extending the GPUKF with a Multiple Model Method. The development of a third prognostic approach aims at the investigation of the UKF as a suitable tool for the prognostic algorithm. Therefore, a third method applies a Particle Filter in combination with the GP. For the evaluation of the aforementioned prognosis algorithms according to their precision and accuracy a set of prevalent performance metrics like the Prognostic Horizon and the Mean Average Percentage Error of a prediction is analyzed.The validity of the determined results is increased by considering the variance of certain metrics over several units under test. Moreover, particular focus is set on the examination of the performance change caused by the use of more training data sets. In order to quantify this process known metrics are extended. The evaluation is based on simulated data sets, which are generated by an exponential degradation model.
The analysis of the implemented algorithms indicates that the applied metrics are in a comparable range. However, the three approaches reveal a different behaviour concerning the convergenceof the performance values according to the number of training data. In particular cases there is even a decline in accuracy and precision attend by a rising number of training data.
Adaptive Driving Situation Characterization for Predicting the Driving Load of Electric Vehicles in Uncertain Environments
Authors: Oliva Javier A. , Bertram Torsten
Published: March 2024

Battery powered electric vehicles (EVs) have emerged as a promising solution for reducing the consumption of fossil fuels in modern transportation systems. Unfortunately the battery pack has a low energy storage capacity, which causes the driving range of the EV to become very limited. It is therefore essential to properly characterize the different driving situations of the vehicle in order to better predict the driving load along the road ahead and to better estimate the remaining driving range (RDR). However, this prediction cannot be achieved straightforward due to sources of uncertainty introduced by the randomness of the driving environment. In this paper a novel approach for characterizing driving situations and for predicting the driving load of an EV is presented. The prediction of the driving load occurs in a model-based fashion, where the model input variables are modeled as discretetime Markov processes. An approach for estimating the transitionprobabilities between Markov states in the presence of sparse driving data is introduced. Furthermore, to capture the changes in the driving environment a Bayes-based methodology for recursively updating the established transition probabilities is presented. The validity of the proposed approach is illustrated through simulation and by a series of experimental case studies.
A Particle Filtering-Based Approach for the Prediction of the Remaining Useful Life of an Aluminum Electrolytic Capacitor
Authors: Rigamonti Marco , Baraldi Piero , Zio Enrico , Astigarraga Daniel , Galarza Ainhoa
Published: March 2024

This work focuses on the estimation of the Remaining Useful Life (RUL) of aluminum electrolytic capacitors used in electrical automotive drives under variable and non-stationary operative conditions. The main cause of the capacitor degradation is the vaporization of the electrolyte due to a chemical reaction. Capacitor degradation can be monitored by observing the capacitor Equivalent Series Resistance (ESR) whose measurement, however, is heavily influenced by the measurement temperature, which, under non-stationary operative conditions, is continuously changing. In this work, we introduce a novel degradation indicator which is independent from the measurement temperature and, thus, can be used for real applications under variable operative conditions. The indicator is defined by the ratio between the ESR measured on the degraded capacitor and the ESR expected value on a new capacitor at the present operational temperature. The definition of this indicator has required the investigation of the relationship between ESR and temperature on a new capacitor by means of experimental laboratory tests. The prediction of the capacitor degradation and its failure time has been performed by resorting to a Particle Filtering-based prognostic technique.
Economic Aspects of Prognostics and Health Management Systems in the Wind Industry
Authors: Geiss Christian T.
Published: March 2024

Since Wind Turbines are one of the most dynamically stressed structures, all parts should be subjected to Prognostics and Health Management System. This is especially true for the supporting structure since it is exposed to high fatigue loads. The current technical trend in the O&M business is to improve the life-time of these supporting structures. In particular, when considering the supporting structure of a wind turbine from a civil engineering perspective; a long term approach is most beneficial in financial, ecological and social aspects. To meet the challenge of managing the life-time of wind turbine supporting structures efficiently, it is necessary to develop technical concepts assessing the consumed life-time of a wind turbine. Future PHM systems of wind turbines must include this function.The global O&M market in the wind energy industry grew in the period from 2005 to 2011 at a rate of around 18 per cent, annually. The main growth driver is the aging overall turbine park. Especially in the European onshore wind market there will be a profit migration of the O&M business at the expense of new construction until 2020. Until the year 2020, three quarters of the total profit in the wind energy industry will be occupied by O&M services (Oliver Wymann, 2011).This paper discusses the special economic aspects of Prognostics and Health Management Systems focusing on a remaining lifetime prediction as a basic maintenance system in application within the wind industry. Besides studies of the future O&M market development, concepts to lower the levelized cost of energy through PHM from a macroeconomic perspective will also be discussed.Keywords: Wind O&M market, Lifetime management, Levelized cost of energy, return on investment analysis.
Cure Monitoring of Composite Carbon/Epoxy through Electrical Impedance Analysis
Authors: Mounkaila M. , Camps T. , Sassi S. , Marguerès Ph. , Olivier Ph. , Fourniols J.Y. , Escriba C.
Published: March 2024

Composite materials are increasingly used in aeronautic; they offer many benefits such as mechanical strength, mass and consumption reduction. However, their process development needs to be known and controlled, in order to adjust the process parameters and optimize the characteristics of structures made from these materials. This paper is focused on impedance spectroscopy measurement and analysis technique to characterize material’s properties. In fact, the composites based on carbon fiber have electrical proprieties; therefore a three-dimensional modeling of the electrical conduction in the material is established by using a distributed allocation of an electrical resistance (RP) in parallel with a capacitance (CP). Then, thin electrodes (40 μm thick) are inserted inside the material and a specific impedance measurement bench is developed to perform real-time measurements of RP and CP on unidirectional (UD) mono-ply and multi-plies samples. During curing (in an oven) the change in values of both RP and CP in different stages of the curing cycle is showed. Then, problems that occur during the curing cycle (layup defect, loss of vacuum) were detected by a large gap of the measured electrical parameters in comparison with the ordinary case. Therefore, by this electrical measurement, we present a way to ensure an automated real-time monitoring of the composite curing process.
Identification and classification protocol for complex systems
Authors: Tran N-H. , Bouaziz M-F. , Zamaï E.
Published: March 2024

This paper proposes a test protocol for drift identification and classification in a complex production system. The key objective here is to develop a classifier for failure causes where variables depend on a set of measured parameters. In the context of our work, we assume that the drift problem of a production system is generally observed in control products phase. The model proposed in this paper for failure causes classification is structured in the form of a causeseffects graph based on Hierarchical Naïve Bayes formalism (HNB). Our key contribution in this is the methodology thatallows developing failure causes classification test model in the complex and uncertain manufacturing context.
An efficient simulation framework for prognostics of asymptotic processes- a case study in composite materials
Authors: Chiachío Manuel , Chiachío Juan , Saxena Abhinav , Rus Guillermo , Goebel Kai
Published: March 2024

This work presents an efficient computational framework for estimating the end of life (EOL) and remaining useful life (RUL) by combining the particle filter (PF)-based prognostics with the technique of Subset simulation. It has been named PFP-SubSim on behalf of the full denomination of the computational framework, namely, PF-based prognostics based on Subset Simulation. This scheme is especially useful when dealing with the prognostics of evolving processes with asymptotic behaviors, as observed in practice for many degradation processes. The effectiveness and accuracy of the proposed algorithm is demonstrated through an example for predicting the probability density function of EOL for a carbon-fibre composite coupon subjected to an asymptotic fatigue degradation process. It is shown that PFP-SubSim algorithm is efficient, and at the same time, fairly accurate in obtaining the probability density function of EOL and RUL as compared to the traditional PF-based prognostic approach reported in the PHM literature.
Fault Diagnostics of Planet Gears in Wind Turbine Using Autocorrelation- based Time Synchronous Averaging (ATSA)
Authors: Ha Jong Moon , Park Jungho , Youn Byeng D. , Jung Yoong Ho
Published: March 2024

A planetary gearbox is widely used in various rotating systems because it can be used as a speed reducer or increaser without change in direction of shaft while transferring great driving power. Despite many attempts it is still challenging to diagnose potential faults of the planetary gearbox because of multiple contacts and axis rotation of planet gears resulting in complex vibration characteristics. This paper thus presents an original method to isolate vibration signals induced by the planet gears from the complex vibration signals for fault diagnostics of the planetary gearbox. First, an in-depth study on the vibration characteristics of planet gears is presented using the autocorrelation function of the vibration signal. The autocorrelation-based time synchronous averaging (ATSA) method is then developed for the isolation of the vibration signals produced by the planet gears. The vibration signals were utilized for extracting health related data which facilitate the efficient fault diagnostics of the planet gears. Case study with a wind turbine testbed showed that the proposed method can diagnose the root crack of the planet gears.
On-board SHM System Architecture and Operational Concept for Small Commuter Aircra
Authors: Finda Jindřich , Hédl Radek
Published: March 2024

Significant R&D progress has been done in the area of SHM technologies in recent years. However real SHM application on aircraft board is still challenging and puts specific requirements on the SHM system design and operation. These challenges include assurance of reliable and provable damage detection capabilities, taking over decision-making responsibilities instead of a human inspector and other challenges related to on-board installation and operation during the flight. Further, minimal weight and dimension, and system reliability and durability should be considered. Due to these challenging requirements the SHM has not been widely implemented in aerospace industry yet.The paper deals with system architecture and operational concept of SHM system for L-410 NG commuter aircraft. The SHM system is based on excitation, sensing and analysis of ultrasonic guided waves using PZT actuators / sensors. The SHM system is designed for monitoring of PSEs of metallic airframe that are hard to access or completely inaccessible for common inspection methods used in the aircraft maintenance. The design puts emphasis on integration of the SHM system within aircraft avionic system in order to achieve highly automated data acquisition and data transfer process to make the health data available for on-ground analysis. Finally, scenario of the SHM system operation in accordance to the L-410 NG maintenance plan is proposed in the paper. The scenario assumes replacement of common inspections that are done within regular maintenance checks by the automated inspections using SHM system. Challenges of the proposed scenario from the point of view of the aircraft certification and operation are discussed as well.
Key factor identification for energy consumption analysis
Authors: Medina-Oliva Gabriela , Voisin Alexandre , Monnin Maxime , Leger Jean-Baptiste , Iung Benoit
Published: March 2024

Nowadays the economic, environmental and societal issues concerning energy consumption require a deeper understanding of the factors influencing it. The influencing factors could concern the technical characteristics of the systems, the operational conditions and usage of equipment, the environmental conditions, etc. To understand the main contributing factors a knowledge model with the influencing factors is formalized in the form of an ontology. This ontology model allows to distinguish in a general way the main concepts (i.e. factors) that show higher consumption  trends. This way, a preliminary analysis reflecting the key influencing factors could be perform in order to focus later on a deeper analysis with data mining techniques. This paper focuses on the  formalizationof an ontology model in the marine domain for energy consumption purposes. Then, the approach is illustrated with an example of a fleet of diesel engines.
Practical PHM for Medium to Large Aerospace Grade Li-Ion Battery Systems
Authors: Boost Mike , Hamblin Kyle , Jackson John , Korenblit Yair , Rajamani Ravi , Stevens Thom , Stewart Joe
Published: March 2024

In this paper we will discuss some practical aspects of health management for a rechargeable Li-ion battery system for aerospace applications. Industry working groups have developed guidance for the flight certification of this type of battery system, and we will show how this guidance is used in the design. We will also discuss safety features embedded in the battery system related to industry guidance; including cell energy balancing, internal temperature monitoring and emergency fuses. The keys to battery prognostics and health management (PHM) are analytic State of Charge (SoC) and State of Health (SoH) algorithms implemented in these battery systems. We show how these are developed and how we have tested them before deployment. These battery systems also collect data that is made available to the aircraft processing systems, e.g., Aircraft Health Management System, On-board Maintenance System, etc.. This allows for near real-time confirmation of proper operation of these battery systems as well as adherence to MSG-3 maintenance standards. We close with a brief discussion of the practical limitations in our implementation and a discussion of our ongoing and future development in this area.
Networked Modular Technology for Integrated Aircraft Health Monitoring: Application to Rotary Structures
Authors: Boukabache Hamza , Robert Vincent , Escriba Christophe , Fourniol Jean-Yves. , Furlan Jean-Philippe
Published: March 2024

The largest variable cost to aircraft’s manufacturers and flying companies is unscheduled maintenance. Therefore, developing efficient and modular PHM system capable to scale different architectures topologies for in flight and on ground health monitoring could be cost effective, since it brings indication and warning prior to damage occurring.In this paper, we propose an innovative diagnostic and prognostic health system based on a combination of modular acquisitions interfaces and processing units.An embedded JTFA (Joined Time-Frequency Analysis) method based on STFT (Short-Time Fourier Transform) or Wigner-Ville transforms are used to extract a relevant signature. The proposed algorithms and PHM system technology are applied for diagnosis of mechanical flows in a high speed rotating gear of a demonstrator machine. A detailed description of data management and rooting from vibration sensors to the processing unit will be exposed.Finally, a proof-of-concept experiment will be designed to demonstrate the integration of all the described system elements to detect any damage or anomaly into the monitored structure.
Placement of alert thresholds on abnormality scores
Authors: Massé Jean-Rémi , Humeau Aurore , Lalonde Pierre , Alimardani Armand
Published: March 2024

The “s or more threshold trespassings out of N consecutive watch periods” detection verification strategy is known to offer advantages in terms of threshold value not too extreme under the constraint of low false alert rate, PFA. Typically PFA < 5%. The definition of PFA here considered is P(No degradation|Alert). It means the probability that there is no degradation given that degradation has been detected. The alert threshold placement has previously been addressed in the case where the abnormality score with no degradation has a stationary distribution and may be approached with a continuous non parametric Parzen distribution. This is illustrated on an abnormality score of the daily lubricant consumption estimation of an aircraft engine. The watch period is a day. The N consecutive watch periods are seven consecutive service days. The s or more trespassings are six or more trespassings out of seven consecutive days. In such configuration, the threshold is 0.21 l/h, which is inside the observed distribution. With an abnormality alert strategy with no verification, i.e. s = N = 1, the threshold is a more extreme value of 0.31 l/h which is outside the observed distribution. Two steps were considered. Step 1: Learning of the abnormality score distribution with no degradation by a non parametric Parzen fit. Step 2: Threshold set by quintile interpolation on the adjustment. This is extended to the case where the abnormality score with no degradation has a discrete distribution close to a Dirac distribution. This is typically the case for abnormality scores based on “out of range” counts for measurement chains along M clock increments of a watch period, corresponding to a flight cycle. With no degradation, most of the counts during a flight, but not all, are zero. Another example is an abnormality score based on a rough quantification of the time, “t SAV open”, between the open command and the start of movement of a starter air valve, during a watch period corresponding to a start sequence. With no degradation, most of the t SAV open of a start sequence are reported “zero”. Only a few start sequences trespass the few first quantification times. In these discrete cases close to Dirac the Parzen adjustment is no longer acceptable. A discrete degradation detection threshold, l, is set as a “l events or more count out of M” clock increments of a watch period, at each watch period for an “s out of N watch periods” confirmation strategy under the same constraint of P(No degradation| Alert) < PFA. This is done according to a binomial as well as a Poisson distribution on the number of events. Like in the continuous case two steps are considered. Step 1: Estimation of the ratio of discrete events with α confidence level based on the number, r, of events during a learning phase of I time increments over watch periods with no degradation. Step 2: Alert threshold set as the limit, l, on a watch period of size M for a “s out of N limit trespassings” detection strategy.
Smart Sensors for Condition Based Maintenance: a Test Case in the Manufacturing Industry
Authors: Pala Simone , Fumagalli Luca , Garetti Marco , Macchi Marco
Published: March 2024

Condition Based Maintenance (CBM) is a well-known concept and it has been demonstrated that it is the way ahead to prognostic maintenance for failure avoidance and for the reduction of maintenance cost.This paper presents an application for Condition Based Maintenance, with a specific focus on State Detection, according to MIMOSA OSA-CBM reference architecture. The papers aims at presenting peculiarity of development of such a kind of solution when considering the use of Smart Sensors instead of traditional devices.Indeed, breakthrough in CBM is expected from the development of ICT and embedded systems. This technology supply integrated chips implementing all the necessary circuitry to manage field data capture, data processing, local diagnosis, local feedback (where possible) and information transfer to the upper control levels. These so-called smart sensors exploit new technologies of micro sensors (MEMS, micro electro mechanical systems) and wireless communication together with the computing power of a microprocessor.In particular, applications related to maintenance and human safety appear to be very promising due to the unstructured nature of these domains, where self-configuring networks of intelligent devices can better comply with an ever changing and partially unpredictable environment.A test case is deployed on a typical manufacturing equipment: a robot. The objective of the test case presented by the paper is not to develop new diagnostic algorithms, but to implement some statistical analysis within a monitoring infrastructure built with Smart Sensors.The case of analysis that the paper will present grounds on the use of wireless sensor devices for temperature measures gathered on the electric motors of the robot. Then, data are transmitted through a wireless network to a receiver unit that accomplishes also elaboration by using statistical methods and then, thanks to a web-service communication, results are made available to external requests and users.An advisory is generated when something is out of the normal behaviour of the equipment. Finally, the user can check this information through the Human Machine Interface available via web-service.
Towards an Integrated COTS Toolset for IVHM Design
Authors: Niculita Octavian , Jennions Ian K , Valdez Miguel Medina
Published: March 2024

This paper describes an end-to-end Integrated Vehicle Health Management (IVHM) development process with a strong emphasis on the automation in creating functional models from 3D Computer Aided Design (CAD) system’s representation, throughout the implementation of this process. It has been demonstrated that functional analysis enhances the design and development of IVHM but this approach is not widely adopted by industry and the research community as it carries a significant amount of subjectivism. This paper is meant to be a guideline that supports the correctness through construction of a functional representation for a complex mechatronic system. The knowledge encapsulated in the 3D CATIA™ System Design environment was linked with the Maintenance Aware Design environment (MADe™) with the scope of automatically creating functional models of the geometry of a system. The entire process is documented step by step and it is demonstrated on a laboratory fuel system test rig. The paper is part of a larger effort towards an integrated COTS toolset for IVHM design. Another objective of the study is to identify the relations between the different types of knowledge supporting the health management development process when used together with the spatial and functional dimensions of an asset. The conclusion of this work is that a 3D CAD model containing the topological representation of a complex system can automate the development of the functional model of such a system.
Statistical Aspects in Neural Network for the Purpose of Prognostics
Authors: An Dawn , Kim Nam-Ho , Choi Joo-Ho
Published: March 2024

Neural network (NN) is a representative data-driven method, which is one of prognostics approaches that is to predict future damage/degradation and the remaining useful life of in-service systems based on the damage data measured at previous usage conditions. Even though NN has a wide range of applications, there are a relatively small number of literature on prognostics compared to the usage in other fields such as diagnostics and pattern recognition. Especially, it is difficult to find studies on statistical aspects of NN for the purpose of prognostics. Therefore, this paper presents the aspects of statistical characteristics of NN that are presumable in practical usages, which arise from measurement data, weight parameters related to the neural network model, and loading conditions. The Bayesian framework and Johnson distribution are employed to handle uncertainties, and crack growth problem is addressed as an example.