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A model-based prognostics method for fatigue crack growth in fuselage panels

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Chinese Journal of Aeronautics 2019年第2期32卷 396-408页

作者： Yiwei WANG Christian GOGU Nicolas BINAUD Christian BES Jian FU School of Mechanical Engineering and Automation Beihang University Institut Clément Ader (UMR CNRS 5312) INSA/UPS/ISAE/Mines Albi Université de Toulouse

This paper proposes a model-based prognostics method that couples the Extended Kalman Filter(EKF) and a new developed linearization method. The proposed prognostics method is developed in the context of fatigue crack propagation in fuselage panels where the model parameters are unknown and the crack propagation is affected by different types of uncertainties. The coupled method is composed of two steps. The first step employs EKF to estimate the unknown model parameters and the current damage state. In the second step, the proposed efficient linearization method is applied to compute analytically the statistical distribution of the damage evolution path in some future time. A numerical case study is implemented to evaluate the performance of the proposed method. The results show that the coupled EKF-linearization method provides satisfactory results: the EKF algorithm well identifies the model parameters, and the linearization method gives comparable prediction results to Monte Carlo(MC) method while leading to very significant computational cost saving. The proposed prognostics method for fatigue crack growth can be used for developing predictive maintenance strategy for an aircraft fleet, in which case, the computational cost saving is significantly meaningful.

关键词： Aircraft fuselage panels Extended Kalman filter Fatigue crack propagation Linearization method Model-based prognostics

The Use of High-Performance Fatigue Mechanics and the Extended Kalman/Particle Filters,for Diagnostics and prognostics of Aircraft Structures

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Computer Modeling in Engineering & Sciences 2015年第5期105卷 1-24页

作者： Hai-Kun Wang Robert Haynes Hong-Zhong Huang Leiting Dong Satya N.Atluri School of Mechanical Electronicand Industrial EngineeringUniversity of Electronic Science and Technology of China Center for Aerospace Research&Education University of CaliforniaIrvine Vehicle Technology Directorate US Army Research Laboratory Department of Hohai University China

In this paper,we propose an approach for diagnostics and prognostics of damaged aircraft structures,by combing high-performance fatigue mechanics with filtering ***&accurate deterministic analyses of fatigue crack propagations are carried out,by using the Finite Element Alternating Method(FEAM)for computing SIFs,and by using the newly developed Moving Least Squares(MLS)law for computing fatigue crack growth *** algorithms for simulating fatigue crack propagations are embedded in the computer program Safe-Flaw,which is called upon as a subroutine within the probabilistic framework of filter *** the extended Kalman as well as particle filters are applied in this study,to obtain the statistically optimal and semi-optimal estimates of crack lengths,from a series of noisy measurements of crack-lengths over *** the specific problem,a simple modification to the particle filter,which can drastically reduce the computational burden,is also *** on the results of such diagnostic analyses,the prognostics of aerospace structures are thereafter achieved,to estimate the probabilistic distribution of the remaining useful *** using a simple example of a single-crack near a fastener hole,we demonstrate the concept and effectiveness of the proposed *** paper thus forms the scientific foundation for the recently proposed concepts of VRAMS(Virtual Risk-Informed Agile Maneuver Sustainment)and Digital Twins of aerospace vehicles.

关键词： Diagnostics and prognostics fatigue mechanics extended Kalmanfilter particle filter

Special Issue on Machine Fault Diagnostics and prognostics

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Chinese Journal of Mechanical Engineering 2017年第6期30卷 1283-1284页

作者： Zhigang Tian Wilson Wang University of Alberta Edmonton Canada Lakehead University Thunder Bay Canada

Machine components and systems, such as gears, bearings, pipes, cutting tools and turbines, may experience various types of faults, such as breakage, crack, pitting, wear, corrosion. If not being properly monitored and treated, such faults can propagate and lead to machinery perfor- mance degradation, malfunction, or even severe compo- nent/system failure. It is significant to reliably detect machinery defects, evaluate their severity, predict the fault propagation trends, and schedule optimized maintenance and inspection activities to prevent unexpected failures. Advances in these areas will support ensuring equipment and production reliability, safety, quality and productivity.

关键词： Special Issue Machine Fault Diagnostics prognostics

prognostics and Remaining Useful Life Prediction of Machinery:Advances,Opportunities,and Challenges

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Journal of Dynamics, Monitoring and Diagnostics 2023年第1期2卷 1-12页

作者： JDMD Editorial Office Nagi Gebraeel Yaguo Lei Naipeng Li Xiaosheng Si Enrico Zio Editorial Office of JDMD Chongqing University of TechnologyChongqingPeople’s Republic of China H.Milton Stewart School of Industrial and Systems Engineering Georgia Institute of TechnologyAtlantaGAUSA Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System Xi’an Jiaotong UniversityXi’anPeople’s Republic of China Zhijian Laboratory Rocket Force University of EngineeringXi’anPeople’s Republic of China MINES Paris PSL Research UniversitySophia AntipolisFrance Energy Department Politecnico di MilanoMilanItaly

As the fundamental and key technique to ensure the safe and reliable operation of vital systems,prognostics with an emphasis on the remaining useful life(RUL)prediction has attracted great attention in the last *** this paper,we briefly discuss the general idea and advances of various prognostics and RUL prediction methods for machinery,mainly including data-driven methods,physics-based methods,hybrid methods,*** on the observations fromthe state of the art,we provide comprehensive discussions on the possible opportunities and challenges of prognostics and RUL prediction of machinery so as to steer the future development.

关键词： prognostics remaining useful life data-driven machine learning degradation modeling

prognostics for lithium-ion battery operating under different depth of discharge using hybrid method

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Prognostics for lithium-ion battery operating under differen...

Mortality prognostics during First 24 Hours Due to Cerebral Stroke among Adult Population of Rostov-on-Don

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作者： Yang Chang Huajing Fang Sai Li Yan Yan School of Automation Huazhong University of Science and Technology

Lithium-ion battery is a widely used power source in electronic system and its degradation has a very serious impact on the system performance and reliability. In this paper, a novel hybrid prognostics method is developed for the battery operating under different depth of discharge（DOD）. Unlike other prognostics schemes, in the proposed design, unscented Kalman filter（UKF） and a fitting method are utilized to generate a prognostics result and a series of raw error data, the error data is adopted by complete ensemble empirical mode decomposition（CEEMD） method and relevance vector machine（RVM） to correct the UKF-based prognostics result. Note that the previously reported prognostics schemes seldom consider the effect of DOD on battery degradation nor the prognostics error. Comparative experiments based on proposed scheme, UKF and RVM are conducted to verify the effectiveness and performance of the proposed hybrid method.

关键词： Lithium-ion battery prognostics Different depth of discharge Hybrid method

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Journal of Earth Science and Engineering 2012年第6期2卷 337-343页

作者： Vazgen Martirosyan Julia Krupskaya Department of Nervous Diseases and Neurosurgery Rostov State Medical University Rostov-on-Don 344000 Russia

In the present research, the model of mortality prognostics during the first 24 hours due to ACA （acute cerebrovascular accident） was developed. Eleven characteristics, developed by logistic regression method, were offered. The present model allows to predict the result ＂died/survived＂ for every adult patient with cerebral stroke, who was delivered to hospital to choose individual approach. And in such way, it raised the effectiveness of treatment and lowered the frequency of fatal case. External causes among solar, geomagnetic and meteorological were defined, which reflected the varied impact of environment and raised of fatal case probability during the first 24 hours.

关键词： Cerebral stroke logistic regression prognostics solar activity.

A method to perform prognostics in electro-hydraulic machines: the case of an independent metering controlled hydraulic crane

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International Journal of Hydromechatronics 2018年第2期1卷 2-2页

作者： Yuri Ghini Andrea Vacca Purdue Univ Maha Fluid Power Res Ctr W Lafayette IN 47907 USA

Remaining useful life (RUL) estimation is a topic that has gained more and more attention in the field of fluid power systems, thanks to the tremendous potentials shown for improving safety and reduced production losses. However, many challenges related to RUL estimation are not solved yet, mainly connected to the definition of the health status of each component. A prognostic method based on a data-driven methodology for hydraulic systems is here proposed to estimate the percentage of life already spent by the monitored components. The potentials of the methodology are shown considering the case of a truck-mounted hydraulic crane, for which a simulation model was available.

关键词： perform prognostics electro-hydraulic machines independent metering controlled hydraulic crane

Reliability estimation and remaining useful lifetime prediction for bearing based on proportional hazard model

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Journal of Central South University 2015年第12期22卷 4625-4633页

作者：王鹭张利王学芝 School of Information Liaoning University

As the central component of rotating machine,the performance reliability assessment and remaining useful lifetime prediction of bearing are of crucial importance in condition-based maintenance to reduce the maintenance cost and improve the reliability.A prognostic algorithm to assess the reliability and forecast the remaining useful lifetime(RUL) of bearings was proposed,consisting of three *** vibration and temperature signals of bearings in normal state were measured during the manufacturing process and the most useful time-dependent features of vibration signals were extracted based on correlation analysis(feature selection step).Time series analysis based on neural network,as an identification model,was used to predict the features of bearing vibration signals at any horizons(feature prediction step).Furthermore,according to the features,degradation factor was *** proportional hazard model was generated to estimate the survival function and forecast the RUL of the bearing(RUL prediction step).The positive results show that the plausibility and effectiveness of the proposed approach can facilitate bearing reliability estimation and RUL prediction.

关键词： prognostics reliability estimation remaining useful life proportional hazard model

A data-driven health indicator extraction method for aircraft air conditioning system health monitoring

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Chinese Journal of Aeronautics 2019年第2期32卷 409-416页

作者： Jianzhong SUN Chaoyi LI Cui LIU Ziwei GONG Ronghui WANG Department of Civil Aviation Nanjing University of Aeronautics and Astronautics Maintenance Engineering Department Xiamen Airlines

prognostics and Health Management(PHM) has become a very important tool in modern commercial aircraft. Considering limited built-in sensing devices on the legacy aircraft model,one of the challenges for airborne system health monitoring is to find an appropriate health indicator that is highly related to the actual degradation state of the system. This paper proposed a novel health indicator extraction method based on the available sensor parameters for the health monitoring of Air Conditioning System(ACS) of a legacy commercial aircraft model. Firstly, a specific Airplane Condition Monitoring System(ACMS) report for ACS health monitoring is defined. Then a non-parametric modeling technique is adopted to calculate the health indicator based on the raw ACMS report data. The proposed method is validated on a single-aisle commercial aircraft widely used for short and medium-haul routes, using more than 6000 ACMS reports collected from a fleet of aircraft during one year. The case study result shows that the proposed health indicator can effectively characterize the degradation state of the ACS, which can provide valuable information for proactive maintenance plan in advance.

关键词： Air conditioning system Aircraft health monitoring Airplane condition monitoring system Health indicator prognostics and health management