Health management based on fusion prognostics for avionics systems

Health management permits the reliability of a system and plays a increasingly important role for achieving efficient system-level maintenance.It has been used for remaining useful life（RUL） prognostics of electronics-rich system including avionics.Prognostics and health management（PHM） have become highly desirable to provide avionics with system level health management.This paper presents a health management and fusion prognostic model for avionics system,combining three baseline prognostic approaches that are model-based,data-driven and knowledge-based approaches,and integrates merits as well as eliminates some limitations of each single approach to achieve fusion prognostics and improved prognostic performance of RUL estimation.A fusion model built upon an optimal linear combination forecast model is then utilized to fuse single prognostic algorithm representing the three baseline approaches correspondingly,and the presented case study shows that the fusion prognostics can provide RUL estimation more accurate and more robust than either algorithm alone.

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

In this paper,we propose an approach for diagnostics and prognostics of damaged aircraft structures,by combing high-performance fatigue mechanics with filtering theories.Fast&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 rates.Such 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 theories.Both 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 time.For the specific problem,a simple modification to the particle filter,which can drastically reduce the computational burden,is also proposed.Based on the results of such diagnostic analyses,the prognostics of aerospace structures are thereafter achieved,to estimate the probabilistic distribution of the remaining useful life.By using a simple example of a single-crack near a fastener hole,we demonstrate the concept and effectiveness of the proposed framework.This 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.

Special Issue on Machine Fault Diagnostics and Prognostics

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.

An Edge-Fog-Cloud Computing-Based Digital Twin Model for Prognostics Health Management of Process Manufacturing Systems

The prognostics health management(PHM)fromthe systematic viewis critical to the healthy continuous operation of processmanufacturing systems(PMS),with different kinds of dynamic interference events.This paper proposes a three leveled digital twinmodel for the systematic PHMof PMSs.The unit-leveled digital twinmodel of each basic device unit of PMSs is constructed based on edge computing,which can provide real-time monitoring and analysis of the device status.The station-leveled digital twin models in the PMSs are designed to optimize and control the process parameters,which are deployed for the manufacturing execution on the fog server.The shop-leveled digital twin maintenancemodel is designed for production planning,which gives production instructions fromthe private industrial cloud server.To cope with the dynamic disturbances of a PMS,a big data-driven framework is proposed to control the three-level digital twin models,which contains indicator prediction,influence evaluation,and decisionmaking.Finally,a case study with a real chemical fiber system is introduced to illustrate the effectiveness of the digital twin model with edge-fog-cloud computing for the systematic PHM of PMSs.The result demonstrates that the three-leveled digital twin model for the systematic PHM in PMSs works well in the system’s respects.

Data-driven prognostics and remaining useful life estimation for lithium-ion battery: A Review

As an important and necessary part in the intelligent battery management systems(BMS),the prognostics and remaining useful life(RUL)estimation for lithium-ion batteries attach more and more attractions.Especially,the data-driven approaches use only the monitoring data and historical data to model the performance degradation and assess the health status,that makes these methods flexible and applicable in actual lithium-ion battery applications.At first,the related concepts and definitions are introduced.And the degradation parameters identification and extraction is presented,as the health indicator and the foundation of RUL prediction for the lithium-ion batteries.Then,data-driven methods used for lithium-ion battery RUL estimation are summarized,in which several statistical and machine learning algorithms are involved.Finally,the future trend for battery prognostics and RUL estimation are forecasted.

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

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 decades.In 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,etc.Based 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.

Battery prognostics and health management for electric vehicles under industry 4.0

Transportation electrification is essential for decarbonizing transport. Currently, lithium-ion batteries are the primary power source for electric vehicles (EVs). However, there is still a significant journey ahead before EVs can establish themselves as the dominant force in the global automotive market. Concerns such as range anxiety, battery aging, and safety issues remain significant challenges.

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

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.

Virtual sample generation for model-based prognostics and health management of on-board high-speed train control system

In view of class imbalance in data-driven modeling for Prognostics and Health Management(PHM),existing classification methods may fail in generating effective fault prediction models for the on-board high-speed train control equipment.A virtual sample generation solution based on Generative Adversarial Network(GAN)is proposed to overcome this shortcoming.Aiming at augmenting the sample classes with the imbalanced data problem,the GAN-based virtual sample generation strategy is embedded into the establishment of fault prediction models.Under the PHM framework of the on-board train control system,the virtual sample generation principle and the detailed procedures are presented.With the enhanced class-balancing mechanism and the designed sample augmentation logic,the PHM scheme of the on-board train control equipment has powerful data condition adaptability and can effectively predict the fault probability and life cycle status.Practical data from a specific type of on-board train control system is employed for the validation of the presented solution.The comparative results indicate that GAN-based sample augmentation is capable of achieving a desirable sample balancing level and enhancing the performance of correspondingly derived fault prediction models for the Condition-based Maintenance(CBM)operations.

Computational Reproducibility Within Prognostics and Health Management

Scientific research frequently involves the use of computational tools and methods.Providing thorough documentation,open-source code,and data–the creation of reproducible computational research(RCR)–helps others understand a researcher’s work.In this study,we investigate the state of reproducible computational research,broadly,and from within the field of prognostics and health management(PHM).In a text mining survey of more than 300 articles,we show that fewer than 1%of PHM researchers make their code and data available to others.To promote the RCR further,our work also highlights several personal benefits for those engaged in the practice.Finally,we introduce an open-source software tool,called PyPHM,to assist PHM researchers in accessing and preprocessing common industrial datasets.

Lifetime and Aging Degradation Prognostics for Lithium-ion Battery Packs Based on a Cell to Pack Method

Aging diagnosis of batteries is essential to ensure that the energy storage systems operate within a safe region.This paper proposes a novel cell to pack health and lifetime prognostics method based on the combination of transferred deep learning and Gaussian process regression.General health indicators are extracted from the partial discharge process.The sequential degradation model of the health indicator is developed based on a deep learning framework and is migrated for the battery pack degradation prediction.The future degraded capacities of both battery pack and each battery cell are probabilistically predicted to provide a comprehensive lifetime prognostic.Besides,only a few separate battery cells in the source domain and early data of battery packs in the target domain are needed for model construction.Experimental results show that the lifetime prediction errors are less than 25 cycles for the battery pack,even with only 50 cycles for model fine-tuning,which can save about 90%time for the aging experiment.Thus,it largely reduces the time and labor for battery pack investigation.The predicted capacity trends of the battery cells connected in the battery pack accurately reflect the actual degradation of each battery cell,which can reveal the weakest cell for maintenance in advance.

Machine learning based online fault prognostics for nonstationary industrial process via degradation feature extraction and temporal smoothness analysis

Fault degradation prognostic, which estimates the time before a failure occurs and process breakdowns, has been recognized as a key component in maintenance strategies nowadays. Fault degradation processes are, in general,slowly varying and can be modeled by autoregressive models. However, industrial processes always show typical nonstationary nature, which may bring two challenges: how to capture fault degradation information and how to model nonstationary processes. To address the critical issues, a novel fault degradation modeling and online fault prognostic strategy is developed in this paper. First, a fault degradation-oriented slow feature analysis(FDSFA) algorithm is proposed to extract fault degradation directions along which candidate fault degradation features are extracted. The trend ability assessment is then applied to select major fault degradation features. Second, a key fault degradation factor(KFDF) is calculated to characterize the fault degradation tendency by combining major fault degradation features and their stability weighting factors. After that, a time-varying regression model with temporal smoothness regularization is established considering nonstationary characteristics. On the basis of updating strategy, an online fault prognostic model is further developed by analyzing and modeling the prediction errors. The performance of the proposed method is illustrated with a real industrial process.

Dynamically updated digital twin for prognostics and health management:Application in permanent magnet synchronous motor

Current research on Digital Twin(DT)based Prognostics and Health Management(PHM)focuses on establishment of DT through integration of real-time data from various sources to facilitate comprehensive product monitoring and health management.However,there still exist gaps in the seamless integration of DT and PHM,as well as in the development of DT multi-field coupling modeling and its dynamic update mechanism.When the product experiences long-period degradation under load spectrum,it is challenging to describe the dynamic evolution of the health status and degradation progression accurately.In addition,DT update algorithms are difficult to be integrated simultaneously by current methods.This paper proposes an innovative dual loop DT based PHM framework,in which the first loop establishes the basic dynamic DT with multi-filed coupling,and the second loop implements the PHM and the abnormal detection to provide the interaction between the dual loops through updating mechanism.The proposed method pays attention to the internal state changes with degradation and interactive mapping with dynamic parameter updating.Furthermore,the Independence Principle for the abnormal detection is proposed to refine the theory of DT.Events at the first loop focus on accurate modeling of multi-field coupling,while the events at the second loop focus on real-time occurrence of anomalies and the product degradation trend.The interaction and collaboration between different loop models are also discussed.Finally,the Permanent Magnet Synchronous Motor(PMSM)is used to verify the proposed method.The results show that the modeling method proposed can accurately track the lifecycle performance changes of the entity and carry out remaining life prediction and health management effectively.

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

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.

Remaining useful life prognostics for aeroengine based on superstatistics and information fusion

Remaining useful life（RUL） prognostics is a fundamental premise to perform conditionbased maintenance（CBM） for a system subject to performance degradation. Over the past decades,research has been conducted in RUL prognostics for aeroengine. However, most of the prognostics technologies and methods simply base on single parameter, making it hard to demonstrate the specific characteristics of its degradation. To solve such problems, this paper proposes a novel approach to predict RUL by means of superstatistics and information fusion. The performance degradation evolution of the engine is modeled by fusing multiple monitoring parameters, which manifest non-stationary characteristics while degrading. With the obtained degradation curve,prognostics model can be established by state-space method, and then RUL can be estimated when the time-varying parameters of the model are predicted and updated through Kalman filtering algorithm. By this method, the non-stationary degradation of each parameter is represented, and multiple monitoring parameters are incorporated, both contributing to the final prognostics. A case study shows that this approach enables satisfactory prediction evolution and achieves a markedly better prognosis of RUL.

Temporal dynamics of neonatal hypoxic-ischemic encephalopathy injuries on magnetic resonance imaging

Moderate to severe perinatal hypoxic-ischemic encephalopathy occurs in~1 to 3/1000 live births in high-income countries and is associated with a significant risk of death or neurodevelopmental disability.Detailed assessment is important to help identify highrisk infants,to help families,and to support appropriate interventions.A wide range of monitoring tools is available to assess changes over time,including urine and blood biomarkers,neurological examination,and electroencephalography.At present,magnetic resonance imaging is unique as although it is expensive and not suited to monitoring the early evolution of hypoxic-ischemic encephalopathy by a week of life it can provide direct insight into the anatomical changes in the brain after hypoxic-ischemic encephalopathy and so offers strong prognostic information on the long-term outcome after hypoxic-ischemic encephalopathy.This review investigated the temporal dynamics of neonatal hypoxic-ischemic encephalopathy injuries,with a particular emphasis on exploring the correlation between the prognostic implications of magnetic resonance imaging scans in the first week of life and their relationship to long-term outcome prediction,particularly for infants treated with therapeutic hypothermia.A comprehensive literature search,from 2016 to 2024,identified 20 pertinent articles.This review highlights that while the optimal timing of magnetic resonance imaging scans is not clear,overall,it suggests that magnetic resonance imaging within the first week of life provides strong prognostic accuracy.Many challenges limit the timing consistency,particularly the need for intensive care and clinical monitoring.Conversely,although most reports examined the prognostic value of scans taken between 4 and 10 days after birth,there is evidence from small numbers of cases that,at times,brain injury may continue to evolve for weeks after birth.This suggests that in the future it will be important to explore a wider range of times after hypoxic-ischemic encephalopathy to fully understand the optimal timing for predicting long-term outcomes.

Carcinosarcoma of the breast:Facing the challenge of a rare nosologic entity

Carcinosarcoma(CS),also known as metaplastic breast carcinoma with mesenchymal differentiation,is one of the five distinct subtypes of metaplastic breast cancer.It is considered as a mixed,biphasic neoplasm consisting of a carcinomatous component combined with a malignant nonepithelial element of mesenchymal origin without an intermediate transition zone.Although cellular origin of this neoplasm remains controversial,most researchers declare that neoplastic cells derive from a cellular structure with potential biphasic differentiation.Despite recent research on the therapeutic strategies against CS neoplastic disorders,surgical resection appears the only potentially curative approach.Since CS metastasize by the lymphatic route,axillary assessment with sentinel lymph node biopsy and/or axillary lymph node dissection is always implemented.Nevertheless,the tumor also presents a hematogenous metastatic pattern including pleural,pulmonary,liver,brain and less commonly bone metastases.Thus,surgical removal of breast CS does not necessarily ensure patient’s long-term recovery.Moreover,alternative therapies,such as radio-and chemotherapy proved insufficient and 5-year survival rate is limited.Nevertheless,there is evidence that following surgery,the combination of radio and chemotherapy is associated with a better prognosis than either treatment alone.The aim of this review is to evaluate the results of surgical treatment for breast CS with special reference to the extent of its histological spread.Clinical features,histogenesis,morphological and immunochemical findings are discussed,while the role of current diagnostic and therapeutic management of this aggressive neoplasm is emphasized.

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

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.

Improving postoperative outcomes in patients with pancreatic cancer:Inflammatory and nutritional biomarkers

This editorial assesses the prognostic value of preoperative inflammatory and nutritional biomarkers in patients undergoing surgical resection for pancreatic cancer.Lu et al evaluated the ability of seven biomarkers to predict postoperative recovery and long-term outcomes.These biomarkers were albumin-to-globulin ratio,prognostic nutritional index(PNI),systemic immune-inflammation index,neutrophil-to-lymphocyte ratio,platelet-to-lymphocyte ratio,nutritional risk index,and geriatric nutritional risk index.The PNI was found to be a strong predictor of both overall and recurrence-free survival,underscoring its clinical relevance in managing patients with pancreatic cancer.

An ontological modelling of multi-attribute criticality analysis to guide Prognostics and Health Management program development

Digital technologies are becoming more pervasive and industrial companies are exploiting them to enhance the potentialities related to Prognostics and Health Management(PHM).Indeed,PHM allows to evaluate the health state of the physical assets as well as to predict their future behaviour.To be effective in developing PHM programs,the most critical assets should be identified so to direct modelling efforts.Several techniques could be adopted to evaluate asset criticality;in industrial practice,criticality analysis is amongst the most utilised.Despite the advancement of artificial intelligence for data analysis and predictions,the criticality analysis,which is built upon both quantitative and qualitative data,has not been improved accordingly.It is the goal of this work to propose an ontological formalisation of a multi-attribute criticality analysis in order to i)fix the semantics behind the terms involved in the analysis,ii)standardize and uniform the way criticality analysis is performed,and iii)take advantage of the reasoning capabilities to automatically evaluate asset criticality and associate a suitable maintenance strategy.The developed ontology,called MOCA,is tested in a food company featuring a global footprint.The application shows that MOCA can accomplish the prefixed goals;specifically,high priority assets towards which direct PHM programs are identified.In the long run,ontologies could serve as a unique knowledge base that integrate multiple data and information across facilities in a consistent way.As such,they will enable advanced analytics to take place,allowing to move towards cognitive Cyber Physical Systems that enhance business performance for companies spread worldwide.