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.

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.

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.

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.

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.

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.

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.

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.

Prognostics and health management of alkaline water electrolyzer: Techno-economic analysis considering replacement moment

Recently,considerable attention has been paid to the installation of renewable energy capacity to mitigate global CO_(2) emissions.H_(2) produced using water electrolysis and renewable energy is regarded as a clean energy carrier,generating electricity without CO_(2) emissions,called‘Green H 2’.In this paper,a prognostics and health man-agement model for an alkaline water electrolyzer was proposed to predict the load voltage on the electrolyzer to obtain the state of health information.The prognostics and health management model was developed by training historical operating data via machine learning models,support vector machine and gaussian process regression,showing the root mean square error of 1.28×10^(−3) and 8.03×10^(−6).In addition,a techno-economic analysis was performed for a green H_(2) production system,composed of 1 MW of photovoltaic plant and 1 MW of alkaline water electrolyzer,to provide economic insights and feasibility of the system.A levelized cost of H_(2) of$6.89 kgH_(2)−1 was calculated and the potential to reach the levelized cost of H_(2) from steam methane reforming with carbon capture and storage was shown by considering the learning rate of the photovoltaic module and elec-trolyzer.Finally,the replacement of the alkaline water electrolyzer at around 10 years was preferred to increase the net present value from the green H_(2) production system when capital expenditure and replacement cost are low enough.

Remaining Useful Life Prediction of Rail Based on Improved Pulse Separable Convolution Enhanced Transformer Encoder

In order to prevent possible casualties and economic loss, it is critical to accurate prediction of the Remaining Useful Life (RUL) in rail prognostics health management. However, the traditional neural networks is difficult to capture the long-term dependency relationship of the time series in the modeling of the long time series of rail damage, due to the coupling relationship of multi-channel data from multiple sensors. Here, in this paper, a novel RUL prediction model with an enhanced pulse separable convolution is used to solve this issue. Firstly, a coding module based on the improved pulse separable convolutional network is established to effectively model the relationship between the data. To enhance the network, an alternate gradient back propagation method is implemented. And an efficient channel attention (ECA) mechanism is developed for better emphasizing the useful pulse characteristics. Secondly, an optimized Transformer encoder was designed to serve as the backbone of the model. It has the ability to efficiently understand relationship between the data itself and each other at each time step of long time series with a full life cycle. More importantly, the Transformer encoder is improved by integrating pulse maximum pooling to retain more pulse timing characteristics. Finally, based on the characteristics of the front layer, the final predicted RUL value was provided and served as the end-to-end solution. The empirical findings validate the efficacy of the suggested approach in forecasting the rail RUL, surpassing various existing data-driven prognostication techniques. Meanwhile, the proposed method also shows good generalization performance on PHM2012 bearing data set.

Use of machine learning models for the prognostication of liver transplantation: A systematic review

BACKGROUND Liver transplantation(LT)is a life-saving intervention for patients with end-stage liver disease.However,the equitable allocation of scarce donor organs remains a formidable challenge.Prognostic tools are pivotal in identifying the most suitable transplant candidates.Traditionally,scoring systems like the model for end-stage liver disease have been instrumental in this process.Nevertheless,the landscape of prognostication is undergoing a transformation with the integration of machine learning(ML)and artificial intelligence models.AIM To assess the utility of ML models in prognostication for LT,comparing their performance and reliability to established traditional scoring systems.METHODS Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines,we conducted a thorough and standardized literature search using the PubMed/MEDLINE database.Our search imposed no restrictions on publication year,age,or gender.Exclusion criteria encompassed non-English studies,review articles,case reports,conference papers,studies with missing data,or those exhibiting evident methodological flaws.RESULTS Our search yielded a total of 64 articles,with 23 meeting the inclusion criteria.Among the selected studies,60.8%originated from the United States and China combined.Only one pediatric study met the criteria.Notably,91%of the studies were published within the past five years.ML models consistently demonstrated satisfactory to excellent area under the receiver operating characteristic curve values(ranging from 0.6 to 1)across all studies,surpassing the performance of traditional scoring systems.Random forest exhibited superior predictive capabilities for 90-d mortality following LT,sepsis,and acute kidney injury(AKI).In contrast,gradient boosting excelled in predicting the risk of graft-versus-host disease,pneumonia,and AKI.CONCLUSION This study underscores the potential of ML models in guiding decisions related to allograft allocation and LT,marking a significant evolution in the field of prognostication.

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.

Predictive model using four ferroptosis-related genes accurately predicts gastric cancer prognosis

BACKGROUND Gastric cancer(GC)is a common malignancy of the digestive system.According to global 2018 cancer data,GC has the fifth-highest incidence and the thirdhighest fatality rate among malignant tumors.More than 60%of GC are linked to infection with Helicobacter pylori(H.pylori),a gram-negative,active,microaerophilic,and helical bacterium.This parasite induces GC by producing toxic factors,such as cytotoxin-related gene A,vacuolar cytotoxin A,and outer membrane proteins.Ferroptosis,or iron-dependent programmed cell death,has been linked to GC,although there has been little research on the link between H.pylori infection-related GC and ferroptosis.AIM To identify coregulated differentially expressed genes among ferroptosis-related genes(FRGs)in GC patients and develop a ferroptosis-related prognostic model with discrimination ability.METHODS Gene expression profiles of GC patients and those with H.pylori-associated GC were obtained from The Cancer Genome Atlas and Gene Expression Omnibus(GEO)databases.The FRGs were acquired from the FerrDb database.A ferroptosis-related gene prognostic index(FRGPI)was created using least absolute shrinkage and selection operator–Cox regression.The predictive ability of the FRGPI was validated in the GEO cohort.Finally,we verified the expression of the hub genes and the activity of the ferroptosis inducer FIN56 in GC cell lines and tissues.RESULTS Four hub genes were identified(NOX4,MTCH1,GABARAPL2,and SLC2A3)and shown to accurately predict GC and H.pylori-associated GC.The FRGPI based on the hub genes could independently predict GC patient survival;GC patients in the high-risk group had considerably worse overall survival than did those in the low-risk group.The FRGPI was a significant predictor of GC prognosis and was strongly correlated with disease progression.Moreover,the gene expression levels of common immune checkpoint proteins dramatically increased in the highrisk subgroup of the FRGPI cohort.The hub genes were also confirmed to be highly overexpressed in GC cell lines and tissues and were found to be primarily localized at the cell membrane.The ferroptosis inducer FIN56 inhibited GC cell proliferation in a dose-dependent manner.CONCLUSION In this study,we developed a predictive model based on four FRGs that can accurately predict the prognosis of GC patients and the efficacy of immunotherapy in this population.

Interaction between inflammatory bowel disease,physical activity,and myokines:Assessment of serum irisin levels

Inflammatory bowel disease(IBD),including Crohn’s disease and ulcerative colitis,showed a wide spectrum of intestinal and extra-intestinal manifestations,which rendered the patients physically inactive and impaired their quality of life.It has been found that physical activity is a non-pharmacological intervention that improves the quality of life for those patients.Irisin is one member of the myokines secreted by muscle contraction during exercise and could be used as an antiinflammatory biomarker in assessing the physical activity of IBD patients.In addition,experimental studies showed that exogenous irisin significantly decreased the inflammatory markers and the histological changes of the intestinal mucosa observed in experimental colitis.Furthermore,irisin produces changes in the diversity of the microbiota.Therefore,endogenous or exogenous irisin,via its anti-inflammatory effects,will improve the health of IBD patients and will limit the barriers to physical activity in patients with IBD.

Lipid metabolism-related long noncoding RNA RP11-817I4.1 promotes fatty acid synthesis and tumor progression in hepatocellular carcinoma

BACKGROUND Hepatocellular carcinoma(HCC)is one of the most common types of tumors.The influence of lipid metabolism disruption on the development of HCC has been demonstrated in published studies.AIM To establish an HCC prognostic model for lipid metabolism-related long non-coding RNAs(LMR-lncRNAs)and conduct in-depth research on the specific role of novel LMR-lncRNAs in HCC.METHODS Correlation and differential expression analyses of The Cancer Genome Atlas data were used to identify differentially expressed LMR-lncRNAs.Quantitative real-time polymerase chain reaction analysis was used to evaluate the expression of LMR-lncRNAs.Nile red staining was employed to observe intracellular lipid levels.The interaction between RP11-817I4.1,miR-3120-3p,and ATP citrate lyase(ACLY)was validated through the performance of dual-luciferase reporter gene and RIP assays.RESULTS Three LMR-lncRNAs(negative regulator of antiviral response,RNA transmembrane and coiled-coil domain family 1 antisense RNA 1,and RP11-817I4.1)were identified as predictive markers for HCC patients and were utilized in the construction of risk models.Additionally,proliferation,migration,and invasion were reduced by RP11-817I4.1 knockdown.An increase in lipid levels in HCC cells was significantly induced by RP11-817I4.1 through the miR-3120-3p/ACLY axis.CONCLUSION LMR-lncRNAs have the capacity to predict the clinical characteristics and prognoses of HCC patients,and the discovery of a novel LMR-lncRNAs,RP11-817I4.1,revealed its role in promoting lipid accumulation,thereby accelerating the onset and progression of HCC.

Clinical features and prognostic factors of duodenal neuroendocrine tumours:A comparative study of ampullary and nonampullary regions

BACKGROUND Duodenal neuroendocrine tumours(DNETs)are rare neoplasms.However,the incidence of DNETs has been increasing in recent years,especially as an incidental finding during endoscopic studies.Regrettably,there is no consensus regarding the ideal treatment of DNETs.Even there are few studies on the clinical features and survival analysis of DNETs.AIM To analyze the clinical characteristics and prognostic factors of patients with duodenal neuroendocrine tumours.METHODS The clinical data of DNETs diagnosed in the First Affiliated Hospital of Air Force Military Medical University from June 2011 to July 2022 were collected.Neuroen-docrine tumours located in the ampulla area of the duodenum were divided into the ampullary region group;neuroendocrine tumours in any part of the duo-denum outside the ampullary area were divided into the nonampullary region group.Using a retrospective study,the clinical characteristics of the two groups and risk factors affecting the survival of DNET patients were analysed.RESULTS Twenty-nine DNET patients were screened.The male to female ratio was 1:1.9,and females comprised the majority.The ampullary region group accounted for 24.1%(7/29),while the nonampullary region group accounted for 75.9%(22/29).When diagnosed,the clinical symptoms of the ampullary region group were mainly abdominal pain(85.7%),while those of the nonampullary region groups were mainly abdominal distension(59.1%).There were differences in the composition of staging of tumours between the two groups(Fisher's exact probability method,P=0.001),with nonampullary stage II tumours(68.2%)being the main stage(P<0.05).After the diagnosis of DNETs,the survival rate of the ampullary region group was 14.3%(1/7),which was lower than that of 72.7%(16/22)in the nonampullary region group(Fisher's exact probability method,P=0.011).The survival time of the ampullary region group was shorter than that of the nonampullary region group(P<0.000).The median survival time of the ampullary region group was 10.0 months and that of the nonampullary region group was 451.0 months.Multivariate analysis showed that tumours in the ampulla region and no surgical treatment after diagnosis were independent risk factors for the survival of DNET patients(HR=0.029,95%CI 0.004-0.199,P<0.000;HR=12.609,95%CI:2.889-55.037,P=0.001).Further analysis of nonampullary DNET patients showed that the survival time of patients with a tumour diameter<2 cm was longer than that of patients with a tumour diameter≥2 cm(t=7.243,P=0.048).As of follow-up,6 patients who died of nonampullary DNETs had a tumour diameter that was≥2 cm,and 3 patients in stage IV had liver metastasis.Patients with a tumour diameter<2 cm underwent surgical treatment,and all survived after surgery.CONCLUSION Surgical treatment is a protective factor for prolonging the survival of DNET patients.Compared to DNETs in the ampullary region,patients in the nonampullary region group had a longer survival period.The liver is the organ most susceptible to distant metastasis of nonampullary DNETs.

Four centrosome-related genes to predict the prognosis and drug sensitivity of patients with colon cancer

BACKGROUND As the primary microtubule organizing center in animal cells,centrosome abnormalities are involved in human colon cancer.AIM To explore the role of centrosome-related genes(CRGs)in colon cancer.METHODS CRGs were collected from public databases.Consensus clustering analysis was performed to separate the Cancer Genome Atlas cohort.Univariate Cox and least absolute shrinkage selection operator regression analyses were performed to identify candidate prognostic CRGs and construct a centrosome-related signature(CRS)to score colon cancer patients.A nomogram was developed to evaluate the CRS risk in colon cancer patients.An integrated bioinformatics analysis was conducted to explore the correlation between the CRS and tumor immune microenvironment and response to immunotherapy,chemotherapy,and targeted therapy.Single-cell transcriptome analysis was conducted to examine the immune cell landscape of core prognostic genes.RESULTS A total of 726 CRGs were collected from public databases.A CRS was constructed,which consisted of the following four genes:TSC1,AXIN2,COPS7A,and MTUS1.Colon cancer patients with a high-risk signature had poor survival.Patients with a high-risk signature exhibited decreased levels of plasma cells and activated memory CD4+T cells.Regarding treatment response,patients with a high-risk signature were resistant to immunotherapy,chemotherapy,and targeted therapy.COPS7A expression was relatively high in endothelial cells and fibroblasts.MTUS1 expression was high in endothelial cells,fibroblasts,and malignant cells.CONCLUSION We constructed a centrosome-related prognostic signature that can accurately predict the prognosis of colon cancer patients,contributing to the development of individualized treatment for colon cancer.