Remaining Useful Life Prediction Method for Multi-Component System Considering Maintenance:Subsea Christmas Tree System as A Case Study

Maintenance is an important technical measure to maintain and restore the performance status of equipment and ensure the safety of the production process in industrial production,and is an indispensable part of prediction and health management.However,most of the existing remaining useful life(RUL)prediction methods assume that there is no maintenance or only perfect maintenance during the whole life cycle;thus,the predicted RUL value of the system is obviously lower than its actual operating value.The complex environment of the system further increases the difficulty of maintenance,and its maintenance nodes and maintenance degree are limited by the construction period and working conditions,which increases the difficulty of RUL prediction.An RUL prediction method for a multi-omponent system based on the Wiener process considering maintenance is proposed.The performance degradation model of components is established by a dynamic Bayesian network as the initial model,which solves the uncertainty of insufficient data problems.Based on the experience of experts,the degree of degradation is divided according to Poisson process simulation random failure,and different maintenance strategies are used to estimate a variety of condition maintenance factors.An example of a subsea tree system is given to verify the effectiveness of the proposed method.

Accurate and efficient remaining useful life prediction of batteries enabled by physics-informed machine learning

The safe and reliable operation of lithium-ion batteries necessitates the accurate prediction of remaining useful life(RUL).However,this task is challenging due to the diverse ageing mechanisms,various operating conditions,and limited measured signals.Although data-driven methods are perceived as a promising solution,they ignore intrinsic battery physics,leading to compromised accuracy,low efficiency,and low interpretability.In response,this study integrates domain knowledge into deep learning to enhance the RUL prediction performance.We demonstrate accurate RUL prediction using only a single charging curve.First,a generalisable physics-based model is developed to extract ageing-correlated parameters that can describe and explain battery degradation from battery charging data.The parameters inform a deep neural network(DNN)to predict RUL with high accuracy and efficiency.The trained model is validated under 3 types of batteries working under 7 conditions,considering fully charged and partially charged cases.Using data from one cycle only,the proposed method achieves a root mean squared error(RMSE)of 11.42 cycles and a mean absolute relative error(MARE)of 3.19%on average,which are over45%and 44%lower compared to the two state-of-the-art data-driven methods,respectively.Besides its accuracy,the proposed method also outperforms existing methods in terms of efficiency,input burden,and robustness.The inherent relationship between the model parameters and the battery degradation mechanism is further revealed,substantiating the intrinsic superiority of the proposed method.

A Hybrid Approach for Predicting the Remaining Useful Life of Bearings Based on the RReliefF Algorithm and Extreme Learning Machine

Accurately predicting the remaining useful life(RUL)of bearings in mining rotating equipment is vital for mining enterprises.This research aims to distinguish the features associated with the RUL of bearings and propose a prediction model based on these selected features.This study proposes a hybrid predictive model to assess the RUL of rolling element bearings.The proposed model begins with the pre-processing of bearing vibration signals to reconstruct sixty time-domain features.The hybrid model selects relevant features from the sixty time-domain features of the vibration signal by adopting the RReliefF feature selection algorithm.Subsequently,the extreme learning machine(ELM)approach is applied to develop a predictive model of RUL based on the optimal features.The model is trained by optimizing its parameters via the grid search approach.The training datasets are adjusted to make them most suitable for the regression model using the cross-validation method.The proposed hybrid model is analyzed and validated using the vibration data taken from the public XJTU-SY rolling element-bearing database.The comparison is constructed with other traditional models.The experimental test results demonstrated that the proposed approach can predict the RUL of bearings with a reliable degree of accuracy.

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.

A Dear Price to Regret Over:On Mr.Stevens’Dignity and Professionalism in The Remains of the Day

The Remains of the Day is one of Kazuo Ishiguro’s renowned novels.The protagonist Stevens is an English butler whose life is deeply entwined with the concepts of dignity and honor.Set against the backdrop of post-World War II Britain and reflecting on the interwar period,the novel examines Stevens’devotion to his role and the traditional values of English aristocracy.The essay discusses how Stevens’identity is shaped by his unwavering commitment to professionalism and his admiration for British gentleman culture and highlights the identity crisis Stevens faces as he realizes the flawed nature of his employer and the outdated ideals he upheld.Through Stevens’journey of self-reassessment and eventual epiphany,the essay delves into the complexities of his internal struggle to reconstruct his identity,ultimately advocating for a more authentic understanding of dignity and honor.

The development of machine learning-based remaining useful life prediction for lithium-ion batteries

Lithium-ion batteries are the most widely used energy storage devices,for which the accurate prediction of the remaining useful life(RUL)is crucial to their reliable operation and accident prevention.This work thoroughly investigates the developmental trend of RUL prediction with machine learning(ML)algorithms based on the objective screening and statistics of related papers over the past decade to analyze the research core and find future improvement directions.The possibility of extending lithium-ion battery lifetime using RUL prediction results is also explored in this paper.The ten most used ML algorithms for RUL prediction are first identified in 380 relevant papers.Then the general flow of RUL prediction and an in-depth introduction to the four most used signal pre-processing techniques in RUL prediction are presented.The research core of common ML algorithms is given first time in a uniform format in chronological order.The algorithms are also compared from aspects of accuracy and characteristics comprehensively,and the novel and general improvement directions or opportunities including improvement in early prediction,local regeneration modeling,physical information fusion,generalized transfer learning,and hardware implementation are further outlooked.Finally,the methods of battery lifetime extension are summarized,and the feasibility of using RUL as an indicator for extending battery lifetime is outlooked.Battery lifetime can be extended by optimizing the charging profile serval times according to the accurate RUL prediction results online in the future.This paper aims to give inspiration to the future improvement of ML algorithms in battery RUL prediction and lifetime extension strategy.

Remaining Useful Life Prediction With Partial Sensor Malfunctions Using Deep Adversarial Networks

In recent years,intelligent data-driven prognostic methods have been successfully developed,and good machinery health assessment performance has been achieved through explorations of data from multiple sensors.However,existing datafusion prognostic approaches generally rely on the data availability of all sensors,and are vulnerable to potential sensor malfunctions,which are likely to occur in real industries especially for machines in harsh operating environments.In this paper,a deep learning-based remaining useful life(RUL)prediction method is proposed to address the sensor malfunction problem.A global feature extraction scheme is adopted to fully exploit information of different sensors.Adversarial learning is further introduced to extract generalized sensor-invariant features.Through explorations of both global and shared features,promising and robust RUL prediction performance can be achieved by the proposed method in the testing scenarios with sensor malfunctions.The experimental results suggest the proposed approach is well suited for real industrial applications.

A Hybrid Ensemble Deep Learning Approach for Early Prediction of Battery Remaining Useful Life

Accurate estimation of the remaining useful life(RUL)of lithium-ion batteries is critical for their large-scale deployment as energy storage devices in electric vehicles and stationary storage.A fundamental understanding of the factors affecting RUL is crucial for accelerating battery technology development.However,it is very challenging to predict RUL accurately because of complex degradation mechanisms occurring within the batteries,as well as dynamic operating conditions in practical applications.Moreover,due to insignificant capacity degradation in early stages,early prediction of battery life with early cycle data can be more difficult.In this paper,we propose a hybrid deep learning model for early prediction of battery RUL.The proposed method can effectively combine handcrafted features with domain knowledge and latent features learned by deep networks to boost the performance of RUL early prediction.We also design a non-linear correlation-based method to select effective domain knowledge-based features.Moreover,a novel snapshot ensemble learning strategy is proposed to further enhance model generalization ability without increasing any additional training cost.Our experimental results show that the proposed method not only outperforms other approaches in the primary test set having a similar distribution as the training set,but also generalizes well to the secondary test set having a clearly different distribution with the training set.The PyTorch implementation of our proposed approach is available at https://github.com/batteryrul/battery_rul_early_prediction.

End-cloud collaboration method enables accurate state of health and remaining useful life online estimation in lithium-ion batteries

Though the lithium-ion battery is universally applied,the reliability of lithium-ion batteries remains a challenge due to various physicochemical reactions,electrode material degradation,and even thermal runaway.Accurate estimation and prediction of battery health conditions are crucial for battery safety management.In this paper,an end-cloud collaboration method is proposed to approach the track of battery degradation process,integrating end-side empirical model with cloud-side data-driven model.Based on ensemble learning methods,the data-driven model is constructed by three base models to obtain cloud-side highly accurate results.The double exponential decay model is utilized as an empirical model to output highly real-time prediction results.With Kalman filter,the prediction results of end-side empirical model can be periodically updated by highly accurate results of cloud-side data-driven model to obtain highly accurate and real-time results.Subsequently,the whole framework can give an accurate prediction and tracking of battery degradation,with the mean absolute error maintained below 2%.And the execution time on the end side can reach 261μs.The proposed end-cloud collaboration method has the potential to approach highly accurate and highly real-time estimation for battery health conditions during battery full life cycle in architecture of cyber hierarchy and interactional network.

Remaining useful life prediction of aero-engines based on random-coefficient regression model considering random failure threshold

Remaining useful life(RUL)prediction is one of the most crucial components in prognostics and health management(PHM)of aero-engines.This paper proposes an RUL prediction method of aero-engines considering the randomness of failure threshold.Firstly,a random-coefficient regression(RCR)model is used to model the degradation process of aeroengines.Then,the RUL distribution based on fixed failure threshold is derived.The prior parameters of the degradation model are calculated by a two-step maximum likelihood estimation(MLE)method and the random coefficient is updated in real time under the Bayesian framework.The failure threshold in this paper is defined by the actual degradation process of aeroengines.After that,a expectation maximization(EM)algorithm is proposed to estimate the underlying failure threshold of aeroengines.In addition,the conditional probability is used to satisfy the limitation of failure threshold.Then,based on above results,an analytical expression of RUL distribution of aero-engines based on the RCR model considering random failure threshold(RFT)is derived in a closed-form.Finally,a case study of turbofan engine is used to demonstrate the effectiveness and superiority of the RUL prediction method and the parameters estimation method of failure threshold proposed.

Remaining useful life prediction based on nonlinear random coefficient regression model with fusing failure time data

Remaining useful life(RUL) prediction is one of the most crucial elements in prognostics and health management(PHM). Aiming at the imperfect prior information, this paper proposes an RUL prediction method based on a nonlinear random coefficient regression(RCR) model with fusing failure time data.Firstly, some interesting natures of parameters estimation based on the nonlinear RCR model are given. Based on these natures,the failure time data can be fused as the prior information reasonably. Specifically, the fixed parameters are calculated by the field degradation data of the evaluated equipment and the prior information of random coefficient is estimated with fusing the failure time data of congeneric equipment. Then, the prior information of the random coefficient is updated online under the Bayesian framework, the probability density function(PDF) of the RUL with considering the limitation of the failure threshold is performed. Finally, two case studies are used for experimental verification. Compared with the traditional Bayesian method, the proposed method can effectively reduce the influence of imperfect prior information and improve the accuracy of RUL prediction.

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.

Machine learning techniques for prediction of capacitance and remaining useful life of supercapacitors: A comprehensive review

Supercapacitors are appealing energy storage devices for their promising features like high power density,outstanding cycling stability,and a quick charge–discharge cycle.The exceptional life cycle and ultimate power capability of supercapacitors are needed in the transportation and renewable energy generation sectors.Hence,predicting the capacitance and lifecycle of supercapacitors is significant for selecting the suitable material and planning replacement intervals for supercapacitors.In addition,system failures can be better addressed by accurately forecasting the lifecycle of SCs.Recently,the use of machine learning for performance prediction of energy storage materials has drawn increasing attention from researchers globally because of its superiority in prediction accuracy,time efficiency,and costeffectiveness.This article presents a detailed review of the progress and advancement of ML techniques for the prediction of capacitance and remaining useful life(RUL)of supercapacitors.The review starts with an introduction to supercapacitor materials and ML applications in energy storage devices,followed by workflow for ML model building for supercapacitor materials.Then,the summary of machine learning applications for the prediction of capacitance and RUL of different supercapacitor materials including EDLCs(carbon based materials),pesudocapacitive(oxides and composites)and hybrid materials is presented.Finally,the general perspective for future directions is also presented.

Study on Remaining Oil at High Water Cut Stage of the Offshore Strong Bottom Water Reservoir

C oilfield is a heavy oil field developed by horizontal wells and single sand body in Bohai oilfield. The edge and bottom water of the reservoir is active and the natural energy development mode is adopted. The comprehensive water cut of the oilfield was 95.3%, which had entered the stage of high water cut oil production. Some reservoirs were limited by crude oil viscosity and oil column height. Under the condition of existing development well pattern, some reserves were not produced or the degree of production was low, and the degree of well control was not high, so there is room for tapping the potential of remaining oil. This paper studied the rising law of water ridge of horizontal wells in bottom water reservoir by reservoir engineering method, and guided the infilling limit of horizontal wells in bottom water reservoir. At the same time, combined with the research results of fine reservoir description, the geological model was established, the numerical simulation was carried out, and the distribution law of remaining oil was analyzed. Through this study, we could understand the law of water flooding and remaining oil in the high water cut period of bottom water heavy oil reservoir, so as to provide guidance for the development strategy of this type of reservoir in the high water cut period.

Stevens’s Ethical Identity Dilemma in The Remains of the Day

Based on ethical literary criticism,this paper aims to analyze the factors contributing to Stevens’loss of ethical identity.It contends that distorted ethical relationships are the primary driver behind Stevens’s ethical identity dilemma,which includes the abnormal father-son relationship,Stevens’s blind admiration for his master,and his avoidance of Miss Kenton’s feelings.

NiCo_(2)O_(4)//GO非对称超级电容器电动汽车动力系统应用仿真

将制备的NiCo_(2)O_(4)电极进行电化学性能测试,结果表明,NiCo_(2)O_(4)电极活性材料通过准可逆的氧化还原反应进行储能,表现出良好的电化学性能.进而使用Simulink仿真模拟了非对称超级电容器单体的放电过程,并使用AVL CRUISE仿真计算了超级电容器-锂离子电池复合储能系统的电动汽车行驶过程.结果表明,汽车在WLTC循环工况下的最大电池功率为38.0 kW,行驶时间为4.9 h,续航里程为224.4 km,相比单独的锂离子电池储能系统最大电池功率减小了31.3%,续航里程提高了11.4%.超级电容器对复合储能系统的电动汽车起到了平衡电池功率和提高续航里程的作用.

近废弃油藏延长生命周期开发调整技术

针对近废弃油藏特高含水、优势通道发育、剩余油高度分散、非均质性强等主要矛盾,以双河油田北块Ⅱ(2油组)4—5层系为例,采用油藏精细地质建模、数值模拟方法和微观驱替实验方法,表征了聚合物驱后油藏剩余油分布特征。聚合物驱后宏观剩余油平面上注采非主流线、主流线弱势区及注采井距较大的边部区域剩余油饱和度较高,纵向上正韵律顶部剩余油富集;微观剩余油以半束缚态为主,依据剩余油分布特征提出了非均相复合驱变流线井网加密调整技术思路。通过井网变流线加密调整,形成交错式行列井网模式,流线方向转变30°以上,流线转向率达80%,促使剩余油有效动用。数值模拟预测该技术可提高采收率10.96%,新增可采储量70.61×10^(4) t,延长生命周期15 a,为聚合物驱后油藏大幅度提高采收率提供新的技术方法。

基于投影寻踪模型的特高含水油藏剩余油可采潜力评价方法

影响特高含水油藏剩余油可采潜力的因素极其复杂,且各因素的影响程度差异明显,常规方法多以剩余油饱和度或剩余油储量丰度等单一指标评价剩余油潜力,难以有效指导特高含水油藏剩余油挖潜。在充分考虑特高含水油藏剩余油可采潜力影响因素的基础上,综合表征储层非均质性、剩余油可采储量规模、水淹状况以及油水分流能力的差异,构建了特高含水油藏剩余油可采潜力量化评价指标体系,并考虑不同指标对剩余油可采潜力控制程度的差异,将加速遗传算法与投影寻踪模型相结合来确定各评价指标的客观权重,从而构建了剩余油可采潜力指数,形成特高含水油藏剩余油可采潜力量化评价新方法。以渤海Q油田南区主力产层NmIL砂体为例,开展特高含水油藏剩余油可采潜力量化评价,结果表明,新方法可综合表征不同区域位置的储层物性、可采储量丰度和油水分流能力对剩余油可采潜力的影响,实现了主力产层NmIL砂体剩余油可采潜力分布的差异化定量评价,优势可采潜力区域刻画明显,将其作为NmIL砂体下一步井网加密调整潜力区域,以精准指导加密水平井的部署,为特高含水油藏剩余油挖潜提供了一种全新的分析方法与思路。

考古遗存中难产死亡推判、成因及案例研究

难产是导致古代孕产妇和胎儿死亡的重要原因,但是相关案例的具体体质形态在文献中却鲜有记载,考古学证据也不多见。本文试图通过梳理考古遗存中发现的难产死亡案例,对难产死亡的考古学推判标准和难产成因作简要综述,同时对郑州洄沟等遗址的典型难产案例进行分析。难产研究的梳理,对相关的墓葬发掘、人骨收集、资料整理等都具有重要的启示意义。

大庆油田裸眼井测井技术进展与展望

为了提高大庆油田裸眼井测井技术支撑能力和研究成果领先水平,全面回顾了大庆油田测井采集系列及解释技术的创新发展历程,系统总结了特高含水期剩余油解释、火山岩等复杂岩性测井评价、碎屑岩储量参数解释、非常规油气“甜点”分类、缝洞型碳酸盐岩储层测井评价等油田勘探开发测井评价技术。在客观分析大庆油田勘探开发测井解释评价需求和面临瓶颈问题的基础上,结合当前油田测井评价对象规模小、物性差、埋藏深、地层结构复杂、非均质性强的特点。指明了测井解释评价核心技术主攻方向。围绕新阶段测井采集及解释评价技术体系完善与建立,对高分辨率和成像测井采集、后油藏时期和非常规测井解释评价、新一代智能解释技术体系等未来发展进行了展望。