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.

Electrothermal Model Based Remaining Charging Time Prediction of Lithium-Ion Batteries against Wide Temperature Range

Battery remaining charging time(RCT)prediction can facilitate charging management and alleviate mileage anxiety for electric vehicles(EVs).Also,it is of great significance to improve EV users’experience.However,the RCT for a lithiumion battery pack in EVs changes with temperature and other battery parameters.This study proposes an electrothermal model-based method to accurately predict battery RCT.Firstly,a characteristic battery cell is adopted to represent the battery pack,thus an equivalent circuit model(ECM)of the characteristic battery cell is established to describe the electrical behaviors of a battery pack.Secondly,an equivalent thermal model(ETM)of the battery pack is developed by considering the influence of ambient temperature,thermal management,and battery connectors in the battery pack to calculate the temperature which is then fed back to the ECM to realize electrothermal coupling.Finally,the RCT prediction method is proposed based on the electrothermal model and validated in the wide temperature range from-20℃to 45℃.The experimental results show that the prediction error of the RCT in the whole temperature range is less than 1.5%.

Remaining Useful Life for Heavy-Duty Railway Cast Steel Knuckles Based on Crack Growth Behavior with Hypothetical Distributions

The current research on the integrity of critical structures of rail vehicles mainly focuses on the design stage,which needs an effective method for assessing the service state.This paper proposes a framework for predicting the remaining useful life(RUL)of in-service structures with and without visible cracks.The hypothetical distribution and delay time models were used to apply the equivalent crack growth life data of heavy-duty railway cast steel knuckles,which revealed the evolution characteristics of the crack length and life scores of the knuckle under different fracture failure modes.The results indicate that the method effectively predicts the RUL of service knuckles in different failure modes based on the cumulative failure probability curves for different locations and surface crack lengths.This study proposes an RUL prediction framework that supports the dynamic overhaul and state maintenance of knuckle fatigue cracks.

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 oil distribution characteristics in an oil reservoir with ultra-high water-cut

An accurate mapping and understanding of remaining oil distribution is very important for water control and stabilize oil production of mature oilfields in ultra-high water-cut stage.Currently,the Tuo-21 Fault Block of the Shengtuo Oilfield has entered the stage of ultra-high water cut(97.2%).Poor adaptability of the well pattern,ineffective water injection cycle and low efficiency of engineering measures(such as workover,re-perforation and utilization of high-capacity pumps)are the significant problems in the ultra-high water-cut reservoir.In order to accurately describe the oil and water flow characteristics,relative permeability curves at high water injection multiple(injected pore volume)and a semiquantitative method is applied to perform fine reservoir simulation of the Sand group 3e7 in the Block.An accurate reservoir model is built and history matching is performed.The distribution characteristics of remaining oil in lateral and vertical directions are quantitatively simulated and analyzed.The results show that the numerical simulation considering relative permeability at high injection multiple can reflect truly the remaining oil distribution characteristics after water flooding in an ultrahigh water-cut stage.The distribution of remaining oil saturation can be mapped more accurately and quantitatively by using the‘four-points and five-types’classification method,providing a basis for potential tapping of various remaining oil types of oil reservoirs in late-stage of development with high water-cut.

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.

基于语料库的动词remain语义韵对比研究

采取基于语料库的方法,对比研究中国学习者和英语本族语者使用高频动词remain时在类联接、搭配词和语义韵等方面的差异。在建立类联接的基础上,逐一对中美语料库中remain一词的语义韵进行比较研究。研究结果表明,中国学习者与本族语者使用remain时,类联接种类基本一致,但是分布差异明显;不同类联接中,remain的语义选择趋向和语义韵有差异,即本族语者仅使用REMAIN+ADJ型式时,表现出积极语义趋向,REMAIN+N和REMAIN+PREP则是中性的语义趋向,其他语法型式主要为消极语义趋向,而中国学习者未能准确把握remain的语义韵,每种类联接都出现语义韵违和现象。

Prediction Method Study on the Remaining Useful Life of Plant New Varieties Rights Based on Weibull Survival Function and Gaussian Model——Taking Hybrid Rice Variety for Example

In view of the difficulty in determining remaining useful life of plant new variety right in economic analysis, Weibull Survival Analysis Method and Gaussian Model to were used to study how to accurately estimate the remaining useful life of plant new variety right. The results showed that the average life of the granted rice varieties was 10.013 years. With the increase of the age of plant variety rights, the probability of the same residual life Ttreaching x was smaller and smaller, the reliability lower and lower, while the probability of the variety rights becoming invalid became greater. The remaining useful life of a specific granted rice variety was closely related to the demonstration promotion age when the granted rice variety reached its maximum area and the appearance of alternative varieties, and when the demonstration promotion age of the granted rice variety reaching the one with the maximum area, the promotion area would be decreased once a new alternative variety appeared, correspondingly with the shortening of the remaining useful life of the variety. Therefore, Weibull Survival Analysis Method and Gaussian Model could describe the remaining useful life＇s time trend, as well as determine the remaining useful life of a concrete plant variety right, clarify the entire life time of varieties rights, and make the economic analysis of plant new varieties rights more accurate and reasonable.

Characteristics of Geological Remains Resources in Bailixia Provincial Geopark in Dazhou City of Sichuan Province

Through the analysis of geological background and geologic structural characteristics in Bailixia Provincial Geopark,the reason why all kinds of characteristic landscapes formed in the geopark was obtained;resources feature evaluation had been conducted on geological remains from the perspective of nature and humanity;geologic remains resources feature of the geopark was illustrated in a systematic way.The paper concluded the features of geologic remains resources in Bailixia Provincial Geopark which included high aesthetic value,rich scientific research value,overall development,and high-grade eco-tourism environment.

The Realization of Stevens’ Autonomy in The Remains of the Day

The Remains of the Day is a masterpiece of Ishiguro Kazuo,the winner of the 2017 Nobel Prize in Literature.Based on a six-day journey,this novel intertwined Stevens’recollections and thoughts,revealing a traditional British butler’s self-deception and self-suppression while confronting with the dilemma of professionalism and personal emotions.This article intends to analyze Stevens’realization of autonomy through his unutterable love toward Miss Kenton in three stages:separateness,competence and emotional autonomy.

Remaining Useful Life Model and Assessment of Mechanical Products: A Brief Review and a Note on the State Space Model Method

The remaining useful life(RUL) prediction of mechanical products has been widely studied for online system performance reliability, device remanufacturing, and product safety(safety awareness and safety improvement). These studies incorporated many di erent models, algorithms, and techniques for modeling and assessment. In this paper, methods of RUL assessment are summarized and expounded upon using two major methods: physics model based and data driven based methods. The advantages and disadvantages of each of these methods are deliberated and compared as well. Due to the intricacy of failure mechanism in system, and di culty in physics degradation observation, RUL assessment based on observations of performance variables turns into a science in evaluating the degradation. A modeling method from control systems, the state space model(SSM), as a first order hidden Markov, is presented. In the context of non-linear and non-Gaussian systems, the SSM methodology is capable of performing remaining life assessment by using Bayesian estimation(sequential Monte Carlo). Being e ective for non-linear and non-Gaussian dynamics, the methodology can perform the assessment recursively online for applications in CBM(condition based maintenance), PHM(prognostics and health management), remanufacturing, and system performance reliability. Finally, the discussion raises concerns regarding online sensing data for SSM modeling and assessment of RUL.

Remaining useful life estimation based on Wiener degradation processes with random failure threshold

Remaining useful life(RUL) estimation based on condition monitoring data is central to condition based maintenance(CBM). In the current methods about the Wiener process based RUL estimation, the randomness of the failure threshold has not been studied thoroughly. In this work, by using the truncated normal distribution to model random failure threshold(RFT), an analytical and closed-form RUL distribution based on the current observed data was derived considering the posterior distribution of the drift parameter. Then, the Bayesian method was used to update the prior estimation of failure threshold. To solve the uncertainty of the censored in situ data of failure threshold, the expectation maximization(EM) algorithm is used to calculate the posteriori estimation of failure threshold. Numerical examples show that considering the randomness of the failure threshold and updating the prior information of RFT could improve the accuracy of real time RUL estimation.

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

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 phases.Online 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 defined.The 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.

RELATIONSHIP BETWEEN INTERNAL ANAL SPHINCTER FUNCTION AND LENGTH OF REMAINING RECTUM AFTER RESECTING RECTAL CARCINOMA

Objective: To study the relationship between internal anal sphincter function and length of remaining rectum after resecting rectal carcinoma. Methods: Preoperatively, 21 patients were evaluated via patients' clinical date, including anal resting pressure (resting pressure) assay. Six months postoperatively, repeated manometric studies and clinical evaluations were performed to assess the level of continence . The formula use for calculating post operative resting pressure is as follows: postoperative resting pressure=0.42×preoperative resting pressure+1.56×length of remaining recturm+12.37(R 2=0.58; P <0.01).Degree of continence was graded based on severity of the dysfunction and grade of the continence score. Results: It was demonstrated the patients with low postoperative resting pressures (<4.0 Kpa) had incontinence, and those with high postoperative resting pressures (>4.7 Kpa) were continent. There were significant correlations between length of the remaining rectum and ratio of the decrease in maximum resting pressure (postoperative/preoperative maximum resting pressure;r=0.62; P <0.01). Conclusion: Continence of rectum is influenced by maximum resting pressure of function of the internal anal sphincter, length of remaining rectum is shorter, the more damage to the internal anal sphincter. It is able to foretell stool incontinence by using the postoperative resting pressure formula, and to determine the length of the remaining rectum.

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 for a nonlinear multi-degradation system with public noise

To predict the remaining useful life(RUL) for a class of nonlinear multi-degradation systems, a method is presented. In the real industrial processes, systems are usually composed by several parts or components, and these parts or components are working in the same environment, thus the degradations of these parts or components will be influenced by common factors. To describe such a phenomenon in degradations, a multi-degradation model with public noise is proposed. To identify the degradation states and the unknown parameters, an iterative estimation method is proposed by using the Kalman filter and the expectation maximization(EM) algorithm. Next, with known thresholds,the RUL of each degradation can be predicted by using the first hitting time(FHT). In addition, the RUL of the whole system can be obtained by a Copula function. Finally, a practical case is used to demonstrate the method proposed.

Remaining useful lifetime prediction for equipment based on nonlinear implicit degradation modeling

Nonlinearity and implicitness are common degradation features of the stochastic degradation equipment for prognostics.These features have an uncertain effect on the remaining useful life(RUL)prediction of the equipment.The current data-driven RUL prediction method has not systematically studied the nonlinear hidden degradation modeling and the RUL distribution function.This paper uses the nonlinear Wiener process to build a dual nonlinear implicit degradation model.Based on the historical measured data of similar equipment,the maximum likelihood estimation algorithm is used to estimate the fixed coefficients and the prior distribution of a random coefficient.Using the on-site measured data of the target equipment,the posterior distribution of a random coefficient and actual degradation state are step-by-step updated based on Bayesian inference and the extended Kalman filtering algorithm.The analytical form of the RUL distribution function is derived based on the first hitting time distribution.Combined with the two case studies,the proposed method is verified to have certain advantages over the existing methods in the accuracy of prediction.

The relationships among perceived organizational support,intention to remain,career success and self-esteem in Chinese male nurses

Aim:The purpose of this study was to investigate the relationships among perceived organizational support,intention to remain,career success and self-esteem in male nurses in China.Background:Recently,turnover is considered increasingly more normal.Male nurses could be stabilized by improving their perceived organizational support,career success and selfesteem.Design:A cross-sectional design was used in the present study.Method:405 full-time male Registered Nurses,from Grade A hospitals in Tianjin,were surveyed.Pearson correlation,Cronbach's alpha coefficient and structural equation modelling were conducted in the study.Results:The results illustrated that self-esteem mediated the relationship between perceived organizational support and career success.Career success mediated the relationship between self-esteem and intention to remain.Conclusion:Hospitals should pay more attention to the benefits of positive personality trait on working motivation.It is meaningful to enhance perceived organizational support,career success and self-esteem in the working process so that nurses are willing to stay in the current employment.