共查询到20条相似文献，搜索用时 15 毫秒
1.
Bin Zhang Lijun Zhang Jinwu Xu 《Quality and Reliability Engineering International》2016,32(2):547-554
Rolling element bearings are among the most widely used and also vulnerable components in rotating machinery equipment. Recently, prognostics and health management of rolling element bearings is more and more attractive both in academics and industry. However, many studies have been focusing on the prognostic aspect of bearing prognostics and health management and few efforts have been performed in relation to the optimal degradation feature selection issue. For more effective and efficient remaining useful life predictions, three goodness metrics of correlation, monotonicity and robustness are defined and combined for automatically more relevant degradation feature selection in this paper. Effectiveness of the proposed method is verified by rolling element bearing degradation experiments. Copyright © 2015 John Wiley & Sons, Ltd. 相似文献
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鉴于Gamma过程具有平稳、独立增量等退化建模所需的属性,将其用于描述设备退化过程,并针对缺乏故障数据时难以进行剩余寿命预测的问题,利用设备运行中采集的表征其退化状态的大量间接状态参数和少量直接状态参数,建立了基于Gamma退化过程的剩余寿命预测模型;针对经验最大化算法中似然函数难以解析求解的问题,引入粒子滤波算法实现了模型参数估计;最后将模型应用于直升机主减速器行星架的剩余寿命预测,得到了不同时刻的预测结果及95%置信区间,验证了预测模型的有效性和准确性。 相似文献
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Accurate prediction of remaining useful life (RUL) plays an important role in the formulation of maintenance strategies. However, due to the diversity of the failure mode of equipment, there are significant differences between the degradation data, which greatly affects the accuracy of RUL prediction. In this case, an ensemble prediction model considering health index-based (HI-based) classification is proposed in this paper. Firstly, the stacked autoencoder (SAE) is employed to construct the HI. Then, the time window is used to sequentially process the HI sequence, so that many data segments with the same length can be achieved. To differentiate the data with the similar degradation process, K-means and Xgboost are selected to construct offline and online data classification models respectively. Finally, according to the results of the data classification, the ensemble model that integrates multiple machine learning methods are separately trained and then adaptively used for RUL prediction. In addition, integrating multiple methods helps to improve the generalization ability of the model. The NASA C-MAPSS dataset is applied to verify the effectiveness of the proposed method, and the results show that the proposed method achieves a higher prediction accuracy and shorter computational time than other existing prediction models. 相似文献
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Christophe Letot Pierre Dehombreux Guillaume Fleurquin Arnaud Lesage 《Quality and Reliability Engineering International》2017,33(8):2043-2058
This paper presents an adaptive maintenance model for equipment that can be adjusted (minor preventive maintenance, imperfect state) or replaced (major preventive maintenance, as good as new) at specific scheduled times based on degradation measurements. An initial reliability law that uses a degradation‐based model is built from the collection of hitting times of a failure threshold. Inspections are performed to update the reliability, the remaining useful life, and the optimum time for preventive maintenance. The case of both as good as new replacements and imperfect adjustments is considered. The proposed maintenance model is based on the optimization of the long‐term expected cost per unit of time. The model is then tested on a numerical case study to assess its effectiveness. This results in an improvement for the occurrences of maintenance tasks that minimizes the mean cost per unit of time as well as an optimized number of adjustments that can be considered before replacing an item. The practical application is a decision aid support to answer the 2 following questions: Should we intervene now or wait for the next inspection? For each intervention, should we adjust or replace the item of equipment? The originality is the presence of 2 criteria that help the maintainer to decide to postpone or not the preventive replacement time depending on the measured degradation and to decide whether the item should be adjusted or replaced. 相似文献
6.
Li Yongxiang Shi Jianming Wang Gong Zhang Mengying 《Quality and Reliability Engineering International》2017,33(8):2711-2725
Prognostics, in other words, remaining useful life (RUL) estimation is a core task of prognostics and health management (PHM). Reliable RUL predictions can reduce maintenance costs, improve production efficiency, and avoid unexpected downtime. Lots of models for RUL predictions have been proposed; however, noise and the nonlinear nature of degradation phenomena often leads to poor prognostics results, and the acquired engineered system data are usually subject to a high level of uncertainty. This makes the RUL estimation models less than satisfactory. Accurate RUL estimation and prediction not only rely on an accurate model but also depend on the adjustments of model parameters to track the variation. In this paper, an ensemble model combining the health index synthesis (HIS) approach and improved particle filtering (PF) is introduced. HIS approach was used to obtain the synthesized health index (SHI) for an engineered system with multiple sensors, which indicated the system's degradation model, while the improved PF approach was used to adjust the parameters of the degradation model obtained from the HIS approach and optimized the RUL estimation results. The performance of the prognostics approach introduced in this paper was demonstrated by using turbofan engine degradation data sets, which was supplied by NASA Ames, and results were compared with several usually used methods. 相似文献
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Mitra Fouladirad Massimiliano Giorgio Gianpaolo Pulcini 《Quality and Reliability Engineering International》2023,39(2):546-564
Most of the stochastic models adopted to describe the evolution over time of degradation phenomena of technological units assume that their degradation level can increase indeterminately. However, these degradation phenomena are typically subjected to obvious bounds, if only because technological units have finite size. In fact, very often, this inconsistency does not significantly affect the effectiveness of unbounded degradation models, since degrading units are usually assumed to fail when their degradation level exceeds a failure threshold that is much smaller than the obvious bounds. Nevertheless, in some cases, due to the very nature of the underlying degradation mechanism, less obvious bounds could exist, which are not necessarily far from the failure thresholds. The question that arises is whether the use of a bounded degradation model, in this latter type of experimental situations, could be beneficial. For this purpose, since a bounded degradation process should necessarily have dependent increments, in this paper we investigate the potential of a new bounded transformed gamma (TG) process to adequately describe bounded degradation phenomena and predict their future evolution. Differently from other existing gamma process based bounded degradation models, here the upper bound is treated as an unknown parameter that has to be estimated from the available degradation data. A numerical example is presented where the parameters of the proposed model are estimated from simulated data. Then the model is applied to a set of wear measures of cylinder liners that equip a diesel engine for marine propulsion, which have also stimulated this study. Model parameters are estimated by using the maximum likelihood (ML) method. The fitting ability of the proposed new bounded process is compared to that of an unbounded gamma process, which was previously adopted to analyze the same liner wear data. Obtained results are critically discussed in the paper. 相似文献
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Ke Yang Yong-jian Wang Yu-nan Yao Shi-dong Fan 《Quality and Reliability Engineering International》2021,37(3):1080-1098
Advancements in information technology have made various industrial equipment increasingly sophisticated in recent years. The remaining useful life (RUL) of equipment plays a crucial important role in the industrial process. It is difficult to establish a functional RUL model as it requires the fusion of time-series data across different scales. This paper proposes a long-short term memory neural network, which integrates a novel partial least square based on a genetic algorithm (GAPLS-LSTM). The parameters are first analyzed by PLS to obtain the parameter fusion function of the health index (HI). The GA then searches the optimal coefficients of the function; the expected HI values can be calculated with the fusion function. Finally, the RUL of the equipment is predicted with the LSTM method. The proposed GAPLS-LSTM was applied to RUL prediction of a marine auxiliary engine to validate it by comparison against GAPLS-BP and GAPLS-RNN methods. The results show that the proposed method is capable of effective RUL prediction. 相似文献
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基于GA-ELM的锂离子电池RUL间接预测方法 总被引：1，自引：0，他引：1
针对锂离子电池在线剩余寿命预测时容量难以直接测量及预测精度不高等问题,提出一种间接预测方法。首先,分析电池寿命状态特征参数,选取等压降放电时间作为锂电池间接健康因子;其次,引入遗传算法优化极限学习机模型参数,建立锂电池剩余使用寿命间接预测模型;最后,基于NASA锂电池实验数据和自主实验数据验证该预测方法的正确性和有效性。实验结果表明,相较高斯过程回归方法和极限学习机方法,该方法准确有效、测试速度快,并且预测结果输出稳定,精度较高。 相似文献
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This paper concerns Remaining Useful Life (RUL) estimation of discrete event systems. For that purpose, physics-based models with partially observed stochastic Petri nets are used to represent the system and its sensors. The advantage of the proposed modelling approach is to provide a realistic representation of the system, including the interaction between the normal behaviours and the failure processes. From the proposed modelling and collected measurements, timed trajectories, which are consistent with the observations, are obtained. Based on the event dates, our approach consists in evaluating the probabilities of the consistent behaviours using probabilistic models. State estimation is obtained as a consequence. The most probable future degradations, from the current state, are then considered and a method for fault prognosis is presented. Finally, the prognosis result is used to estimate the RUL as a time interval. A case study is proposed to show the applicability of the proposed method. 相似文献
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Jalloul Elmeliani Lotfi Nabli Hassani Messaoud 《Quality and Reliability Engineering International》2013,29(1):125-138
This paper aims at developing a model for the monitoring of a rotating machine. The contribution in this model is the development of the last stage of monitoring which is the prognostic; indeed, the method used takes into account the required quality criteria of the product on one hand and the state of the current system of one somewhere else on the other. The objective of this contribution is to estimate the reliability of the system in time and to plan the time of total system dysfunction. The interval constrained Petri nets are used for the modeling of an industrial example which is the centrifuge pump. Copyright © 2012 John Wiley & Sons, Ltd. 相似文献
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Fu-Kwun Wang Chang-Yi Huang Tadele Mamo Xiao-Bin Cheng 《Quality and Reliability Engineering International》2021,37(1):34-46
Proton exchange membrane fuel cell (PEMFC) stacks are widely used in mobile and portable applications due to their clean and efficient model of operation. We propose an ensemble model based on a stacked long short-term memory model that combines three machine-learning models, including long short-term memory with attention mechanism, support vector regression, and random forest regression, to improve the degradation prediction of a PEMFC stack. The prediction intervals can be derived using the dropout technique. The proposed model is compared with some existing models using two PEMFC stacks. The results show that the proposed model outperforms the other models in terms of mean absolute percentage error and root mean square error. Regarding the remaining useful life prediction, the proposed model with the sliding window approach can provide better results. 相似文献
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With the increase of product reliability, collecting time‐to‐failure data is becoming difficult, and degradation‐based method has gained popularity. In this paper, a novel multi‐hidden semi‐Markov model is proposed to identify degradation and estimate remaining useful life of a system. Multiple fused features are used to describe the degradation process so as to improve the effectiveness and accuracy. The similarities of the features are depicted by a new variable combined with forward and backward variables to reduce computational effort. The degradation state is identified using modified Viterbi algorithm, in which linear function is adopted to describe the contribution of each feature to the state recognition. Subsequently, the remaining useful life can be forecasted by backward recursive equations. A case study is presented, and the results demonstrate the validity and effectiveness of the proposed method. Copyright © 2012 John Wiley & Sons, Ltd. 相似文献
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Wenbin Wang 《国际生产研究杂志》2013,51(19):5764-5779
When we schedule a system to perform a task, a factor that should be taken into account is the remaining useful life prognostics of the system. This prognostics of the system may depend not only on the health state of the system, but also on the characteristics of the task to be performed. Assuming such prognostics is available at the time of system scheduling, the problem is to find a method to schedule the system, which can improve the expected profit rate. Two system life models were proposed for the case considered in this paper. Due to the dynamic nature of the problem, a global optimal policy is hard to find, we proposed an approach based on the approximated expected profit rate to schedule the systems. The approach is validated through simulations compared with a number of other task scheduling rules to show the advantage of the proposed approach. We also find the optimal global stationary result by exhaustive search of small scheduling problems of few systems and tasks to compare with the proposed approximate one. Further numerical analyses are presented to demonstrate the process of determining a decision variable and the sensitivity analysis in terms of a cost parameter. 相似文献
15.
Mohammed Bouzenita Leila-Hayet Mouss Farid Melgani Toufik Bentrcia 《Quality and Reliability Engineering International》2020,36(6):2146-2169
In this paper, we propose new fusion and selection approaches to accurately predict the remaining useful life. The fusion scheme is built upon the combination of outcomes delivered by an ensemble of Gaussian process regression models. Each regressor is characterized by its own covariance function and initial hyperparameters. In this context, we adopt the induced ordered weighted averaging as a fusion tool to achieve such combination. Two additional fusion techniques based on the simple averaging and the ordered weighted averaging operators besides a selection approach are implemented. The differences between adjacent elements of the raw data are used for training instead of the original values. Experimental results conducted on lithium-ion battery data report a significant improvement in the obtained results. This work may provide some insights regarding the development of efficient intelligent fusion alternatives for further prognostic advances. 相似文献
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Morteza Soleimani Felician Campean Daniel Neagu 《Quality and Reliability Engineering International》2021,37(8):3746-3778
Diagnostics and prognostics have a significant role in the reliability enhancement of systems and are focused topics of active research. Engineered systems are becoming more complex and are subjected to miscellaneous failure modes that impact adversely their performability. This ever-increasing complexity makes fault diagnostics and prognostics challenging for the system-level functions. A significant number of successes have been achieved and acknowledged in some review papers; however, these reviews rarely focused on application to complex engineered systems nor provided a systematic review of diverse techniques and approaches to address the related challenges. To bridge the gap, this paper first presents a review to systematically cover the general concepts and recent development of various diagnostics and prognostics approaches, along with their strengths and shortcomings for the application of diverse engineered systems. Afterwards, given the characteristics of complex systems, the applicability of different techniques and methods that are capable to address the features of complex systems are reviewed and discussed, and some of the recent achievements in the literature are introduced. Finally, the unaddressed challenges are discussed by taking into account the characteristics of automotive systems as an example of complex systems. In addition, future development and potential research trends are offered to address those challenges. Consequently, this review provides a systematic view of the state-of-the-art and case studies with a reference value for scholars and practitioners. 相似文献
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Xiaochuan Li David Mba Edmund Okoroigwe Tianran Lin 《Quality and Reliability Engineering International》2021,37(2):681-693
In this study, a three-step remaining service life (RSL) prediction method, which involves feature extraction, feature selection, and fusion and prognostics, is proposed for large-scale rotating machinery in the presence of scarce failure data. In the feature extraction step, eight time-domain degradation features are extracted from the faulty variables. A fitness function as a weighted linear combination of the monotonicity, robustness, correlation, and trendability metrics is defined and used to evaluate the suitability of the features for RSL prediction. The selected features are merged using a canonical variate residuals-based method. In the prognostic step, gray model is used in combination with empirical Bayesian algorithm for RSL prediction in the presence of scarce failure data. The proposed approach is validated on failure data collected from an operational industrial centrifugal pump and a compressor. 相似文献
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容量或内阻是衡量锂离子电池健康状态的重要指标,但在锂电池实际运行中,其容量和内阻很难实时获取。为此,提出了基于放电过程信息获取新健康指标的方法,并对锂电池的剩余寿命进行预测。主要研究了锂电池放电过程中电压变化的规律,提出两种可在线测量的新健康指标,并通过Box-Cox变换修正了新健康指标的准确性。比较分析表明,所提取的健康指标与容量之间存在着强相关性,在某种程度上可以解决锂电池容量难以在线测量的问题。此外还基于新健康指标建立了锂电池退化过程模型,并利用相关向量机算法进行锂电池的剩余寿命预测。实验结果表明,在寿命预测性能上相关向量机算法优于其他算法,并且预测时间越晚,预测结果就越准确,所提取的健康指标也能够很好地描述锂电池的退化过程,并在剩余寿命预测结果上表现优越。 相似文献
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In this paper, we investigate a joint modeling method for hard failures where both degradation signals and time‐to‐event data are available. The mixed‐effects model is used to model degradation signals, and extended hazard model is used for the time‐to‐event data. The extended hazard is a general model which includes two well‐known hazard rate models, the Cox proportional hazards model and accelerated failure time model, as special cases. A two‐stage estimation approach is used to obtain model parameters, based on which remaining useful life for the in‐service unit can be predicted. The performance of the method is demonstrated through both simulation studies and a real case study. 相似文献
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Xinhua Lu;Haobo Pan;Lingxiao Zhang;Li Ma;Hui Wan; 《Quality and Reliability Engineering International》2024,40(4):1795-1810
Predicting the remaining useful life (RUL) of an engine is one of the key tasks of Prognostics and health management (PHM). Modern mechanical equipment typically operates in complex operating conditions and fault modes, leading to dispersed distribution of sensor data and challenges for feature extraction. To improve the accuracy of predicting the RUL under the complex scenarios, this paper proposes a multi-scale convolutional network (CNN) and bidirectional gated recurrent unit (MSC-BiGRU) mode under a dual path framework with temporal attention. Specifically, the multi-scale CNN in the first path is to learn complex features, and Swish Activation function is used to improve the prediction ability of the network; the bidirectional gated recurrent unit (BiGRU) in the second path can handle both forward and backward time series, and adaptively capture the importance of outputs at different times using temporal attention, enhancing the model's feature extraction ability in the temporal dimension. A feature fusion mechanism is developed to connect two paths in parallel, overcoming the overfitting and high computational complexity in deep complex models. We verify the effectiveness of the proposed method using a simulated turbofan engine dataset, especially on datasets FD002 and FD004 under complex operating conditions and fault modes, the RMSE values were reduced by 17.37% and 9.97%, respectively, compared to the BiGRU-TSAM. 相似文献