Bearing is a key component in rotary machines. Their failures may cause the abrupt shutdown of these machines, which would result in substantial economic losses. Therefore, the prediction of the ...remaining useful life (RUL) of bearings is regarded as one of the critical approaches to avoid failure of bearings and their systems. In this article, an ensemble data-driven approach is proposed to predict the RUL of bearings. It uses feature extraction, an attention mechanism, and uncertainty analysis. First, the features embedded in the bearings' vibration signals are extracted. Second, a stacked gated recurrent unit (GRU) is constructed to predict the bearing RUL. A novel attention mechanism based on dynamic time warping (DTW) is developed to improve the performance of information extraction, and a Bayesian approach is employed to analyze the prediction uncertainty. Finally, the proposed approach is validated using two benchmark-bearing data sets. The results show that the proposed approach can predict the bearing RUL effectively, and the prediction uncertainty can also be evaluated.
Nonlinear degradation trajectories are encountered frequently, and not all of them evolve homogeneously in practical systems. To take nonlinearity, heterogeneity, and the entire historical ...degradation data into account, we propose a nonlinear heterogeneous Wiener process model with an adaptive drift to characterize degradation trajectories. A state-space based method is employed to delineate our model. Due to the introduction of the adaptive drift, it is difficult to directly apply Kalman filter methods to update the distribution of the estimated degradation drift. To address this issue, we develop an online filtering algorithm based on Bayes' theorem. The expectation-maximization (EM) algorithm, as well as a novel Bayes'-theorem-based smoother, are adopted to estimate the unknown parameters in our model. Moreover, the distribution of the predicted remaining useful life (RUL) incorporating the complete distribution of the estimated degradation drift is achieved analytically. Finally, a simulation, and a case study are provided to validate the proposed approach.
This paper introduces a real-time reliability prediction method for a dynamic system which suffers from a hidden degradation process. The hidden degradation process is firstly identified by use of ...particle filtering based on measurable outputs of the considered dynamic system. Then the system's reliability is predicted according to the model of the degradation path. We analyze the identification algorithm mathematically, and validate the effectiveness of this method through computer simulations of a three-vessel water tank. This real-time reliability prediction method is beneficial to the dynamic system's condition monitoring, and may be further helpful to make a proper predictive maintenance policy for the system.
A steam turbine is one of the critical components in a power generation system whose failure may result in unexpected consequences, even catastrophic losses. Thus, the reliability of steam turbines ...needs to be guaranteed all the time, which requires that its health state can be monitored and predicted effectively. Due to various failure modes, it is difficult to build physics-of-failure models used for health prognostics for steam turbines. In this paper, a data-driven integrated framework for health prognostics for steam turbines, which is based on extreme gradient boosting (XGBoost) and dynamic time warping (DTW), is proposed. The proposed framework includes two modules: anomaly detection and remaining useful life (RUL) prediction. The anomalies refer to the overall abnormal operation of steam turbines. In the process of anomaly detection, the temporal variables which can represent the operating conditions of the considered steam turbine are selected first. Appropriately selected temporal variables can reduce the input dimension and will improve real-time performance. Then, XGBoost is used to detect anomalies based on learning historical data. In the process of RUL prediction, a similarity-based algorithm with DTW is used to gain the RUL by contrasting the measured temporal variables with those in the historical cases. The similarity-based algorithm can predict the RUL without establishing a degradation path model, which can avoid the difficulties in parameter estimation for the degradation model and model generalization. The proposed framework is validated by real case studies from an industrial steam turbine. The results show that the proposed approach can detect the anomalies successfully and predict the RUL effectively.
A dynamic preventive maintenance policy for system with continuously degrading components is investigated in this paper. Different from traditional cost-centric preventive maintenance policy, our ...maintenance strategy is formulated from the value perspective. Component value is modelled as a function of component reliability distribution. Maintenance action is triggered whenever the system reliability drops below a certain threshold. Our policy mainly consists of two steps: (i) determine which component to maintain; (ii) determine to what degree the component should be maintained. In Step 1, we introduce the yield-cost importance to select the most important component. In Step 2, the optimal maintenance level is obtained by maximizing the net value of the maintenance action. Finally, numerical examples are given to illustrate the proposed policy.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
While a specific system is in use, its reliability will decrease gradually after the infant mortality period because of the components' degradation, or external attacks. Thus, reliability is a ...natural characteristic of a system's health, and can be used for condition monitoring & predictive maintenance. This paper introduces a new real-time reliability prediction method for dynamic systems which incorporates an on-line fault prediction algorithm. The factors that may reduce a system's reliability are modeled as an additive fault input to the system, and the fault is assumed to be varying linearly with time, approximately. The time-varying fault is roughly estimated based on a modified particle filtering algorithm at first. Then, as a time series, the fault estimate sequence is smoothed, and predicted by an exponential smoothing method. Mathematical analysis shows that the effects of the system, and measurement noises on the fault estimates are greatly reduced by exponential smoothing, which indicates that the comparatively high accuracy of the fault estimates & predictions is guaranteed. Based on the particle filtering & fault prediction results, the whole system's predictive reliability is computed through a Monte Carlo simulation strategy. The effectiveness of the proposed real-time reliability prediction method is validated by a computer simulation of a three-vessel water tank system.
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In this study, we investigated the performance of a synthetic resin for the adsorption of Li from pre-desilicated solution which is the waste liquid produced by extracting aluminum ...from fly ash. The adsorption kinetics and isotherms of the resin were obtained and analyzed. The saturated adsorption sites of the resin were in agreement with the quasi-second-order kinetic model. Then, the pore diffusion model (PDM) was applied to represent the lithium adsorption kinetics which confirming that the external mass is the limiting step. Moreover, we evaluated the adsorption properties of this resin in fixed-bed mode. We established a feasible extraction process for Li from strong alkaline solutions with low Li concentrations. The process parameters, such as the flow rate, initial adsorption solution concentration, water washing process, desorption agent concentration, and flow rate were studied. The desorption rate of the Li+ ions was directly proportional with the concentration of the desorption agent. The time required to accumulate Li decreased as the hydrochloric acid concentration and flow rate increased. Time of the peak appeared increased from 0.5 bed volume (BV) to 2.5 BV as the concentration was increased from 1 to 3 mol·L−1, and the peak increased from 231 to 394 mg·L−1. The resin presented good selectivity for Li+ ions and could effectively separate impurity ions from the pre-desilication solution.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The coal pulverizing system is an important auxiliary system in thermal power generation systems. The working condition of a coal pulverizing system may directly affect the safety and economy of ...power generation. Prognostics and health management is an effective approach to ensure the reliability of coal pulverizing systems. As the coal pulverizing system is a typical dynamic and nonlinear high-dimensional system, it is difficult to construct accurate mathematical models used for anomaly detection. In this paper, a novel data-driven integrated framework for anomaly detection of the coal pulverizing system is proposed. A neural network model based on gated recurrent unit (GRU) networks, a type of recurrent neural network (RNN), is constructed to describe the temporal characteristics of high-dimensional data and predict the system condition value. Then, aiming at the prediction error, a novel unsupervised clustering algorithm for anomaly detection is proposed. The proposed framework is validated by a real case study from an industrial coal pulverizing system. The results show that the proposed framework can detect the anomaly successfully.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
In order to build an effective condition monitoring (CM) model for the target wind turbines (WTs) with few operational data, an approach based on the feature transfer learning and a modified ...generative adversarial network is proposed. First, a large amount of labelled data from WTs are analyzed to construct a CM model with the aid of an autoencoder. This forms the knowledge of CM for WTs in the source domain. Second, a generative adversarial network is trained to build a mapping relationship between the features of different WTs. Third, the health status of the target WT is determined by analyzing the data collected from it online based on the proposed approach. Two case studies are conducted to verify that the proposed method can transfer the CM knowledge from source WT to target WT and achieve good performance in the CM of target WT.
Background
Depression is associated with an increased risk of death in patients with coronary heart disease (CHD). This study aimed to explore the factors influencing depression in elderly patients ...with CHD and to construct a prediction model for early identification of depression in this patient population.
Materials and methods
We used propensity-score matching to identify 1,065 CHD patients aged ≥65 years from four hospitals in Chongqing between January 2015 and December 2021. The patients were divided into a training set (
n
= 880) and an external validation set (
n
= 185). Univariate logistic regression, multivariate logistic regression, and least absolute shrinkage and selection operator regression were used to determine the factors influencing depression. A nomogram based on the multivariate logistic regression model was constructed using the selected influencing factors. The discrimination, calibration, and clinical utility of the nomogram were assessed by the area under the curve (AUC) of the receiver operating characteristic curve, calibration curve, and decision curve analysis (DCA) and clinical impact curve (CIC), respectively.
Results
The predictive factors in the multivariate model included the lymphocyte percentage and the blood urea nitrogen and low-density lipoprotein cholesterol levels. The AUC values of the nomogram in the training and external validation sets were 0.762 (95% CI = 0.722–0.803) and 0.679 (95% CI = 0.572–0.786), respectively. The calibration curves indicated that the nomogram had strong calibration. DCA and CIC indicated that the nomogram can be used as an effective tool in clinical practice. For the convenience of clinicians, we used the nomogram to develop a web-based calculator tool (
https://cytjt007.shinyapps.io/dynnomapp_depression/
).
Conclusion
Reductions in the lymphocyte percentage and blood urea nitrogen and low-density lipoprotein cholesterol levels were reliable predictors of depression in elderly patients with CHD. The nomogram that we developed can help clinicians assess the risk of depression in elderly patients with CHD.
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