Optimize the Signal Quality of the Composite Health  Index via Data Fusion for Degradation Modeling  and Prognostic Analysis

Liu, Kaibo; Chehade, Abdallah; Song, Changyue

doi:10.1109/tase.2015.2446752

Cited by 117 publications

(26 citation statements)

References 24 publications

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“…The marginal degradation models are established according to (1) and 2, and the degradation trajectory µ k (•) should be determined first. Liu et al [27] predicted the RUL on this dataset with the exponential model, here we also establish the exponential model for degradation indicators as…”

Section: B Degradation Modeling and Parameter Estimationmentioning

confidence: 99%

Remaining Useful Life Prediction for Complex Systems With Multiple Indicators Based on Particle Filter and Parameter Correlation

et al. 2020

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In practical applications, the failure of large-scale complex equipment is often caused by the simultaneous degradation of multiple components. It is necessary to predict the remaining useful life (RUL) of the equipment with multiple degradation indicators. This paper proposes a new joint-RUL-prediction method in the presence of multiple degradation indicators based on parameter correlation. The stochastic process model is established for each degradation indicator, and the model parameters are estimated by kernel smoothing particle filter (KS-PF) and maximum likelihood estimation (MLE). Meanwhile, to facilitate the dependencies between multiple degradation indicators, correlations of the degradation model parameter between multiple degradation indicators are established in KS-PF. In addition, optimal tuning (OT) is introduced to choose the best kernel parameter. A case study on the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset is applied to verify the proposed method, the experiment shows that the proposed joint-RUL-prediction method based on parameter correlation possesses a superior prediction performance compared with that by using a single degradation indicator.

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Section: B Degradation Modeling and Parameter Estimationmentioning

confidence: 99%

Remaining Useful Life Prediction for Complex Systems With Multiple Indicators Based on Particle Filter and Parameter Correlation

et al. 2020

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“…In addition, the time series is used to predict the future aggregated value, and finally the context fusion value and the predicted value are input to the type-2 fuzzy inference system to obtain high-accuracy event recognition. Liu et al [32] applied a data fusion method to health monitoring systems and developed a new data-level fusion model. The model fuses the information of multiple degraded signals to construct a comprehensive health index, which solves the problem of predicting when multiple sensors simultaneously monitor the health status of degraded units.…”

Section: Related Workmentioning

confidence: 99%

Multi-Sensor Data Fusion Algorithm Based on Trust Degree and Improved Genetics

Sun¹,

Zhang²,

Zheng³

et al. 2019

Sensors

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Aiming at the problems of low data fusion precision and poor stability in greenhouse wireless sensor networks (WSNs), a multi-sensor data fusion algorithm based on trust degree and improved genetics is proposed. The original data collected by the sensor nodes are sent to the gateway through the sink node, and data preprocessing based on cubic exponential smoothing is performed at the gateway to eliminate abnormal data and noise data. In fuzzy theory, the range of membership functions is determined, according to this feature, the data fusion algorithm based on exponential trust degree is used to fuse the smooth data to avoid the absolute degree of mutual trust between data. In this paper, we have improved the crossover and mutation operations in the standard genetic algorithm, the variation is separated from the intersection, the chaotic sequence is used to determine the intersection, and the weakest single-point intersection is implemented to improve the convergence accuracy of the algorithm, weaken and avoid jitter problems during optimization. The chaotic sequence is used to mutate multiple genes in the chromosome to avoid premature algorithm maturity. Finally, the improved genetic algorithm is used to optimize the fusion estimation value. The experimental results show that the cubic exponential smoothing can significantly reduce the data fluctuation and improve the stability of the system. Compared with the commonly used data fusion algorithms such as arithmetic average method and adaptive weighting method, the data fusion algorithm based on trust degree and improved genetics has higher fusion precision. At the same time, the execution time of the algorithm is greatly reduced.

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“…In the literature, Khelif et al abandoned the tedious procedure of estimating degradation states and modeled the relationship among the sensors as the HI by using the support vector regression. For the case when one single sensor cannot show all the features of the degradation system, Liu et al considered data fusion and then presented a novel indicator by integrating multi‐sensor observations under a constraint of the signal‐to‐noise ratio metric. Zhao et al exploited both neural network and learned network as a pattern learning‐based method, for the purpose of discovering the relation between the multivariate time series degradation data and the RULs.…”

Section: Introductionmentioning

confidence: 99%

Remaining useful life prediction for multivariable stochastic degradation systems with non‐Markovian diffusion processes

Zhou

Chen

et al. 2020

Quality & Reliability Eng

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Multivariable stochastic degradation system (MSDS) is quite common in indus-tries such as blast furnace ironmaking, vehicle transportation, and aerospace manufacturing. Large-scale complex equipments may be affected by multiple factors, resulting in not just a single deteriorating performance characteristic. It is difficult to handle unknown failure structures of practical systems by using traditional univariate degradation modeling methods. A novel health index (HI) is constructed to quantitatively analyze the health state for the overall system. Considering the interaction between internal reactions and external environments, the fractional Brownian motion (FBM), a typical non-Markovian diffusion process, is added for the purpose of reflecting stochastic uncertainties and memory effects. Based on the wavelet estimators and the maximum likelihood estimation (MLE) algorithm, multi-sensor observations of degradation variables are analyzed simultaneously to identify model parameters. A closed-form distribution of system-level remaining useful life (RUL) is obtained with a mild two-layer approximation. Relevant case studies are then handled that adequately demonstrate the effectiveness and the practical utility of the proposed method.

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Optimize the Signal Quality of the Composite Health Index via Data Fusion for Degradation Modeling and Prognostic Analysis

Cited by 117 publications

References 24 publications

Remaining Useful Life Prediction for Complex Systems With Multiple Indicators Based on Particle Filter and Parameter Correlation

Remaining Useful Life Prediction for Complex Systems With Multiple Indicators Based on Particle Filter and Parameter Correlation

Multi-Sensor Data Fusion Algorithm Based on Trust Degree and Improved Genetics

Remaining useful life prediction for multivariable stochastic degradation systems with non‐Markovian diffusion processes

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