Similarity-based Particle Filter for Remaining Useful Life prediction with enhanced performance

Cai, Haoshu; Feng, Jianshe; Li, Wenzhe; Hsu, Yuan-Ming; Lee, Jay

doi:10.1016/j.asoc.2020.106474

Cited by 51 publications

(18 citation statements)

References 28 publications

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“…Algorithm. Particle filter is an algorithm proposed based on the continuity of target motion [29,30]. e general particle filter-tracking problem can be expressed in the following way.…”

Section: Particle Filter Convolutional Target Adaptive Trackingmentioning

confidence: 99%

[Retracted] Target Adaptive Tracking Based on GOTURN Algorithm with Convolutional Neural Network and Data Fusion

Jia-dong

2021

Computational Intelligence and Neuroscience

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With the advent of the artificial intelligence era, target adaptive tracking technology has been rapidly developed in the fields of human-computer interaction, intelligent monitoring, and autonomous driving. Aiming at the problem of low tracking accuracy and poor robustness of the current Generic Object Tracking Using Regression Network (GOTURN) tracking algorithm, this paper takes the most popular convolutional neural network in the current target-tracking field as the basic network structure and proposes an improved GOTURN target-tracking algorithm based on residual attention mechanism and fusion of spatiotemporal context information for data fusion. The algorithm transmits the target template, prediction area, and search area to the network at the same time to extract the general feature map and predicts the location of the tracking target in the current frame through the fully connected layer. At the same time, the residual attention mechanism network is added to the target template network structure to enhance the feature expression ability of the network and improve the overall performance of the algorithm. A large number of experiments conducted on the current mainstream target-tracking test data set show that the tracking algorithm we proposed has significantly improved the overall performance of the original tracking algorithm.

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“…Algorithm. Particle filter is an algorithm proposed based on the continuity of target motion [29,30]. e general particle filter-tracking problem can be expressed in the following way.…”

Section: Particle Filter Convolutional Target Adaptive Trackingmentioning

confidence: 99%

[Retracted] Target Adaptive Tracking Based on GOTURN Algorithm with Convolutional Neural Network and Data Fusion

Jia-dong

2021

Computational Intelligence and Neuroscience

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“…Then, for example, the most similar datasets can be selected as training data. Such approaches are presented in [180], [181], and [182]. However, by actively minimizing the MMD, source and target distributions can also be aligned.…”

Section: A Feature Alignment By the Maximum Mean Discrepancymentioning

confidence: 99%

Using Data From Similar Systems for Data-Driven Condition Diagnosis and Prognosis of Engineering Systems: A Review and an Outline of Future Research Challenges

Braig

Zeiler

2023

IEEE Access

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Prognostics and health management (PHM) is an engineering approach dealing with the diagnosis, prognosis, and management of the health state of engineering systems. Methods that can analyze system behavior, fault conditions, and degradation are crucial for PHM applications, as they create the basis for determining, predicting, and monitoring the health of engineering systems. Data-driven methods have been proven to be suitable for automated diagnosis or prognosis due to their pattern recognition and anomaly detection abilities. Moreover, they do not require knowledge of the underlying degradation process. However, training data-driven methods usually requires a large amount of data, whose collection, cleansing, organization, and preparation are generally very time-consuming and costly. There are usually little or no run-to-failure data available at market launch, especially for new systems such as new machine generations. Nevertheless, related systems, hereinafter referred to as similar systems, often already exist, differing only in some technical characteristics. In this paper, the similar system problem is defined, and explanations of the difficulties that arise when using data from similar systems are presented. Furthermore, it is discussed why the usage of these data offers great potential for condition diagnosis and prognosis of engineering systems. An overview of data-driven methods that can be used to utilize data from similar systems is provided, and the methods that such systems already consider are highlighted. Two related research areas are identified, namely, fleet learning and transfer learning. In the paper, it is shown that similar system approaches will become an important branch of research in PHM. However, some difficulties must be overcome.INDEX TERMS Condition diagnosis, condition prognosis, data-driven methods, fleet learning, prognostics and health management (PHM), similar system approach, similar system problem, transfer learning.

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“…Due to the limitation of the paper length, the implementation process of the drift coefficient adaptive update is appropriately omitted. The corresponding specific theoretical basis can be found in Li et al, 30 Zhang et al, 36 Weiet al, 52 and Cai et al 53 Since the self-similar parameter, that is, Hurst exponent H has no coupling relationship with other parameters of the degradation process described by Equation ( 5), the parameter of H can be estimated separately. The approaches such as rescaled range analysis, 28 multifractal detrended fluctuation analysis, 54,55 and wavelet analysis 56 have been applied to estimate the Hurst exponent H. However, each method has its own shortcomings.…”

Section: Parameters Estimationmentioning

confidence: 99%

“…In addition, the RUL prediction results of Bearing1_1 in the 100th min are presented in Figure 18(c), which demonstrates that the developed method (M0) is with higher prediction accuracy. The quantitative evaluation of RUL prediction performance for Bearing1_1, that is, MAE and RMSE calculated by Equations ( 52) and (53), is shown in Table 6, indicating a smaller prediction error of M0.…”

Section: Fusion (Mmf) Is Expressed As MDmentioning

confidence: 99%

Performance degradation assessment for mechanical system based on semi-analytical solution of self-similar stable distribution process

et al. 2023

Structural Health Monitoring

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To more accurately predict remaining useful life (RUL) and quantitatively evaluate the uncertainty of the predicted results, a performance degradation assessment framework based on semi-analytical solution of self-similar stable distribution process is proposed. The established performance degradation model based on adaptive fractional Lévy stable motion (AFLSM) is more flexible in revealing the long-range dependence, non-Gaussian, and heavy-tailed distribution properties of the incremental behavior. The corresponding stable distribution parameters are estimated through characteristic function method, and Hurst exponent is calculated based on the generalized Hurst exponent approach with narrower confidence interval. Aiming at the difficulties in solving the exact analytical solution and the excessive computation of the numerical solution in the whole process, based on Mellin-Stieltjes transform and direct integration, a semi-analytical solution of RUL distribution function is proposed, which can be readily implemented in practical equipment operations. The proposed performance degradation assessment framework is validated by the novel truck transmission dataset and the benchmark rolling bearing dataset. Experimental results indicate that the developed framework is more effective and superior than other state-of-the-art approaches in terms of RUL prediction.

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Similarity-based Particle Filter for Remaining Useful Life prediction with enhanced performance

Cited by 51 publications

References 28 publications

[Retracted] Target Adaptive Tracking Based on GOTURN Algorithm with Convolutional Neural Network and Data Fusion

[Retracted] Target Adaptive Tracking Based on GOTURN Algorithm with Convolutional Neural Network and Data Fusion

Using Data From Similar Systems for Data-Driven Condition Diagnosis and Prognosis of Engineering Systems: A Review and an Outline of Future Research Challenges

Performance degradation assessment for mechanical system based on semi-analytical solution of self-similar stable distribution process

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