Remaining useful life prediction via long‐short time memory neural network with novel partial least squares and genetic algorithm

Yang, Ke; Wang, Yongjian; Yao, Yunan; Fan, Shidong

doi:10.1002/qre.2782

Cited by 12 publications

(10 citation statements)

References 24 publications

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“…Here, genetic algorithm (GA) is applied to find the optimal weight coefficients. 37 The detailed procedure is presented in Figure 4. The optimal fusion weight can be obtained after appropriate iterations.…”

Section: Multi-sensor Data Fusion Modelmentioning

confidence: 99%

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Remaining useful life prediction using composite health index and hybrid LSTM‐SVR model

Zheng

Zhou

et al. 2022

Quality & Reliability Eng

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Remaining useful life (RUL) plays an important role in prognostic and health management to reduce maintenance costs and avoid possible accidents. Massive multi‐sensor data makes it challenging to extract degradation features and predict RUL. This paper develops a novel RUL prediction framework consisted of a multi‐sensor fusion model and a hybrid prediction model. HI curves are constructed by synthesizing multiple metrics including time correlation, monotonicity, robustness, and consistency, making our data fusion method different from existing methods considering only one metric. In many practical situations, the initial degradation levels of obtained HI curves are different, and treating all curves without classification will result in errors in RUL prediction. The K‐means method is used to partition degradation paths into discrete states and the obtained HI curves are grouped by their initial states and then trained separately. A hybrid prediction model combining Long Short‐Term Memory (LSTM) network and Support Vector Regression (SVR) is developed to predict the RUL. The dataset of turbofan engines is used to verify the proposed method. The results show that the proposed method performs better than many existing methods.

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Section: Multi-sensor Data Fusion Modelmentioning

confidence: 99%

“…If the number of sensors or the number of samples is large, the computational complexity is high. Here, genetic algorithm (GA) is applied to find the optimal weight coefficients 37 . The detailed procedure is presented in Figure 4.…”

Section: Rul Prediction Frameworkmentioning

confidence: 99%

Remaining useful life prediction using composite health index and hybrid LSTM‐SVR model

Zheng

Zhou

et al. 2022

Quality & Reliability Eng

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“…In the literature, Genetic algorithm has been successfully implemented in different situations. Readers may refer to Faraz [22], Liu et al [28], Parkinson [32], Yang et al [36]. In this work, we exploit a version of non-dominated sorting GA called NSGA-II proposed by Deb et al [16].…”

Section: Dutta and Kayalmentioning

confidence: 99%

Robust estimation of dependent competing risk model under interval monitoring and determining optimal inspection intervals

Mondal¹,

Baghel²

2022

Preprint

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Recently, a growing amount interest is quite evident in modelling dependent competing risks in life time prognosis problem. In this work, we propose to model the dependent competing risks by Marshal-Olkin bivariate exponential distribution. The observable data consists of number of failures due to different causes across different time intervals. The failure count data is common in instances like one shot devices where state of the subjects are inspected at different inspection times rather than the exact failure times. The point estimation of the life time distribution in presence of competing risk has been studied through divergence based robust estimation method called minimum density power divergence estimation (MDPDE). The testing of hypothesis is performed based on a Wald type test statistic. The influence function is derived both for the point estimator and the test statistic, which reflects the degree of robustness. Another, key contribution of this work is to determine the optimal set of inspection times based on some predefined objectives. This article presents determination of multi criteria based optimal design. Population based heuristic algorithm non-dominated sorting-based multiobjective Genetic algorithm is exploited to solve this optimization problem.

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“…By means of the fault prognosis technique, the faulty component can be replaced at an appropriate time according to the predicted RUL, thereby avoiding premature component replacement waste and production accidents. Over recent years, the issue of fault prognosis has garnered considerable attention and yielded fruitful results in many areas, such as mechanical, electrical and electronic fields 2–6 …”

Section: Introductionmentioning

confidence: 99%

Fault prognosis for linear stochastic systems with intermittent fault and strong noise via health indicator extraction approach

Huai,

Gao,

Liu

et al. 2024

Quality & Reliability Eng

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In this paper, the problem of fault prognosis is investigated for linear stochastic systems with intermittent faults (IFs) and strong noise. Different from permanent faults, the degradation state of IFs are related to the fault duration time, leading to difficulties in the first step of fault prognosis, that is, the health indicator (HI) extraction. Generally, HI extraction of IFs is achieved by fault detection techniques. Therefore, the proposed fault prognosis approach mainly covers the following steps. First, the HI and degradation model are introduced for linear stochastic systems with IFs. Then, considering dynamic characteristic of systems and influence of strong noise, an IF detection algorithm under strong noise is proposed using weighted moving average methods to extract the HI. Moreover, an alternative HI extraction approach is given by an HI estimation way, which avoids using fault detection techniques. Subsequently, the degradation model is fitted by virtue of HIs and the least square method, and the remaining useful life is predicted with the aid of the obtained model. Finally, an experiment concerning the rotary steerable drilling tool system is provided to verify the effectiveness of the derived results.

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Remaining useful life prediction via long‐short time memory neural network with novel partial least squares and genetic algorithm

Cited by 12 publications

References 24 publications

Remaining useful life prediction using composite health index and hybrid LSTM‐SVR model

Remaining useful life prediction using composite health index and hybrid LSTM‐SVR model

Robust estimation of dependent competing risk model under interval monitoring and determining optimal inspection intervals

Fault prognosis for linear stochastic systems with intermittent fault and strong noise via health indicator extraction approach

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