A neural network-evolutionary computational framework for remaining useful life estimation of mechanical systems

Chen, Zhaoyin; Schütze, Oliver; Sun, Jian‐Qiao

doi:10.1016/j.neunet.2019.04.016

Cited by 47 publications

(13 citation statements)

References 25 publications

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“…ANNs represent one of the most popular and highly effective data‐driven approaches for diagnostics 95,104 and prognostics 105‐108 in engineered systems. DL models, as a branch of ML, which is based on ANN with representation learning, are also applied for diagnostics and prognostics purposes 109,110 .…”

Section: Diagnostics and Prognostics Approachesmentioning

confidence: 99%

“…Disadvantages include its black box nature (i.e., unexplained behavior of the network), greater computational burden, the difficulty of showing the problem to the network, risk of overfitting, and the experimental characteristics of model development 53,91 . The features of ANN, particularly, its capability to implicitly detect complex nonlinear relationships between dependent and independent variables, make it a suitable method for diagnostics and prognostics analyses of complex systems 104,106‐108 . However, the adaptive forms of ANN are widely used for complex systems’ applications, which are discussed in Section 3.2.…”

Section: Critical Review Of Diagnostic and Prognostics Applications To Complex Systemsmentioning

confidence: 99%

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Diagnostics and prognostics for complex systems: A review of methods and challenges

Soleimani

Campean

Neagu

2021

Quality & Reliability Eng

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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.

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Section: Diagnostics and Prognostics Approachesmentioning

confidence: 99%

Section: Critical Review Of Diagnostic and Prognostics Applications To Complex Systemsmentioning

confidence: 99%

Diagnostics and prognostics for complex systems: A review of methods and challenges

Soleimani

Campean

Neagu

2021

Quality & Reliability Eng

View full text Add to dashboard Cite

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“…Long Short-Term Memory (LSTM) networks are a substantial branch of Recurrent Neural Networks (RNN), capable of learning long-term territories (Laredo, Chen, Schütze, & Sun, 2019;Wu, Yuan, Dong, Lin, & Liu, 2018). LSTM is a model approach to solved some forecast issues.…”

Section: Literature Reviewmentioning

confidence: 99%

Time Series Forecasting for Average Temperature in 96041 Station Using Long Short-Term Memory Model

Damanik

Pane

Dewi

et al. 2021

jmika

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An understanding of patterns and gauge of normal temperature joined of parameter climate and climate information for way better water asset administration and arranging during a bowl is exceptionally vital. Investigate climate patterns utilizing typical and neighborhood annually normal temperatures, compare and make perceptions. during this consider, we'll analyze nearby and ordinary normal temperature information in 96041 Station supported perception station in place. the foremost objective of this considers to seem the execution of the traditional temperature in an exceedingly single station and to foresee the conventional temperature information utilizing the Long memory Demonstrate approach. supported the results of ordinary informatics of investigating temperature with nearby temperature relationship, we got the show of preparing bend, remaining plot, and therefore the diffuse plot is appeared utilizing these codes. the nice execution of 96041 Station had an Mean Squared Error esteem of 0.01 and R squared esteem 0.98, concerning zero will speak to superior quality of the indicator.

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“…A method that uses deep learning tools and curve matching technology is proposed to estimate the robustness of the system [26]. A framework for estimating the RUL of mechanical systems is proposed, which is composed of the multi-layer perceptron and multilayer perceptron and evolutionary algorithm for optimizing parameters [27]. Besides, there are many other machine learning algorithms, such as neural networks [28]- [30], capsule neural networks [31], dynamic Bayesian networks [32] and so on.…”

Section: New Faultmentioning

confidence: 99%

An Improved PF Remaining Useful Life Prediction Method Based on Quantum Genetics and LSTM

Yang

Sun

2019

IEEE Access

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Remaining useful life (RUL) is the premise and basis of the equipment health management plan. As accurate as possible life prediction is of great significance to reliability and economy of equipment maintenance. In this paper, a data-driven improved particle filter (PF) RUL prediction method is proposed. A health indicator extraction method based on multi-feature fusion is introduced for the RUL prediction, which can visually show the degradation trend of the healthy state of the equipment. The degradation model and observation model of equipment health indicators are established, and the PF algorithm is used to track parameters of the model. A quantum genetic algorithm is employed to improve the problem of particle degradation in PF. On the basis of filter tracking, long short term memory (LSTM) network is used to predict the trend of model coefficients, which further improves the accuracy of RUL prediction. The experiment using the C-MAPSS data set shows the proposed method has a better prediction accuracy than other methods. INDEX TERMS Remaining useful life, particle filter, quantum genetic algorithm, long short term memory.

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A neural network-evolutionary computational framework for remaining useful life estimation of mechanical systems

Cited by 47 publications

References 25 publications

Diagnostics and prognostics for complex systems: A review of methods and challenges

Diagnostics and prognostics for complex systems: A review of methods and challenges

Time Series Forecasting for Average Temperature in 96041 Station Using Long Short-Term Memory Model

An Improved PF Remaining Useful Life Prediction Method Based on Quantum Genetics and LSTM

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