Data alignments in machinery remaining useful life prediction using deep adversarial neural networks

Li, Xiang; Zhang, Wei; Ma, Hui; Zhang, Luo; Xu, Li

doi:10.1016/j.knosys.2020.105843

Cited by 128 publications

(48 citation statements)

References 41 publications

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“…They demonstrated that this model provides satisfactory performance for the RUL prediction of turbofan engines. Li et al [ 42 ] applied the generative adversarial network (GAN) algorithm to compute the distribution of the healthy state data and proposed a health indicator. Promising results were achieved on two rotating machinery datasets.…”

Section: Background and Related Workmentioning

confidence: 99%

Remaining Useful Life (RUL) Prediction of Equipment in Production Lines Using Artificial Neural Networks

Kang

Çatal

Tekinerdoğan

2021

Sensors

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Predictive maintenance of production lines is important to early detect possible defects and thus identify and apply the required maintenance activities to avoid possible breakdowns. An important concern in predictive maintenance is the prediction of remaining useful life (RUL), which is an estimate of the number of remaining years that a component in a production line is estimated to be able to function in accordance with its intended purpose before warranting replacement. In this study, we propose a novel machine learning-based approach for automating the prediction of the failure of equipment in continuous production lines. The proposed model applies normalization and principle component analysis during the pre-processing stage, utilizes interpolation, uses grid search for parameter optimization, and is built with multilayer perceptron neural network (MLP) machine learning algorithm. We have evaluated the approach using a case study research to predict the RUL of engines on NASA turbo engine datasets. Experimental results demonstrate that the performance of our proposed model is effective in predicting the RUL of turbo engines and likewise substantially enhances predictive maintenance results.

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Section: Background and Related Workmentioning

confidence: 99%

Remaining Useful Life (RUL) Prediction of Equipment in Production Lines Using Artificial Neural Networks

Kang

Çatal

Tekinerdoğan

2021

Sensors

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“…It also makes model evaluation problematic. The reason is that the RUL estimation error decreases towards the EoL event (see for example (Li et al, Peng, Zi, Jin, & Tsui, 2015;Li, Zhang, Ma, Luo, & Li, 2020;Xia et al, 2019)). So longer signals usually result in larger errors.…”

Section: Benchmark Data Setsmentioning

confidence: 99%

“…Model accuracy also varies significantly during a component's lifespan (Saxena et al, 2010) -it is low at the start and increases towards the EoL (see for example (Li et al, 2019;Y. Wang et al, 2015;Li et al, 2020;Xia et al, 2019)). Finally, the error of an early or late RUL should reflect the higher cost associated with delayed maintenance interventions.…”

Section: Model Evaluationmentioning

confidence: 99%

Remaining Useful Life Estimation of Bearings

Ferreira

Sousa

2021

IJPHM

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In the domain of predictive maintenance, when trying to repli- cate and compare research in remaining useful life estimation (RUL), several inconsistencies and errors were identified in the experimental methodology used by various researchers. This makes the replication and the comparison of results diffi- cult, thus severely hindering both progress in this research do- main and its practical application to industry. We survey the literature to evaluate the experimental procedures that were used, and identify the most common errors and omission in both experimental procedures and reporting. A total of 70 papers on RUL were audited. From this meta- analysis we estimate that approximately 11% of the papers present work that will allow for replication and comparison. Surprisingly, only about 24.3% (17 of the 70 articles) com- pared their results with previous work. Of the remaining work, 41.4% generated and compared several models of their own and, somewhat unsettling, 31.4% of the researchers made no comparison whatsoever. The remaining 2.9% did not use the same data set for comparisons. The results of this study were also aggregated into 3 categories: problem class selec- tion, model fitting best practices and evaluation best practices. We conclude that model evaluation is the most problematic one. The main contribution of the article is a proposal of an ex- perimental protocol and several recommendations that specif- ically target model evaluation. Adherence to this protocol should substantially facilitate the research and application of RUL prediction models. The goals are to promote the collab- oration between scholars and practitioners alike and advance the research in this domain.

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“…To minimize data, the distribution discrepancy of the feature space between sample and target domain, transferable convolution neural network (TCNN) and Generative adversarial networks (GAN) are proposed to predict the RUL 40,41 …”

Section: Related Workmentioning

confidence: 99%

Using temporal convolution network for remaining useful lifetime prediction

Chen

Liu

2020

Engineering Reports

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Because Convolutional Neural Network (CNN) can extract spatial feature, while Long Short‐Term Memory (LSTM) can learn temporal features, many methods combining CNN with LSTM are proposed for remaining useful lifetime prediction. In practice, it is better to learn temporal features from long history sequence data because of the slow inherently long‐term degradation process. However, LSTM is less efficient in processing long history sequence. To solve this problem, in this work, a temporal convolution network combining causal filters with dilated convolutions is used to expand the receptive field length of network. The network structure can be fixed through three key parameters, and the size of time window adopted for time sequence processing is the same as the receptive field length. These two characteristics allow the network to easily be applied for engineering purposes. The method is tested and evaluated using two well‐known datasets, namely the “Turbofan Engine Degradation Simulation Dataset C‐MAPSS” and “Milling Dataset.” The performance analysis shows that the proposed method outperforms more classical methods in terms of prediction accuracy.

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Data alignments in machinery remaining useful life prediction using deep adversarial neural networks

Cited by 128 publications

References 41 publications

Remaining Useful Life (RUL) Prediction of Equipment in Production Lines Using Artificial Neural Networks

Remaining Useful Life (RUL) Prediction of Equipment in Production Lines Using Artificial Neural Networks

Remaining Useful Life Estimation of Bearings

Using temporal convolution network for remaining useful lifetime prediction

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