Convolution and Long Short-Term Memory Hybrid Deep Neural Networks for Remaining Useful Life Prognostics

Kong, Zhengmin; Cui, Yande; Zhou, Xia; Lv, He

doi:10.3390/app9194156

Cited by 105 publications

(42 citation statements)

References 51 publications

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“…• Neural Networks (NN) (Chryssolouris, 2006;Chen et al, 2019) • Deep Neural Networks (DNN) (Zhao et al, 2017) • Convolutional Neural Networks (CNN) (Li et al, 2018;Mourtzis et al, 2020a) • Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) (Zhao et al, 2017) • Gated Recurrent Units (GRU) (Chen et al, 2019) • Recurrent Neural Network (CNN-RNN) (Banerjee et al, 2019) • Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM) (Kong et al, 2019) • Convolutional Neural Network and Gated Recurrent Unit (CNN-GRU) (Lei et al, 2018) As Industry 4.0 continues to evolve, many companies are struggling with the realities of AI implementation. Indeed, the benefits of PdM such as helping determine the condition of equipment and predicting when maintenance should be performed, are extremely strategic.…”

Section: Machine Learningmentioning

confidence: 99%

Intelligent Predictive Maintenance and Remote Monitoring Framework for Industrial Equipment Based on Mixed Reality

2020

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The currently applied maintenance strategies, including Reactive and Preventive maintenance can be considered obsolete. The constant improvements in Information and Communication Technologies as well as in Digital Technologies along with the increase of computational power, have facilitated the development of new Artificial Intelligence algorithms to integrate cognition in computational systems. This trend is posing a great challenge for engineers, as such developments will enable the creation of robust systems that can monitor the current status of the machines and by extension to predict unforeseeable situations. Furthermore, Smart Computers will be capable of examining all possible scenarios and suggest viable solutions in a fraction of time compared to humans. Therefore, in this paper, the modelling, design and development of a Predictive Maintenance and Remote Monitoring system are proposed, based on the utilization of Artificial Intelligence algorithms for data acquisition, fusion, and post-processing. In addition to that, the proposed framework will integrate a Mixed Reality application for the intuitive visualization of the data, that will ultimately facilitate production and maintenance engineers to monitor the condition of the machines, and most importantly to get an accurate prediction of the oncoming failures.

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Section: Machine Learningmentioning

confidence: 99%

Intelligent Predictive Maintenance and Remote Monitoring Framework for Industrial Equipment Based on Mixed Reality

2020

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“…Multiple deep learning algorithms have been used to generate data-driven models to predict RUL for C-MAPSS aircraft gas turbine engines data. It can be observed from the literatures [12][13][14][15][16][17][18][19][20] that the most suitable deep learning algorithms for training the high accuracy C-MAPSS models is the Long-Short Term Memory Recurrent Neural Network (LSTM). The hybrid deep neural network layers with LSTM is also an ongoing investigation and experiment on the C-MAPSS dataset.…”

Section: Related Workmentioning

confidence: 99%

“…They employed CNN as part of the network layers in their experiment and proposed the hybrid models by combining the CNN layers with LSTM layers. Their approaches have proven to achieve highest accuracy over the other standard methods [17]. Other works previously published [18][19][20] mostly focused on adopting the LSTM network and proposing new models without addressing the complexity reduction in their approaches.…”

Section: Related Workmentioning

confidence: 99%

Deep Neural Network Feature Selection Approaches for Data-Driven Prognostic Model of Aircraft Engines

2020

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Predicting Remaining Useful Life (RUL) of systems has played an important role in various fields of reliability engineering analysis, including in aircraft engines. RUL prediction is critically an important part of Prognostics and Health Management (PHM), which is the reliability science that is aimed at increasing the reliability of the system and, in turn, reducing the maintenance cost. The majority of the PHM models proposed during the past few years have shown a significant increase in the amount of data-driven deployments. While more complex data-driven models are often associated with higher accuracy, there is a corresponding need to reduce model complexity. One possible way to reduce the complexity of the model is to use the features (attributes or variables) selection and dimensionality reduction methods prior to the model training process. In this work, the effectiveness of multiple filter and wrapper feature selection methods (correlation analysis, relief forward/backward selection, and others), along with Principal Component Analysis (PCA) as a dimensionality reduction method, was investigated. A basis algorithm of deep learning, Feedforward Artificial Neural Network (FFNN), was used as a benchmark modeling algorithm. All those approaches can also be applied to the prognostics of an aircraft gas turbine engines. In this paper, the aircraft gas turbine engines data from NASA Ames prognostics data repository was used to test the effectiveness of the filter and wrapper feature selection methods not only for the vanilla FFNN model but also for Deep Neural Network (DNN) model. The findings show that applying feature selection methods helps to improve overall model accuracy and significantly reduced the complexity of the models.

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“…The outlet pressure signal of the airborne fuel pump is a set of one-dimensional data, which has certain correlation in time dimension [28]. To make full use of data sequence correlation, this reasearch employs the method of data space reconstruction [29], and the one-dimensional data sequence is converted into the following matrix: Then multi-step prediction can be conducted using single point iteration method:…”

Section: ) Remaining Useful Life Predictionmentioning

confidence: 99%

Degradation Pattern Identification and Remaining Useful Life Prediction for Mechanical Equipment Using SKF-EN

et al. 2020

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Due to the influence of working environment and stress, the failure degradation process of mechanical equipment is characterized by non-stationarity and uncertainty. The degradation pattern of a large number of mechanical equipment obeys the process from steady degradation to accelerated degradation. The rotating machinery wear results in gradual degradation in the early stage while rough contact surface and spall of equipment leads to accelerated degradation stage. However, the change-point of the degradation patterns are uncertain which leads to the ambiguity of the degradation pattern transition and uncertainty of the remaining life prediction model. Aiming at the problems of ambiguity of the pattern transition and uncertainty of the prediction model, the SKF-EN method is proposed in this paper. In this method the real-time data samples are processed through Switching Kalman filters, and the current degradation pattern is identified based on the posterior probability between the two filters of steady degradation and accelerated degradation. When the filters confidence indicates the entering of accelerated degradation pattern, the Elastic Net is used to model the degradation trajectory and predict its life in real time. The method was verified with an airborne fuel pump and rolling element bearings, and it realized the real-time identification of the degradation pattern and effective prediction of the remaining useful life.

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Convolution and Long Short-Term Memory Hybrid Deep Neural Networks for Remaining Useful Life Prognostics

Cited by 105 publications

References 51 publications

Intelligent Predictive Maintenance and Remote Monitoring Framework for Industrial Equipment Based on Mixed Reality

Intelligent Predictive Maintenance and Remote Monitoring Framework for Industrial Equipment Based on Mixed Reality

Deep Neural Network Feature Selection Approaches for Data-Driven Prognostic Model of Aircraft Engines

Degradation Pattern Identification and Remaining Useful Life Prediction for Mechanical Equipment Using SKF-EN

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