scholarly journals Remaining useful life prediction with insufficient degradation data based on deep learning approach

2021 ◽  
Vol 23 (4) ◽  
pp. 745-756
Author(s):  
Yi Lyu ◽  
Yijie Jiang ◽  
Qichen Zhang ◽  
Ci Chen
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Time Window ◽  
Synthetic Data ◽  
Remaining Useful Life ◽  
Generative Adversarial Network ◽  
Data Set ◽  
Adversarial Network ◽  
Degradation Data ◽  
Useful Life

Remaining useful life (RUL) prediction plays a crucial role in decision-making in conditionbased maintenance for preventing catastrophic field failure. For degradation-failed products, the data of performance deterioration process are the key for lifetime estimation. Deep learning has been proved to have excellent performance in RUL prediction given that the degradation data are sufficiently large. However, in some applications, the degradation data are insufficient, under which how to improve the prediction accuracy is yet a challenging problem. To tackle such a challenge, we propose a novel deep learning-based RUL prediction framework by amplifying the degradation dataset. Specifically, we leverage the cycle-consistent generative adversarial network to generate the synthetic data, based on which the original degradation dataset is amplified so that the data characteristics hidden in the sample space could be captured. Moreover, the sliding time window strategy and deep bidirectional long short-term memory network are employed to complete the RUL prediction framework. We show the effectiveness of the proposed method by running it on the turbine engine data set from the National Aeronautics and Space Administration. The comparative experiments show that our method outperforms a case without the use of the synthetically generated data.

scholarly journals Deep learning-based anomaly-onset aware remaining useful life estimation of bearings

2021 ◽  
Vol 7 ◽  
pp. e795
Author(s):  
Pooja Vinayak Kamat ◽  
Rekha Sugandhi ◽  
Satish Kumar
Keyword(s):  
Deep Learning ◽  
Anomaly Detection ◽  
Short Term Memory ◽  
Rotating Machinery ◽  
Remaining Useful Life ◽  
Operating Conditions ◽  
Learning Models ◽  
Vibration Data ◽  
Degradation Data ◽  
Useful Life

Remaining Useful Life (RUL) estimation of rotating machinery based on their degradation data is vital for machine supervisors. Deep learning models are effective and popular methods for forecasting when rotating machinery such as bearings may malfunction and ultimately break down. During healthy functioning of the machinery, however, RUL is ill-defined. To address this issue, this study recommends using anomaly monitoring during both RUL estimator training and operation. Essential time-domain data is extracted from the raw bearing vibration data, and deep learning models are used to detect the onset of the anomaly. This further acts as a trigger for data-driven RUL estimation. The study employs an unsupervised clustering approach for anomaly trend analysis and a semi-supervised method for anomaly detection and RUL estimation. The novel combined deep learning-based anomaly-onset aware RUL estimation framework showed enhanced results on the benchmarked PRONOSTIA bearings dataset under non-varying operating conditions. The framework consisting of Autoencoder and Long Short Term Memory variants achieved an accuracy of over 90% in anomaly detection and RUL prediction. In the future, the framework can be deployed under varying operational situations using the transfer learning approach.

scholarly journals Remaining Useful Life Estimation Using Deep Convolutional Generative Adversarial Networks Based on an Autoencoder Scheme

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Guisheng Hou ◽  
Shuo Xu ◽  
Nan Zhou ◽  
Lei Yang ◽  
Quanhao Fu
Keyword(s):  
Feature Extraction ◽  
Short Term Memory ◽  
Health Management ◽  
Remaining Useful Life ◽  
Fine Tuning ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Useful Life ◽  
Prediction Approach

Accurate predictions of remaining useful life (RUL) of important components play a crucial role in system reliability, which is the basis of prognostics and health management (PHM). This paper proposed an integrated deep learning approach for RUL prediction of a turbofan engine by integrating an autoencoder (AE) with a deep convolutional generative adversarial network (DCGAN). In the pretraining stage, the reconstructed data of the AE not only participate in its error reconstruction but also take part in the DCGAN parameter training as the generated data of the DCGAN. Through double-error reconstructions, the capability of feature extraction is enhanced, and high-level abstract information is obtained. In the fine-tuning stage, a long short-term memory (LSTM) network is used to extract the sequential information from the features to predict the RUL. The effectiveness of the proposed scheme is verified on the NASA commercial modular aero-propulsion system simulation (C-MAPSS) dataset. The superiority of the proposed method is demonstrated via excellent prediction performance and comparisons with other existing state-of-the-art prognostics. The results of this study suggest that the proposed data-driven prognostic method offers a new and promising prediction approach and an efficient feature extraction scheme.

Prognostics and RUL Estimations of SAC305, SAC105 and SnAg Solders Under Temperature and Vibration Using Long Short-Term Memory (LSTM) Deep Learning

2021 ◽  
Author(s):  
Pradeep Lall ◽  
Tony Thomas ◽  
Ken Blecker
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Short Term Memory ◽  
Remaining Useful Life ◽  
Operating Conditions ◽  
Short Term ◽  
Term Memory ◽  
Rectangular Pattern ◽  
Useful Life ◽  
Long Short Term Memory

Abstract Prognostics and Remaining Useful Life (RUL) estimations of complex systems are essential to operational safety, increased efficiency, and help to schedule maintenance proactively. Modeling the remaining useful life of a system with many complexities is possible with the rapid development in the field of deep learning as a computational technique for failure prediction. Deep learning can adapt to multivariate parameters complex and nonlinear behavior, which is difficult using traditional time-series models for forecasting and prediction purposes. In this paper, a deep learning approach based on Long Short-Term Memory (LSTM) network is used to predict the remaining useful life of the PCB at different conditions of temperature and vibration. This technique can identify the different underlying patterns in the time series that can predict the RUL. This study involves feature vector identification and RUL estimations for SAC305, SAC105, and Tin Lead solder PCBs under different vibration levels and temperature conditions. The acceleration levels of vibration are fixed at 5g and 10g, while the temperature levels are 55°C and 100°C. The test board is a multilayer FR4 configuration with JEDEC standard dimensions consists of twelve packages arranged in a rectangular pattern. Strain signals are acquired from the backside of the PCB at symmetric locations to identify the failure of all the packages during vibration. The strain signals are resistance values that are acquired simultaneously during the experiment until the failure of most of the packages on the board. The feature vectors are identified from statistical analysis on the strain signals frequency and instantaneous frequency components. The principal component analysis is used as a data reduction technique to identify the different patterns produced from the four strain signals with failures of the packages during vibration. LSTM deep learning method is used to model the RUL of the packages at different individual operating conditions of vibration for all three solder materials involved in this study. A combined model for RUL prediction for a material that can take care of the changes in the operating conditions is also modeled for each material.

scholarly journals A novel deep capsule neural network for remaining useful life estimation

2019 ◽  
Vol 234 (1) ◽  
pp. 151-167 ◽  
Author(s):  
Andrés Ruiz-Tagle Palazuelos ◽  
Enrique López Droguett ◽  
Rodrigo Pascual
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Computation Time ◽  
Remaining Useful Life ◽  
Sensor Data ◽  
Fault Diagnostics ◽  
Data Set ◽  
Life Estimation ◽  
Useful Life ◽  
Fault Prognostics

With the availability of cheaper multi-sensor systems, one has access to massive and multi-dimensional sensor data for fault diagnostics and prognostics. However, from a time, engineering and computational perspective, it is often cost prohibitive to manually extract useful features and to label all the data. To address these challenges, deep learning techniques have been used in the recent years. Within these, convolutional neural networks have shown remarkable performance in fault diagnostics and prognostics. However, this model present limitations from a prognostics and health management perspective: to improve its feature extraction generalization capabilities and reduce computation time, ill-based pooling operations are employed, which require sub-sampling of the data, thus loosing potentially valuable information regarding an asset’s degradation process. Capsule neural networks have been recently proposed to address these problems with strong results in computer vision–related classification tasks. This has motivated us to extend capsule neural networks for fault prognostics and, in particular, remaining useful life estimation. The proposed model, architecture and algorithm are tested and compared to other state-of-the art deep learning models on the benchmark Commercial Modular Aero Propulsion System Simulation turbofans data set. The results indicate that the proposed capsule neural networks are a promising approach for remaining useful life prognostics from multi-dimensional sensor data.

Remaining Useful Life Estimation and Prognostication of SAC305 PCB's for Dynamic Conditions of Temperature and Vibration Loads

2021 ◽  
Author(s):  
Pradeep Lall ◽  
Tony Thomas ◽  
Ken Blecker
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Data ◽  
Short Term Memory ◽  
Multivariate Time Series ◽  
Remaining Useful Life ◽  
Series Data ◽  
Sac305 Solder ◽  
Feature Vectors ◽  
Useful Life

Abstract This study focuses on the feature vector identification and Remaining Useful Life (RUL) estimation of SAC305 solder alloy PCB's of two different configurations during varying conditions of temperature and vibration. The feature vectors are identified using the strain signals acquired from four symmetrical locations of the PCB at regular intervals during vibration. Two different types of experiments are employed to characterize the PCB's dynamic changes with varying temperature and acceleration levels. The strain signals acquired during each of these experiments are compared based on both time and frequency domain characteristics. Different statistical and frequency-based techniques were used to identify the strain signal variations with changes in the environment and loading conditions. The feature vectors in predicting failure at a constant working temperature and load were identified, and as an extension to this work, the effectiveness of the feature vectors during varying conditions of temperature and acceleration levels are investigated. The remaining Useful Life of the packages was estimated using a deep learning approach based on Long Short Term Memory (LSTM) network. This technique can identify the underlying patterns in multivariate time series data that can predict the packages' life. The autocorrelation function's residuals were used as the multivariate time series data in conjunction with the LSTM deep learning technique to forecast the packages' life at different varying temperatures and acceleration levels during vibration.

scholarly journals Synthetic image data augmentation for fibre layup inspection processes: Techniques to enhance the data set

2021 ◽  
Author(s):  
Sebastian Meister ◽  
Nantwin Möller ◽  
Jan Stüve ◽  
Roger M. Groves
Keyword(s):  
Deep Learning ◽  
Test Method ◽  
Process Efficiency ◽  
Synthetic Image ◽  
Generative Adversarial Network ◽  
Data Set ◽  
Manufacturing Defects ◽  
Fiber Placement ◽  
Adversarial Network ◽  
Geometrical Transformation

AbstractIn the aerospace industry, the Automated Fiber Placement process is an established method for producing composite parts. Nowadays the required visual inspection, subsequent to this process, typically takes up to 50% of the total manufacturing time and the inspection quality strongly depends on the inspector. A Deep Learning based classification of manufacturing defects is a possibility to improve the process efficiency and accuracy. However, these techniques require several hundreds or thousands of training data samples. Acquiring this huge amount of data is difficult and time consuming in a real world manufacturing process. Thus, an approach for augmenting a smaller number of defect images for the training of a neural network classifier is presented. Five traditional methods and eight deep learning approaches are theoretically assessed according to the literature. The selected conditional Deep Convolutional Generative Adversarial Network and Geometrical Transformation techniques are investigated in detail, with regard to the diversity and realism of the synthetic images. Between 22 and 166 laser line scan sensor images per defect class from six common fiber placement inspection cases are utilised for tests. The GAN-Train GAN-Test method was applied for the validation. The studies demonstrated that a conditional Deep Convolutional Generative Adversarial Network combined with a previous Geometrical Transformation is well suited to generate a large realistic data set from less than 50 actual input images. The presented network architecture and the associated training weights can serve as a basis for applying the demonstrated approach to other fibre layup inspection images.

scholarly journals RUL Estimation Enhancement using Hybrid Deep Learning Methods

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Ikram Remadna ◽  
Labib Sadek Terrissa ◽  
Soheyb Ayad ◽  
Nourddine Zerhouni
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Hybrid Methods ◽  
Remaining Useful Life ◽  
Health State ◽  
Learning Methods ◽  
Turbofan Engines ◽  
Learning Techniques ◽  
Useful Life ◽  
Behavior Health

The turbofan engine is one of the most critical aircraft components. Its failure may introduce unwanted downtime, expensive repair, and affect safety performance. Therefore, It is essential to accurately detect upcoming failures by predicting the future behavior health state of turbofan engines as well as its Remaining Useful Life. The use of deep learning techniques to estimate Remaining Useful Life has seen a growing interest over the last decade. However, hybrid deep learning methods have not been sufficiently explored yet by researchers.In this paper, we proposed two-hybrid methods combining Convolutional Auto-encoder (CAE), Bi-directional Gated Recurrent Unit (BDGRU), Bi-directional Long-Short Term Memory (BDLSTM), and Convolutional Neural Network (CNN) to enhance the RUL estimation. The results indicate that the hybrid methods exhibit the most reliable RUL prediction accuracy and significantly outperform the most robust predictions in the literature.

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