scholarly journals An Autoencoder Gated Recurrent Unit for Remaining Useful Life Prediction

Processes ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1155
Author(s):  
Yi-Wei Lu ◽  
Chia-Yu Hsu ◽  
Kuang-Chieh Huang
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Short Term Memory ◽  
Development Trend ◽  
Production Equipment ◽  
Remaining Useful Life ◽  
Sensor Data ◽  
Smart Manufacturing ◽  
Useful Life ◽  
Gated Recurrent Unit

With the development of smart manufacturing, in order to detect abnormal conditions of the equipment, a large number of sensors have been used to record the variables associated with production equipment. This study focuses on the prediction of Remaining Useful Life (RUL). RUL prediction is part of predictive maintenance, which uses the development trend of the machine to predict when the machine will malfunction. High accuracy of RUL prediction not only reduces the consumption of manpower and materials, but also reduces the need for future maintenance. This study focuses on detecting faults as early as possible, before the machine needs to be replaced or repaired, to ensure the reliability of the system. It is difficult to extract meaningful features from sensor data directly. This study proposes a model based on an Autoencoder Gated Recurrent Unit (AE-GRU), in which the Autoencoder (AE) extracts the important features from the raw data and the Gated Recurrent Unit (GRU) selects the information from the sequences to forecast RUL. To evaluate the performance of the proposed AE-GRU model, an aircraft turbofan engine degradation simulation dataset provided by NASA was used and a comparison made of different recurrent neural networks. The results demonstrate that the AE-GRU is better than other recurrent neural networks, such as Long Short-Term Memory (LSTM) and GRU.

scholarly journals Predicting Remaining Useful Life using Time Series Embeddings based on Recurrent Neural Networks

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Narendhar Gugulothu ◽  
Vishnu TV ◽  
Pankaj Malhotra ◽  
Lovekesh Vig ◽  
Puneet Agarwal ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Recurrent Neural Networks ◽  
Missing Values ◽  
Multivariate Time Series ◽  
Remaining Useful Life ◽  
Sensor Data ◽  
Novel Approach ◽  
Useful Life ◽  
Robust To Noise

We consider the problem of estimating the remaining useful life (RUL) of a system or a machine from sensor data. Many approaches for RUL estimation based on sensor data make assumptions about how machines degrade. Additionally, sensor data from machines is noisy and often suffers from missing values in many practical settings. We propose Embed-RUL: a novel approach for RUL estimation from sensor data that does not rely on any degradation-trend assumptions, is robust to noise, and handles missing values. Embed-RUL utilizes a sequence-to-sequence model based on Recurrent Neural Networks (RNNs) to generate embeddings for multivariate time series subsequences. The embeddings for normal and degraded machines tend to be different, and are therefore found to be useful for RUL estimation. We show that the embeddings capture the overall pattern in the time series while filtering out the noise, so that the embeddings of two machines with similar operational behavior are close to each other, even when their sensor readings have significant and varying levels of noise content. We perform experiments on publicly available turbofan engine dataset and a proprietary real-world dataset, and demonstrate that Embed-RUL outperforms the previously reported state-of-the-art (Malhotra, TV, et al., 2016) on several metrics.

scholarly journals Transfer Learning with Deep Recurrent Neural Networks for Remaining Useful Life Estimation

2018 ◽  
Vol 8 (12) ◽  
pp. 2416 ◽  
Author(s):  
Ansi Zhang ◽  
Honglei Wang ◽  
Shaobo Li ◽  
Yuxin Cui ◽  
Zhonghao Liu ◽  
...  
Keyword(s):  
Neural Networks ◽  
Transfer Learning ◽  
Recurrent Neural Networks ◽  
Prediction Models ◽  
Learning Algorithm ◽  
Remaining Useful Life ◽  
Operating Conditions ◽  
Training Data ◽  
Good Prediction ◽  
Useful Life

Prognostics, such as remaining useful life (RUL) prediction, is a crucial task in condition-based maintenance. A major challenge in data-driven prognostics is the difficulty of obtaining a sufficient number of samples of failure progression. However, for traditional machine learning methods and deep neural networks, enough training data is a prerequisite to train good prediction models. In this work, we proposed a transfer learning algorithm based on Bi-directional Long Short-Term Memory (BLSTM) recurrent neural networks for RUL estimation, in which the models can be first trained on different but related datasets and then fine-tuned by the target dataset. Extensive experimental results show that transfer learning can in general improve the prediction models on the dataset with a small number of samples. There is one exception that when transferring from multi-type operating conditions to single operating conditions, transfer learning led to a worse result.

scholarly journals Developing Deep Survival Model for Remaining Useful Life Estimation Based on Convolutional and Long Short-Term Memory Neural Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Chia-Hua Chu ◽  
Chia-Jung Lee ◽  
Hsiang-Yuan Yeh
Keyword(s):  
Neural Networks ◽  
Short Term Memory ◽  
Survival Model ◽  
Remaining Useful Life ◽  
Superior Performance ◽  
Short Term ◽  
Term Memory ◽  
Useful Life ◽  
Long Short Term Memory ◽  
Prediction Approach

The application of mechanical equipment in manufacturing is becoming more and more complicated with technology development and adoption. In order to keep the high reliability and stability of the production line, reducing the downtime to repair and the frequency of routine maintenance is necessary. Since machine and components’ degradations are inevitable, accurately estimating the remaining useful life of them is crucial. We propose an integrated deep learning approach with convolutional neural networks and long short-term memory networks to learn the latent features and estimate remaining useful life value with deep survival model based on the discrete Weibull distribution. We conduct the turbofan engine degradation simulation dataset from Commercial Modular Aero-Propulsion System Simulation dataset provided by NASA to validate our approach. The improved results have proven that our proposed model can capture the degradation trend of a fault and has superior performance under complex conditions compared with existing state-of-the-art methods. Our study provides an efficient feature extraction scheme and offers a promising prediction approach to make better maintenance strategies.

scholarly journals A Novel Remaining Useful Life Prediction Method for Hydrogen Fuel Cells Based on the Gated Recurrent Unit Neural Network

2022 ◽  
Vol 12 (1) ◽  
pp. 432
Author(s):  
Bing Long ◽  
Kunping Wu ◽  
Pengcheng Li ◽  
Meng Li
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fuel Cells ◽  
Prediction Method ◽  
Remaining Useful Life ◽  
Hydrogen Fuel Cells ◽  
Hydrogen Fuel ◽  
Starting Point ◽  
Useful Life ◽  
Gated Recurrent Unit

The remaining useful life (RUL) prediction for hydrogen fuel cells is an important part of its prognostics and health management (PHM). Artificial neural networks (ANNs) are proven to be very effective in RUL prediction, as they do not need to understand the failure mechanisms behind hydrogen fuel cells. A novel RUL prediction method for hydrogen fuel cells based on the gated recurrent unit ANN is proposed in this paper. Firstly, the data were preprocessed to remove outliers and noises. Secondly, the performance of different neural networks is compared, including the back propagation neural network (BPNN), the long short-term memory (LSTM) network and the gated recurrent unit (GRU) network. According to our proposed method based on GRU, the root mean square error was 0.0026, the mean absolute percentage error was 0.0038 and the coefficient of determination was 0.9891 for the data from the challenge datasets provided by FCLAB Research Federation, when the prediction starting point was 650 h. Compared with the other RUL prediction methods based on the BPNN and the LSTM, our prediction method is better in both prediction accuracy and convergence rate.

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.

scholarly journals Noisy Multipath Parallel Hybrid Model for Remaining Useful Life Estimation (NMPM)

2020 ◽  
Vol 12 (1) ◽  
pp. 10
Author(s):  
Ali AlDulaimi ◽  
Arash Mohammadi ◽  
Amir Asif
Keyword(s):  
Neural Networks ◽  
Hybrid Model ◽  
Short Term Memory ◽  
Sequence Data ◽  
Remaining Useful Life ◽  
Life Estimation ◽  
Parallel Path ◽  
Temporal Features ◽  
Parallel Hybrid ◽  
Useful Life

The parallel hybrid models of different deep neural networks architectures are the most promising approaches for remaining useful life (RUL) estimation. In light of that, this paper introduces for the first time in the literature a new parallel hybrid deep neural network (DNN) solution for RUL estimation, named as the Noisy Multipath Parallel Hybrid Model for Remaining Useful Life Estimation (NMPM). The proposed framework comprises of three parallel paths, the first one utilizes a noisy Bidirectional Long-short term memory (BLSTM) that used for extracting temporal features and learning the dependencies of sequence data in two directions, forward and backward, which can benefit completely from the input data. While the second parallel path employs noisy multilayer perceptron (MLP) that consists of three layers to extract different class of features. The third parallel path utilizes noisy convolutional neural networks (CNN) to extract another class of features. The concatenated output of the previous parallel paths is then fed into a noisy fusion center (NFC) to predict the RLU. The NMPM has been trained based on a noisy training to enhance the generalization behavior, as well as strengthen the model accuracy and robustness. The NMPM framework is tested and evaluated by using CMAPSS dataset provided by NASA.

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