scholarly journals A Remaining Useful Life Prognosis of Turbofan Engine Using Temporal and Spatial Feature Fusion

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 418
Author(s):  
Cheng Peng ◽  
Yufeng Chen ◽  
Qing Chen ◽  
Zhaohui Tang ◽  
Lingling Li ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Short Term Memory ◽  
Feature Fusion ◽  
Remaining Useful Life ◽  
Turbofan Engine ◽  
Useful Life ◽  
Temporal And Spatial

The prognosis of the remaining useful life (RUL) of turbofan engine provides an important basis for predictive maintenance and remanufacturing, and plays a major role in reducing failure rate and maintenance costs. The main problem of traditional methods based on the single neural network of shallow machine learning is the RUL prognosis based on single feature extraction, and the prediction accuracy is generally not high, a method for predicting RUL based on the combination of one-dimensional convolutional neural networks with full convolutional layer (1-FCLCNN) and long short-term memory (LSTM) is proposed. In this method, LSTM and 1- FCLCNN are adopted to extract temporal and spatial features of FD001 andFD003 datasets generated by turbofan engine respectively. The fusion of these two kinds of features is for the input of the next convolutional neural networks (CNN) to obtain the target RUL. Compared with the currently popular RUL prediction models, the results show that the model proposed has higher prediction accuracy than other models in RUL prediction. The final evaluation index also shows the effectiveness and superiority of the model.

scholarly journals A Prototype Similarity-based System for Remaining Useful Life Estimation for Future Industry by Singular Spectrum Analysis-Long Short Term Memory Neural Networks Algorithm

2020 ◽  
Author(s):  
Prakit Intachai ◽  
Peerapol Yuvapoositanon
Keyword(s):  
Neural Networks ◽  
Short Term Memory ◽  
Singular Spectrum Analysis ◽  
Remaining Useful Life ◽  
Window Length ◽  
Short Term ◽  
Turbofan Engine ◽  
Term Memory ◽  
Useful Life ◽  
Long Short Term Memory

In this paper, we propose a prototype similarity-based approach of estimating the remaining useful life (RUL) of turbofan engine data using the singular spectrum analysis and the long-short term memory (SSA-LSTM) neural networks algorithm. The algorithm consists of two steps. First, the optimal window length of the trajectory matrix of the dataset is empirically determined from a prototype dataset. Second, the estimation of the RUL of the target datasets is performed using the window length parameter obtained from the first step. The validity of the proposed algorithm is verified by testing with 200 turbofan engine datasets. The results are shown to have a significant improvement in the performance of the RUL estimation over the existing LSTM algorithm.

scholarly journals A Genetic Algorithm Optimized RNN-LSTM Model for Remaining Useful Life Prediction of Turbofan Engine

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 285
Author(s):  
Kwok Tai Chui ◽  
Brij B. Gupta ◽  
Pandian Vasant
Keyword(s):  
Genetic Algorithm ◽  
Feature Extraction ◽  
Prediction Model ◽  
Prediction Models ◽  
Remaining Useful Life ◽  
Prediction Algorithm ◽  
Short Term ◽  
Turbofan Engine ◽  
Term Prediction ◽  
Useful Life

Understanding the remaining useful life (RUL) of equipment is crucial for optimal predictive maintenance (PdM). This addresses the issues of equipment downtime and unnecessary maintenance checks in run-to-failure maintenance and preventive maintenance. Both feature extraction and prediction algorithm have played crucial roles on the performance of RUL prediction models. A benchmark dataset, namely Turbofan Engine Degradation Simulation Dataset, was selected for performance analysis and evaluation. The proposal of the combination of complete ensemble empirical mode decomposition and wavelet packet transform for feature extraction could reduce the average root-mean-square error (RMSE) by 5.14–27.15% compared with six approaches. When it comes to the prediction algorithm, the results of the RUL prediction model could be that the equipment needs to be repaired or replaced within a shorter or a longer period of time. Incorporating this characteristic could enhance the performance of the RUL prediction model. In this paper, we have proposed the RUL prediction algorithm in combination with recurrent neural network (RNN) and long short-term memory (LSTM). The former takes the advantages of short-term prediction whereas the latter manages better in long-term prediction. The weights to combine RNN and LSTM were designed by non-dominated sorting genetic algorithm II (NSGA-II). It achieved average RMSE of 17.2. It improved the RMSE by 6.07–14.72% compared with baseline models, stand-alone RNN, and stand-alone LSTM. Compared with existing works, the RMSE improvement by proposed work is 12.95–39.32%.

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 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 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.

scholarly journals Remaining Useful Life Prediction from 3D Scan Data with Genetically Optimized Convolutional Neural Networks

2021 ◽  
Author(s):  
Giovanni Diraco ◽  
Pietro Siciliano ◽  
Alessandro Leone
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Support Vector Regression ◽  
Transfer Learning ◽  
Convolutional Neural Networks ◽  
Life Prediction ◽  
Point Clouds ◽  
Remaining Useful Life ◽  
Support Vector ◽  
Useful Life

In the current industrial landscape, increasingly pervaded by technological innovations, the adoption of optimized strategies for asset management is becoming a critical key success factor. Among the various strategies available, the “Prognostics and Health Management” strategy is able to support maintenance management decisions more accurately, through continuous monitoring of equipment health and “Remaining Useful Life” forecasting. In the present study, Convolutional Neural Network-based Deep Neural Network techniques are investigated for the Remaining Useful Life prediction of a punch tool, whose degradation is caused by working surface deformations during the machining process. Surface deformation is determined using a 3D scanning sensor capable of returning point clouds with micrometric accuracy during the operation of the punching machine, avoiding both downtime and human intervention. The 3D point clouds thus obtained are transformed into bidimensional image-type maps, i.e., maps of depths and normal vectors, to fully exploit the potential of convolutional neural networks for extracting features. Such maps are then processed by comparing 15 genetically optimized architectures with the transfer learning of 19 pre-trained models, using a classic machine learning approach, i.e., Support Vector Regression, as a benchmark. The achieved results clearly show that, in this specific case, optimized architectures provide performance far superior (MAPE=0.058) to that of transfer learning which, instead, remains at a lower or slightly higher level (MAPE=0.416) than Support Vector Regression (MAPE=0.857).

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