Time series analysis and long short-term memory (LSTM) network prediction of BPV current density

An enhanced architecture of recurrent neural network based on Long Short-Term Memory (LSTM) is suggested in this paper for predicting the microclimate inside the greenhouse through its time series data. The microclimate inside the greenhouse largely affected by the external weather variations and it has a great impact on the greenhouse crops and its production. Therefore, it is a massive importance to predict the microclimate inside greenhouse as a preceding stage for accurate design of a control system that could fulfill the requirements of suitable environment for the plants and crop managing. The LSTM network is trained and tested by the temperatures and relative humidity data measured inside the greenhouse utilizing the mathematical greenhouse model with the outside weather data over 27 days. To evaluate the prediction accuracy of the suggested LSTM network, different measurements, such as Root Mean Square Error (RMSE) and Mean Absolute Error (MAE), are calculated and compared to those of conventional networks in references. The simulation results of LSTM network for forecasting the temperature and relative humidity inside greenhouse outperform over those of the traditional methods. The prediction results of temperature and humidity inside greenhouse in terms of RMSE approximately are 0.16 and 0.62 and in terms of MAE are 0.11 and 0.4, respectively, for both of them.

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Fault Detection and Diagnosis Using Combined Autoencoder and Long Short-Term Memory Network

Sensors ◽

10.3390/s19214612 ◽

2019 ◽

Vol 19 (21) ◽

pp. 4612 ◽

Cited By ~ 19

Author(s):

Pangun Park ◽

Piergiuseppe Di Marco ◽

Hyejeon Shin ◽

Junseong Bang

Keyword(s):

Time Series ◽

Fault Detection ◽

Short Term Memory ◽

Fault Detection And Diagnosis ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory ◽

Detection And Diagnosis ◽

Lstm Network ◽

Types Of Faults

Fault detection and diagnosis is one of the most critical components of preventing accidents and ensuring the system safety of industrial processes. In this paper, we propose an integrated learning approach for jointly achieving fault detection and fault diagnosis of rare events in multivariate time series data. The proposed approach combines an autoencoder to detect a rare fault event and a long short-term memory (LSTM) network to classify different types of faults. The autoencoder is trained with offline normal data, which is then used as the anomaly detection. The predicted faulty data, captured by autoencoder, are put into the LSTM network to identify the types of faults. It basically combines the strong low-dimensional nonlinear representations of the autoencoder for the rare event detection and the strong time series learning ability of LSTM for the fault diagnosis. The proposed approach is compared with a deep convolutional neural network approach for fault detection and identification on the Tennessee Eastman process. Experimental results show that the combined approach accurately detects deviations from normal behaviour and identifies the types of faults within the useful time.

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Spatio-temporal predictions of SST time series in China’s offshore waters using a regional convolution long short-term memory (RC-LSTM) network

International Journal of Remote Sensing ◽

10.1080/01431161.2019.1701724 ◽

2020 ◽

Vol 41 (9) ◽

pp. 3368-3389 ◽

Cited By ~ 2

Author(s):

Lingyu Xu ◽

Qin Li ◽

Jie Yu ◽

Lei Wang ◽

Jiang Xie ◽

...

Keyword(s):

Time Series ◽

Short Term Memory ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory ◽

Spatio Temporal ◽

Lstm Network

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Time Series Multiple Channel Convolutional Neural Network with Attention-Based Long Short-Term Memory for Predicting Bearing Remaining Useful Life

Sensors ◽

10.3390/s20010166 ◽

2019 ◽

Vol 20 (1) ◽

pp. 166 ◽

Cited By ~ 4

Author(s):

Jehn-Ruey Jiang ◽

Juei-En Lee ◽

Yi-Ming Zeng

Keyword(s):

Neural Network ◽

Time Series ◽

Convolutional Neural Network ◽

Short Term Memory ◽

Remaining Useful Life ◽

Multiple Channels ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory ◽

Lstm Network

This paper proposes two deep learning methods for remaining useful life (RUL) prediction of bearings. The methods have the advantageous end-to-end property that they take raw data as input and generate the predicted RUL directly. They are TSMC-CNN, which stands for the time series multiple channel convolutional neural network, and TSMC-CNN-ALSTM, which stands for the TSMC-CNN integrated with the attention-based long short-term memory (ALSTM) network. The proposed methods divide a time series into multiple channels and take advantage of the convolutional neural network (CNN), the long short-term memory (LSTM) network, and the attention-based mechanism for boosting performance. The CNN performs well for extracting features from data with multiple channels; dividing a time series into multiple channels helps the CNN extract relationship among far-apart data points. The LSTM network is excellent for processing temporal data; the attention-based mechanism allows the LSTM network to focus on different features at different time steps for better prediction accuracy. PRONOSTIA bearing operation datasets are applied to the proposed methods for the purpose of performance evaluation and comparison. The comparison results show that the proposed methods outperform the others in terms of the mean absolute error (MAE) and the root mean squared error (RMSE) of RUL prediction.

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Landslide Prediction Using Long Short Term Memory(LSTM)Neural Network on time series data in Pakistan

2021 International Conference on Artificial Intelligence (ICAI) ◽

10.1109/icai52203.2021.9445236 ◽

2021 ◽

Author(s):

Mehreen Mubashar ◽

Gul Muhammad Khan ◽

Ramla Khan

Keyword(s):

Neural Network ◽

Time Series ◽

Time Series Data ◽

Short Term Memory ◽

Series Data ◽

Short Term ◽

Landslide Prediction ◽

Term Memory ◽

Long Short Term Memory

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Prediction of InSAR deformation time-series using a long short-term memory neural network

International Journal of Remote Sensing ◽

10.1080/01431161.2021.1947540 ◽

2021 ◽

Vol 42 (18) ◽

pp. 6921-6944

Author(s):

Yi Chen ◽

Yi He ◽

Lifeng Zhang ◽

Youdong Chen ◽

Hongyu Pu ◽

...

Keyword(s):

Neural Network ◽

Time Series ◽

Short Term Memory ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory

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Extraction of local and global features by a convolutional neural network–long short-term memory network for diagnosing bearing faults

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1177/09544062211016505 ◽

2021 ◽

pp. 095440622110165

Author(s):

Zhang Chao ◽

Wang Wei-zhi ◽

Zhang Chen ◽

Fan Bin ◽

Wang Jian-guo ◽

...

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Condition Monitoring ◽

Short Term Memory ◽

Vibration Signal ◽

Short Term ◽

Global Features ◽

Term Memory ◽

Long Short Term Memory ◽

Lstm Network

Accurate and reliable fault diagnosis is one of the key and difficult issues in mechanical condition monitoring. In recent years, Convolutional Neural Network (CNN) has been widely used in mechanical condition monitoring, which is also a great breakthrough in the field of bearing fault diagnosis. However, CNN can only extract local features of signals. The model accuracy and generalization of the original vibration signals are very low in the process of vibration signal processing only by CNN. Based on the above problems, this paper improves the traditional convolution layer of CNN, and builds the learning module (local feature learning block, LFLB) of the local characteristics. At the same time, the Long Short-Term Memory (LSTM) is introduced into the network, which is used to extract the global features. This paper proposes the new neural network—improved CNN-LSTM network. The extracted deep feature is used for fault classification. The improved CNN-LSTM network is applied to the processing of the vibration signal of the faulty bearing collected by the bearing failure laboratory of Inner Mongolia University of science and technology. The results show that the accuracy of the improved CNN-LSTM network on the same batch test set is 98.75%, which is about 24% higher than that of the traditional CNN. The proposed network is applied to the bearing data collection of Western Reserve University under the condition that the network parameters remain unchanged. The experiment shows that the improved CNN-LSTM network has better generalization than the traditional CNN.

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Long Short-Term Memory Neural Network (LSTM-NN) for Aquifer Level Time Series Forecasting Using in-Situ Piezometric Observations

Journal of Hydrology ◽

10.1016/j.jhydrol.2021.126800 ◽

2021 ◽

pp. 126800

Author(s):

Ryan Solgi ◽

Hugo A. Loáiciga ◽

Mark Kram

Keyword(s):

Neural Network ◽

Time Series ◽

Short Term Memory ◽

Time Series Forecasting ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory

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

10.1115/ipack2021-74066 ◽

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.

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