scholarly journals Multi-class Damage Detection and Localization Using Long Short-Term Memory (LSTM) Networks

2021 ◽  
Author(s):  
Sandeep Sony
Keyword(s):  
Damage Detection ◽  
Structural Damage ◽  
Short Term Memory ◽  
Damage Identification ◽  
Binary Classification ◽  
Short Term ◽  
Damage Classification ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Lstm Network

In this paper, a novel method is proposed for detecting and localizing structural damage by classifying acceleration responses of a structure using a long short-term memory (LSTM) network. Windows of samples are extracted from acceleration responses in a novel data pre-processing pipeline, and an LSTM network is developed to classify the signals into multiple classes. A predicted classification of a signal by the LSTM network into one of the damage levels indicates a damage detection. Furthermore, multiple signals obtained from the vibration sensors placed on a structure are provided as input to the LSTM model, and the resulting predicted class probabilities are used to identify the locations with high probability of damage. The proposed method is validated on the experimental setup of the Qatar University Grandstand Simulator (QUGS) for binary classification, as well as, full-scale study of the Z24 bridge benchmark data for multi-class damage classification. Experiments show that the proposed LSTM-based method performs on par with 1D convolutional neural networks (1D CNN) on the QUGS dataset, and outperforms the 1D CNN on the Z24 dataset. The novelty of this study lies in the use of recurrent neural network based LSTM for vibration data for multi-class damage identification and localization.

scholarly journals Damage Identification of Long-Span Bridges Using the Hybrid of Convolutional Neural Network and Long Short-Term Memory Network

Algorithms ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 180
Author(s):  
Lei Fu ◽  
Qizhi Tang ◽  
Peng Gao ◽  
Jingzhou Xin ◽  
Jianting Zhou
Keyword(s):  
Short Term Memory ◽  
Damage Identification ◽  
Identification Accuracy ◽  
Damage Localization ◽  
Short Term ◽  
Term Memory ◽  
Localization Accuracy ◽  
Long Span Bridges ◽  
Long Span ◽  
Long Short Term Memory

The shallow features extracted by the traditional artificial intelligence algorithm-based damage identification methods pose low sensitivity and ignore the timing characteristics of vibration signals. Thus, this study uses the high-dimensional feature extraction advantages of convolutional neural networks (CNNs) and the time series modeling capability of long short-term memory networks (LSTM) to identify damage to long-span bridges. Firstly, the features extracted by CNN and LSTM are fused as the input of the fully connected layer to train the CNN-LSTM model. After that, the trained CNN-LSTM model is employed for damage identification. Finally, a numerical example of a large-span suspension bridge was carried out to investigate the effectiveness of the proposed method. Furthermore, the performance of CNN-LSTM and CNN under different noise levels was compared to test the feasibility of application in practical engineering. The results demonstrate the following: (1) the combination of CNN and LSTM is satisfactory with 94% of the damage localization accuracy and only 8.0% of the average relative identification error (ARIE) of damage severity identification; (2) in comparison to the CNN, the CNN-LSTM results in superior identification accuracy; the damage localization accuracy is improved by 8.13%, while the decrement of ARIE of damage severity identification is 5.20%; and (3) the proposed method is capable of resisting the influence of environmental noise and acquires an acceptable recognition effect for multi-location damage; in a database with a lower signal-to-noise ratio of 3.33, the damage localization accuracy of the CNN-LSTM model is 67.06%, and the ARIE of the damage severity identification is 31%. This work provides an innovative idea for damage identification of long-span bridges and is conducive to promote follow-up studies regarding structural condition evaluation.

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

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.

scholarly journals Intelligent Islanding Detection of Microgrids Using Long Short-Term Memory Networks

Energies ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5762
Author(s):  
Syed Basit Ali Bukhari ◽  
Khawaja Khalid Mehmood ◽  
Abdul Wadood ◽  
Herie Park
Keyword(s):  
Short Term Memory ◽  
Computational Time ◽  
Islanding Detection ◽  
Phase Voltage ◽  
Short Term ◽  
Term Memory ◽  
Three Phase ◽  
Empirical Wavelet Transform ◽  
Long Short Term Memory ◽  
Lstm Network

This paper presents a new intelligent islanding detection scheme (IIDS) based on empirical wavelet transform (EWT) and long short-term memory (LSTM) network to identify islanding events in microgrids. The concept of EWT is extended to extract features from three-phase signals. First, the three-phase voltage signals sampled at the terminal of targeted distributed energy resource (DER) or point of common coupling (PCC) are decomposed into empirical modes/frequency subbands using EWT. Then, instantaneous amplitudes and instantaneous frequencies of the three-phases at different frequency subbands are combined, and various statistical features are calculated. Finally, the EWT-based features along with the three-phase voltage signals are input to the LSTM network to differentiate between non-islanding and islanding events. To assess the efficacy of the proposed IIDS, extensive simulations are performed on an IEC microgrid and an IEEE 34-node system. The simulation results verify the effectiveness of the proposed IIDS in terms of non-detection zone (NDZ), computational time, detection accuracy, and robustness against noisy measurement. Furthermore, comparisons with existing intelligent methods and different LSTM architectures demonstrate that the proposed IIDS offers higher reliability by significantly reducing the NDZ and stands robust against measurements uncertainty.

scholarly journals Underground Cylindrical Objects Detection and Diameter Identification in GPR B-Scans via the CNN-LSTM Framework

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1804
Author(s):  
Wentai Lei ◽  
Jiabin Luo ◽  
Feifei Hou ◽  
Long Xu ◽  
Ruiqing Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Short Term Memory ◽  
Vital Role ◽  
Short Term ◽  
Term Memory ◽  
Region Detection ◽  
Field Environment ◽  
Long Short Term Memory ◽  
Lstm Network

Ground penetrating radar (GPR), as a non-invasive instrument, has been widely used in the civil field. The interpretation of GPR data plays a vital role in underground infrastructures to transfer raw data to the interested information, such as diameter. However, the diameter identification of objects in GPR B-scans is a tedious and labor-intensive task, which limits the further application in the field environment. The paper proposes a deep learning-based scheme to solve the issue. First, an adaptive target region detection (ATRD) algorithm is proposed to extract the regions from B-scans that contain hyperbolic signatures. Then, a Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) framework is developed that integrates Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network to extract hyperbola region features. It transfers the task of diameter identification into a task of hyperbola region classification. Experimental results conducted on both simulated and field datasets demonstrate that the proposed scheme has a promising performance for diameter identification. The CNN-LSTM framework achieves an accuracy of 99.5% on simulated datasets and 92.5% on field datasets.

ECG Based Stress Detection in Automobile Drivers Using Long Short-Term Memory (LSTM) Network

2021 ◽  
pp. 333-342
Author(s):  
Ramyashri B. Ramteke ◽  
Vijaya R. Thool
Keyword(s):  
Short Term Memory ◽  
Stress Detection ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Lstm Network

Very-Short-Term Solar Forecasting with Long Short-Term Memory (LSTM) Network

2020 ◽  
Author(s):  
Yanbin Lin ◽  
Dongliang Duan ◽  
Xueming Hong ◽  
Xiang Cheng ◽  
Liuqing Yang ◽  
...  
Keyword(s):  
Short Term Memory ◽  
Solar Forecasting ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Lstm Network

Deep Network based on Long Short-Term Memory for Time Series Prediction of Microclimate Data inside the Greenhouse

2020 ◽  
Vol 19 (02) ◽  
pp. 2050013
Author(s):  
Sawsan Morkos Gharghory
Keyword(s):  
Time Series ◽  
Relative Humidity ◽  
Time Series Data ◽  
Short Term Memory ◽  
Weather Data ◽  
Series Data ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Lstm Network

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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