scholarly journals Rolling Bearing Initial Fault Detection Using Long Short-Term Memory Recurrent Network

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 171559-171569 ◽  
Author(s):  
Huaitao Shi ◽  
Lei Guo ◽  
Shuai Tan ◽  
Xiaotian Bai
Keyword(s):  
Fault Detection ◽  
Short Term Memory ◽  
Rolling Bearing ◽  
Recurrent Network ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Multi Time Steps Prediction dengan Recurrent Neural Network Long Short Term Memory

2018 ◽  
Vol 18 (1) ◽  
pp. 115-124
Author(s):  
Ahmad Ashril Rizal ◽  
Siti Soraya
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Short Term Memory ◽  
Recurrent Network ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Cell Cell

Tidak tersedianya sumber daya alam seperti migas, hasil hutan ataupun industri manufaktur yang berskala besar di pulau Lombok menyebabkan pariwisata telah menjadi sektor andalan dalam pembangunan daerah. Kontribusi sektor pariwisata menunjukkan trend yang semakin meningkat dari tahun ke tahun. Dampak positif pengeluaran wisatawan terhadap perekonomian terdistribusikan ke berbagai sektor. Akan tetapi, pemerinatah daerah umumnya akan melakukan persiapan wisata daerah hanya pada saat even  lokal saja. Padahal kunjungan wisatawan bukan hanya karena faktor adanya event lokal saja. Persiapan pemerintah daerah dan pelaku wisata sangat penting untuk meningkatkan stabilitas kunjungan wisatawan. Penelitian ini mengkaji prediksi kunjungan wisatawan dengan pendekatan Recurrent Neural Network Long Short Term Memory (RNN LSTM). LSTM berisi informasi di luar aliran normal dari recurrent nertwork dalam gate cell. Cell membuat keputusan tentang apa yang harus disimpan dan kapan mengizinkan pembacaan, penulisan dan penghapusan, melalui gate yang terbuka dan tertutup. Gate menyampaikan informasi berdasarkan kekuatan yang masuk ke dalamnya dan akan difilter menjadi bobot dari gate itu sendiri. Bobot tersebut sama seperti bobot input dan hidden unit yang disesuaikan melalui proses leraning pada recurrent network. Hasil penelitian yang dilakukan dengan membangun model prediksi kunjungan wisatawan dengan RNN LSTM menggunakan multi time steps mendapatkan hasil RMSE sebesar 6888.37 pada data training dan 14684.33 pada data testing.

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

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4612 ◽  
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.

Remaining Life Prediction Method for Rolling Bearing Based on the Long Short-Term Memory Network

2019 ◽  
Vol 50 (3) ◽  
pp. 2437-2454 ◽  
Author(s):  
Fengtao Wang ◽  
Xiaofei Liu ◽  
Gang Deng ◽  
Xiaoguang Yu ◽  
Hongkun Li ◽  
...  
Keyword(s):  
Life Prediction ◽  
Short Term Memory ◽  
Prediction Method ◽  
Rolling Bearing ◽  
Remaining Life ◽  
Short Term ◽  
Term Memory ◽  
Memory Network ◽  
Long Short Term Memory
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