scholarly journals A Novel Hybrid Deep Learning Method for Fault Diagnosis of Rotating Machinery Based on Extended WDCNN and Long Short-Term Memory

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6614
Author(s):  
Yangde Gao ◽  
Cheol Hong Kim ◽  
Jong-Myon Kim
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Short Term Memory ◽  
Rotating Machinery ◽  
Identification Accuracy ◽  
Learning Method ◽  
Short Term ◽  
Deep Convolutional Neural Networks ◽  
Term Memory ◽  
Long Short Term Memory

Deep learning (DL) plays a very important role in the fault diagnosis of rotating machinery. To enhance the self-learning capacity and improve the intelligent diagnosis accuracy of DL for rotating machinery, a novel hybrid deep learning method (NHDLM) based on Extended Deep Convolutional Neural Networks with Wide First-layer Kernels (EWDCNN) and long short-term memory (LSTM) is proposed for complex environments. First, the EWDCNN method is presented by extending the convolution layer of WDCNN, which can further improve automatic feature extraction. The LSTM then changes the geometric architecture of the EWDCNN to produce a novel hybrid method (NHDLM), which further improves the performance for feature classification. Compared with CNN, WDCNN, and EWDCNN, the proposed NHDLM method has the greatest performance and identification accuracy for the fault diagnosis of rotating machinery.

scholarly journals Aircraft Gearbox Fault Diagnosis System: An Approach based on Deep Learning Techniques

2020 ◽  
Vol 30 (1) ◽  
pp. 258-272
Author(s):  
P B Mallikarjuna ◽  
M Sreenatha ◽  
S Manjunath ◽  
Niranjan C Kundur
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Frequency Domain ◽  
Short Term Memory ◽  
Aircraft Engine ◽  
Learning Models ◽  
Short Term ◽  
Term Memory ◽  
Gearbox Vibration ◽  
Long Short Term Memory

Abstract Gearbox is one of the vital components in aircraft engines. If any small damage to gearbox, it can cause the breakdown of aircraft engine. Thus it is significant to study fault diagnosis in gearbox system. In this paper, two deep learning models (Long short term memory (LSTM) and Bi-directional long short term memory (BLSTM)) are proposed to classify the condition of gearbox into good or bad. These models are applied on aircraft gearbox vibration data in both time and frequency domain. A publicly available aircraft gearbox vibration dataset is used to evaluate the performance of proposed models. The results proved that accuracy achieved by LSTM and BLSTM are highly reliable and applicable in health monitoring of aircraft gearbox system in time domain as compared to frequency domain. Also, to show the superiority of proposed models for aircraft gearbox fault diagnosis, performance is compared with classical machine learning models.

scholarly journals Implementasi Deep Learning Menggunakan Arsitektur Long Short Term Memory(LSTM) Untuk Prediksi Curah Hujan Kota Malang

Repositor ◽  
2020 ◽  
Vol 2 (3) ◽  
pp. 331
Author(s):  
Muhammad Rizki ◽  
Setio Basuki ◽  
Yufis Azhar
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Daily Activities ◽  
Learning Method ◽  
Short Term ◽  
Term Memory ◽  
Weather Forecasts ◽  
Long Short Term Memory ◽  
Input Gate ◽  
Gate Input

AbstrakTidak selamanya cuaca di Indonesia berjalan dengan normal atau sesuai dengan musimnya, cuaca sering berubah secara tiba-tiba setiap saat karena ada faktor-faktor yang mempengaruhi penurunan dan peningkatan curah hujan. perkiraan cuaca sangatlah dibutuhkan dan sangat bermanfaat olah berbagai pihak karena bisa menjadi acuan bagi berbagai kalangan untuk menjalani kegiatan mereka sehari-hari. Penelitian dilakukan menggunakan metode Deep Learning karena dari beberapa penelitian sebelumnya yang menggunakan Deep Learning dalam kasus yang berbeda mampu menghasilkan akurasi diatas 85%. Deep learning adalah jaringan yang terdiri dari beberapa layer. Layer-layer tersebut berasal dari kumpulan node-node. Arsitektur yang digunakan yaitu Long Short Term Memory(LSTM) karena pada penelitian-penelitian sebelumnya menggunakan LSTM dalam kasus yang berbeda mendapat hasil yang baik yaitu RME yang dihasilkan kecil. LSTM memiliki struktur seperti rantai dan struktur pada tiap sel terdapat 3 gate yaitu forget gate, input gate, dan output gate. Oleh karena itu, perhitungan yang dilakukan lebih kompleks ditambah lagi dengan Deep Learning diharapkan mendapat hasil yang lebih akurat. Data yang digunakan yaitu data curah hujan kota Malang yang berasal dari BMKG. Abstract The weather in Indonesia does not always run normally or in accordance with the season, the weather often changes suddenly at any time because there are factors that affect the decrease and increase in rainfall. weather forecasts are needed and very useful if the various parties because it can be a reference for various circles to undergo their daily activities. The study was conducted using Deep Learning method because of some previous research using Deep Learning in different cases able to produce accuracy above 85%. Deep learning is a network consisting of several layers. The layers are derived from a collection of nodes. The architecture used is Long Short Term Memory (LSTM) because in previous studies using LSTM in different case got good result that is small generated RME. LSTM has a structure like chains and structures in each cell there are 3 gates of forget gate, input gate, and output gate. Therefore, the calculations performed more complex plus the Deep Learning is expected to get more accurate results. The data used is the rainfall data of Malang city that comes from BMKG. 

scholarly journals Fault Diagnosis for Bearing Based on 1DCNN and LSTM

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Haibin Sun ◽  
Shichao Zhao
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Short Term Memory ◽  
Rotating Machinery ◽  
Fault Identification ◽  
Short Term ◽  
Term Memory ◽  
One Dimensional ◽  
Memory Network ◽  
Long Short Term Memory

Condition monitoring and fault diagnosis of the bearing are essential for the smooth operation of rotating machinery. In this paper, an end-to-end intelligent fault diagnosis method for bearing combining one-dimensional convolutional neural network with long short-term memory network (1DCNN-LSTM) is proposed for the deficiencies of existing fault diagnosis methods. First, the proposed method takes one-dimensional fault data directly as input. Second, one-dimensional convolutional neural network (1DCNN) is used for self-adaptively extracting robust features from the original bearing signal, and more features are extracted while ensuring the validity and saliency of the extracted features by combining maximum pooling and average pooling layers to downsample features. Then, long short-term memory network (LSTM) is used to learn the temporal dependencies among features. At last, fault identification is achieved. 1DCNN-LSTM does not require any manual feature extraction, and the errors caused by reliance on expert experience and incomplete information in traditional feature extraction methods are avoided. The results show that the proposed classifier with good generalization performance not only diagnoses the category of fault quickly and accurately under different load conditions but also achieves an average fault identification accuracy of 99.95%. For its powerful learning abilities, this method can also be applied to the bearing fault diagnosis of rotating machinery in many fields.

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