scholarly journals Canonical variable analysis and long short-term memory for fault diagnosis and performance estimation of a centrifugal compressor

2018 ◽  
Vol 72 ◽  
pp. 177-191 ◽  
Author(s):  
Xiaochuan Li ◽  
Fang Duan ◽  
Panagiotis Loukopoulos ◽  
Ian Bennett ◽  
David Mba
Keyword(s):  
Fault Diagnosis ◽  
Centrifugal Compressor ◽  
Canonical Variable ◽  
Short Term Memory ◽  
Performance Estimation ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
And Performance ◽  
Variable Analysis

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 Prediction of Process Quality Performance Using Statistical Analysis and Long Short-Term Memory

2022 ◽  
Vol 12 (2) ◽  
pp. 735
Author(s):  
Tola Pheng ◽  
Tserenpurev Chuluunsaikhan ◽  
Ga-Ae Ryu ◽  
Sung-Hoon Kim ◽  
Aziz Nasridinov ◽  
...  
Keyword(s):  
Short Term Memory ◽  
Process Capability ◽  
Process Quality ◽  
Control Analysis ◽  
Short Term ◽  
Statistical Process ◽  
Quality Performance ◽  
Term Memory ◽  
Long Short Term Memory ◽  
And Performance

In the manufacturing industry, the process capability index (Cpk) measures the level and capability required to improve the processes. However, the Cpk is not enough to represent the process capability and performance of the manufacturing processes. In other words, considering that the smart manufacturing environment can accommodate the big data collected from various facilities, we need to understand the state of the process by comprehensively considering diverse factors contained in the manufacturing. In this paper, a two-stage method is proposed to analyze the process quality performance (PQP) and predict future process quality. First, we propose the PQP as a new measure for representing process capability and performance, which is defined by a composite statistical process analysis of such factors as manufacturing cycle time analysis, process trajectory of abnormal detection, statistical process control analysis, and process capability control analysis. Second, PQP analysis results are used to predict and estimate the stability of the production process using a long short-term memory (LSTM) neural network, which is a deep learning algorithm-based method. The present work compares the LSTM prediction model with the random forest, autoregressive integrated moving average, and artificial neural network models to convincingly demonstrate the effectiveness of our proposed approach. Notably, the LSTM model achieved higher accuracy than the other models.

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