scholarly journals First steps on fan matrix condition monitoring and fault diagnosis using an array of digital MEMS microphones

2017 ◽  
Author(s):  
Lara del Val ◽  
Alberto Izquierdo ◽  
Juan J. Villacorta ◽  
Luis Suárez ◽  
Marta Herráez
Keyword(s):  
Fault Diagnosis ◽  
Condition Monitoring ◽  
Mems Microphones ◽  
Matrix Condition

scholarly journals Analysis of the feasibility of an array of MEMS microphones to machinery condition monitoring or fault diagnosis

2017 ◽  
Vol 141 (5) ◽  
pp. 3958-3958
Author(s):  
Lara del-Val ◽  
Alberto Izquierdo ◽  
Juan J. Villacorta ◽  
Luis Suarez ◽  
Marta Herráez
Keyword(s):  
Fault Diagnosis ◽  
Condition Monitoring ◽  
Machinery Condition Monitoring ◽  
Mems Microphones

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.

Internet/Intranet Based Remote Condition Monitoring and Fault Diagnosis Scheme and System for Steam Turboset

2005 ◽  
Vol 293-294 ◽  
pp. 365-372 ◽  
Author(s):  
Yong Yong He ◽  
Wen Xiu Lu ◽  
Fu Lei Chu
Keyword(s):  
Fault Diagnosis ◽  
Power System ◽  
Electric Power ◽  
Condition Monitoring ◽  
Economic Benefits ◽  
Electric Power System ◽  
The Internet ◽  
Time Data ◽  
On Line ◽  
Line Condition

The steam turboset is the key equipment of the electric power system. Thus, it is very important and necessary to monitor and diagnose the running condition and the faults of the steam turboset for the safe and normal running of the electric power system. In this paper, the Internet/Intranet based remote condition monitoring and fault diagnosis scheme is proposed. The corresponding technique and methods are discussed in detail. And a real application system is developed for the 300MW steam turboset. In this scheme, the system is built on the Internet/Intranet and the Client/Server construction and Web/Server model are adopted. The proposed scheme can guarantee real-time data acquisition and on-line condition analysis simultaneously. And especially, the remote condition monitoring and fault diagnosis can be implemented effectively. The developed system has been installed in a power plant of China. And the plant has obtained great economic benefits from it.

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