Improving Performance of an Artificial Neural Network Based Gearbox Fault Diagnosis System

2010 ◽  
Author(s):  
Ali Hajnayeb ◽  
Ahmad Ghasemloonia ◽  
Siamak Esmaeelzadeh Khadem ◽  
Mohammad Hasan Moradi
Keyword(s):  
Fault Diagnosis ◽  
Condition Monitoring ◽  
Vibration Signal ◽  
Monitoring Systems ◽  
Test Rig ◽  
Tooth Gear ◽  
Different Types ◽  
Condition Monitoring Systems ◽  
Artificial Neural ◽  
Detection Speed

The automatic vibration monitoring methods of gears and gearboxes due to their extensive applications in industry are improving. Hence, their vibration signal and its derived features, has been an interesting topic for researchers in this field. On the other hand, optimizing the number of vibration signal features used in the detection and diagnosis process is crucial for increasing the fault detection speed of automatic condition monitoring systems. In this paper, a system based on multiple layer perceptron artificial neural networks (MLP ANNs) is designed to diagnose different types of fault in a gearbox. Using a feature selection method, the system is optimized through eliminating unimportant features of vibration signals. This method is based on a simple and fast sensitivity evaluation process, which results in a considerable estimation, despite its simplicity. Consequently, the system’s speed increases, while the classification error decreases or remains constant in some other cases. An experimental test rig data set is used to verify the effectiveness and accuracy of the mentioned method. Four different types of data which are generated through the test rig setup are: no fault condition, 5% fault (5% eroded tooth) gear, 20% eroded tooth gear and the broken tooth gear. The results verify that eliminating some input features of gear vibration signal, using this method, will increase the accuracy and detection speed of gear fault diagnosis methods. The improved systems with fewer input features and higher precision, facilitates the development of online automatic condition monitoring systems.

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.

A Survey of Condition Monitoring Systems Operation in Romanian Transmission Substations

2021 ◽  
Author(s):  
Bogdan Leu ◽  
Bogdan-Adrian Enache ◽  
Florin-Ciprian Argatu ◽  
Marilena Stanculescu
Keyword(s):  
Condition Monitoring ◽  
Monitoring Systems ◽  
Condition Monitoring Systems

A Highly Reliable Online Monitoring and Real-Time Alarm System for the Insulation Condition of Power Transformers

2014 ◽  
Vol 971-973 ◽  
pp. 1045-1050
Author(s):  
Wen Xing Sun ◽  
Zhao Hui Li ◽  
Shi Jie Cheng
Keyword(s):  
Electric Power ◽  
Real Time ◽  
Condition Monitoring ◽  
Online Monitoring ◽  
Hydropower Plant ◽  
Power Transformers ◽  
Monitoring Systems ◽  
Alarm System ◽  
Condition Monitoring Systems ◽  
Insulation Condition

Many successful applications for the online monitoring of the insulation condition for electric power transformers have been reported over last thirty years. However, false or unsolved alarms have been quite frequently generated by those condition monitoring systems. Failures and some occasionally catastrophic accidents involving transformers have still occurred. A highly reliable insulation condition online monitoring and real-time alarm system has been developed, to help resolve these problems. An electric power transformer has strongly linked mechanical, electrical, magnetic, chemical and thermal characteristics, and is also directly linked to circuit breakers and generators. Team Intelligence (TI) was employed to integrate all the monitoring modules of the various different aspects of the transformer into one unique system. This system could also be integrate with the condition monitoring systems of various linked facilities, such as the monitoring systems of the turbine and the generator in a Optimal Maintenance Information System for Hydropower Plant (HOMIS). Highly reliable monitoring and real-time alarms of transformer insulation condition could be achieved, due to highly coordinated and rapid response features. This system has been deployed in several hydropower plants. The industrial application examples are demonstrated.

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