Mechanical Condition Monitoring of Vacuum Circuit Breakers Using Artificial Neural Network

2005 ◽  
Vol E88-C (8) ◽  
pp. 1652-1658 ◽  
Author(s):  
Y. MENG
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Condition Monitoring ◽  
Circuit Breakers ◽  
Mechanical Condition ◽  
Artificial Neural

Tool Condition Monitoring Using Artificial Neural Network Models

2022 ◽  
pp. 400-426
Author(s):  
Srinivasa P. Pai ◽  
Nagabhushana T. N.
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Condition Monitoring ◽  
Medium Carbon Steel ◽  
Tool Condition Monitoring ◽  
Training Time ◽  
Tool Condition ◽  
Cell Structures ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Tool wear is a major factor that affects the productivity of any machining operation and needs to be controlled for achieving automation. It affects the surface finish, tolerances, dimensions of the workpiece, increases machine down time, and sometimes performance of machine tool and personnel are affected. This chapter deals with the application of artificial neural network (ANN) models for tool condition monitoring (TCM) in milling operations. The data required for training and testing the models studied and developed are from live experiments conducted in a machine shop on a widely used steel, medium carbon steel (En 8) using uncoated carbide inserts. Acoustic emission data and surface roughness data has been used in model development. The goal is for developing an optimal ANN model, in terms of compact architecture, least training time, and its ability to generalize well on unseen (test) data. Growing cell structures (GCS) network has been found to achieve these requirements.

A Novel Approach for Gas Turbine Condition Monitoring Combining CUSUM Technique and Artificial Neural Network

2009 ◽  
Author(s):  
Magnus Fast ◽  
Thomas Palme´ ◽  
Magnus Genrup
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Gas Turbine ◽  
Condition Monitoring ◽  
Sequential Analysis ◽  
Ann Model ◽  
Novel Approach ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Operational Data

Investigation of a novel condition monitoring approach, combining artificial neural network (ANN) with a sequential analysis technique, has been reported in this paper. For this purpose operational data from a Siemens SGT600 gas turbine has been employed for the training of an ANN model. This ANN model is subsequently used for the prediction of performance parameters of the gas turbine. Simulated anomalies are introduced on two different sets of operational data, acquired one year apart, whereupon this data is compared with corresponding ANN predictions. The cumulative sum (CUSUM) technique is used to improve and facilitate the detection of such anomalies in the gas turbine’s performance. The results are promising, displaying fast detection of small changes and detection of changes even for a degraded gas turbine.

Artificial Neural Network Architecture Design Using Similarity Measure With Applications in Engineering Monitoring and Diagnosis

1996 ◽  
Vol 118 (3) ◽  
pp. 635-639 ◽  
Author(s):  
Yuedong Chen ◽  
R. Du
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Condition Monitoring ◽  
Network Architecture ◽  
Cutting Condition ◽  
Architecture Design ◽  
Monitoring And Diagnosis ◽  
Training Samples ◽  
Artificial Neural ◽  
Engineering Monitoring

Artificial Neural Network (ANN) has been widely used for engineering monitoring and diagnosis. However, there are still several important problems unsolved and one of them is the architecture design of the ANN (namely, choosing the number of nodes in the hidden layer). In this technical brief, a new method of ANN architecture is introduced based on the idea that an ANN represents a mapping of training samples. Hence, the best ANN should represent the mapping that is most similar to the training samples. The method is tested using three practical engineering monitoring and diagnosis examples, including tool condition monitoring in turning, cutting condition monitoring in tapping, and metallographic condition monitoring in welding. It is demonstrated that the proposed method can improve the monitoring and diagnosis by approximately 3 percent.

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