Development of Fault Detection System for Ball Bearing of Induction Motor Using Vibration Signal

2012 ◽  
Vol 2 (5) ◽  
pp. 256-259
Author(s):  
Amit Shrivastava ◽  
◽  
Dr. S. Wadhwani Dr. S. Wadhwani
Keyword(s):  
Fault Detection ◽  
Induction Motor ◽  
Ball Bearing ◽  
Detection System ◽  
Vibration Signal

Fault Detection System of Automobile Engine Based on Correlation Dimension Feature Extraction

2013 ◽  
Vol 380-384 ◽  
pp. 782-785
Author(s):  
Xian Heng Zeng ◽  
Li Hua Yin
Keyword(s):  
Feature Extraction ◽  
Fault Detection ◽  
Correlation Dimension ◽  
Detection System ◽  
Vibration Signal ◽  
Great Difficulty ◽  
Classification Performance ◽  
Computational Accuracy ◽  
Automobile Engine ◽  
Feature Extraction Method

According to the high fault rate and the great difficulty of diagnosis for the automobile engine, an automobile engine faults detection system was designed. Because the vibration signal of the engine could reflect the faults types to a great extent, a fault detection method was proposed based on the extraction of the vibration signal correlation dimension. The collected vibration signal which was from different type of automobile engines was processed and analyzed. The correlation dimension was extracted and an improved correlation algorithm was proposed in the system, the computational accuracy was improved, and the standard deviation of the improved algorithm lowers about 50% in comparison with the traditional algorithm, the classification performance is raised variously, the excellent detection performance was showed in the system. The detection result shows that the correlation dimension feature extraction method that this paper proposed can detect and diagnose different types of automobile engine faults such as start subsystem fault, ignition subsystem fault, fuel supply subsystem, etc. The detection conclusion was stable and the simulation result has much great application performance.

scholarly journals Establish Induction Motor Fault Diagnosis System Based on Feature Selection Approaches with MRA

Processes ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1055
Author(s):  
Chun-Yao Lee ◽  
Meng-Syun Wen
Keyword(s):  
Feature Selection ◽  
Fault Detection ◽  
Induction Motor ◽  
Detection System ◽  
Fitness Value ◽  
Correlation Based Feature Selection ◽  
Fault Diagnosis System ◽  
The Common ◽  
Artificial Neural Network Ann ◽  
Feature Selection Approach

This paper proposes a feature selection (FS) approach, namely, correlation and fitness value-based feature selection (CFFS). CFFS is an improvement feature selection approach of correlation-based feature selection (CFS) for the common failure cases of the induction motor. CFFS establishes the induction motor fault detection (FD) system with artificial neural network (ANN). This study analyzes the current signal of the induction motor with multiresolution analysis (MRA), extracts the features, and uses feature selection approaches (ReliefF, CFS, and CFFS) to reduce the number of features and maintain the accuracy of the induction motor fault detection system. Finally, the induction motor fault detection system is trained by the feature selection approaches selected features. The best induction motor fault detection system will be established through the comparison of the efficiency of these FS approaches.

scholarly journals Wireless Fault Detection System for an Industrial Robot Based on Statistical Control Chart

2017 ◽  
Vol 7 (6) ◽  
pp. 3421
Author(s):  
Alaa Abdulhady Jaber ◽  
Robert Bicker
Keyword(s):  
Fault Detection ◽  
Control Chart ◽  
Production Systems ◽  
Detection System ◽  
Industrial Robot ◽  
Vibration Signal ◽  
Cost Effective ◽  
Base Station ◽  
Industrial Robots ◽  
Statistical Control

Industrial robots are now commonly used in production systems to improve productivity, quality and safety in manufacturing processes. Recent developments involve using robots cooperatively with production line operatives. Regardless of application, there are significant implications for operator safety in the event of a robot malfunction or failure, and the consequent downtime has a significant impact on productivity in manufacturing. Machine healthy monitoring is a type of maintenance inspection technique by which an operational asset is monitored and the data obtained is analysed to detect signs of degradation and thus reducing the maintenance costs. Developments in electronics and computing have opened new horizons in the area of condition monitoring. The aim of using wireless electronic systems is to allow data analysis to be carried out locally at field level and transmitting the results wirelessly to the base station, which as a result will help to overcome the need for wiring and provides an easy and cost-effective sensing technique to detect faults in machines. So, the main focuses of this research is to develop an online and wireless fault detection system for an industrial robot based on statistical control chart approach. An experimental investigation was accomplished using the PUMA 560 robot and vibration signal capturing was adopted, as it responds immediately to manifest itself if any change is appeared in the monitored machine, to extract features related to the robot health conditions. The results indicate the successful detection of faults at the early stages using the key extracted parameters.

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