scholarly journals Gearbox Fault Diagnosis Based on Vibration Signals Measured Remotely

2009 ◽  
Vol 413-414 ◽  
pp. 175-180 ◽  
Author(s):  
Salem Al-Arbi ◽  
Feng Shou Gu ◽  
Lu Yang Guan ◽  
Andrew Ball ◽  
Abdelhamid Naid
Keyword(s):  
Signal Processing ◽  
Fault Diagnosis ◽  
Condition Monitoring ◽  
Transmission System ◽  
Gear Transmission ◽  
Gear System ◽  
Vibration Signals ◽  
Remote Measurements ◽  
Processing Techniques ◽  
Signal Processing Techniques

In many cases, it is impractical to measure the vibrations directly at or close to their source. It is a common practice to measure the vibration at a location far from the source for condition monitoring purposes. The vibration measured in this way inevitably has high distortions from the vibrations due to the effect of the attenuation of signal paths and the interference from other sources. The suppression of the distortions is a key issue for the remote measurements based condition monitoring. In this paper, the influences of transducer locations are investigated on a typical gearbox transmission system for the detection of the faults induced to the gearbox. Several signal processing techniques’ analysis results show that the attenuation and interference cause high influences on the gear transmission signals. However, time synchronous average (TSA) is very effective to detect the local faults induced to the gear system.

scholarly journals Development of a Condition Monitoring Algorithm for Industrial Robots based on Artificial Intelligence and Signal Processing Techniques

2018 ◽  
Vol 8 (2) ◽  
pp. 996 ◽  
Author(s):  
Alaa Adulhady Jaber ◽  
Robert Bicker
Keyword(s):  
Signal Processing ◽  
Condition Monitoring ◽  
Industrial Robots ◽  
Discrete Wavelet ◽  
Advantages And Disadvantages ◽  
Condition Monitoring System ◽  
The Time Domain ◽  
Processing Techniques ◽  
Signal Processing Techniques ◽  
Monitoring Algorithm

Signal processing plays a significant role in building any condition monitoring system. Many types of signals can be used for condition monitoring of machines, such as vibration signals, as in this research; and processing these signals in an appropriate way is crucial in extracting the most salient features related to different fault types. A number of signal processing techniques can fulfil this purpose, and the nature of the captured signal is a significant factor in the selection of the appropriate technique. This chapter starts with a discussion of the proposed robot condition monitoring algorithm. Then, a consideration of the signal processing techniques which can be applied in condition monitoring is carried out to identify their advantages and disadvantages, from which the time-domain and discrete wavelet transform signal analysis are selected.

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