Vibration-based fault diagnosis of spur bevel gear box using fuzzy technique
Publication Type:Journal Article
Source:Expert Systems with Applications, Elsevier, Volume 36, Issue 2, Number 2, p.3119–3135 (2009)
To determine the condition of an inaccessible gear in an operating machine the vibration signal of the machine can be continuously monitored by placing a sensor close to the source of the vibrations. These signals can be further processed to extract the features and identify the status of the machine. The vibration signal acquired from the operating machine has been used to effectively diagnose the condition of inaccessible moving components inside the machine. Suitable sensors are kept at various locations to pick up the signals produced by machinery and these signals are very meaningful in condition diagnosis surveillance. To determine the important characteristics and to unravel the significance of these signals, further analysis or processing is required.
This paper presents the use of decision tree for selecting best statistical features that will discriminate the fault conditions of the gear box from the signals extracted. These features are extracted from vibration signals. A rule set is formed from the extracted features and fed to a fuzzy classifier. The rule set necessary for building the fuzzy classifier is obtained largely by intuition and domain knowledge. This paper also presents the usage of decision tree to generate the rules automatically from the feature set. The vibration signal from a piezo-electric transducer is captured for the following conditions – good bevel gear, bevel gear with tooth breakage (GTB), bevel gear with crack at root of the tooth (GTC), and bevel gear with face wear of the teeth (TFW) for various loading and lubrication conditions. The statistical features were extracted and good features that discriminate the different fault conditions of the gearbox were selected using decision tree. The rule set for fuzzy classifier is obtained by once using the decision tree again. A fuzzy classifier is built and tested with representative data. The results are found to be encouraging.
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