Current research in wireless sensor networks has chiefly focused on environmental monitoring applications. Wireless sensors are emerging as viable instrumentation techniques for industrial applications because of their flexibility, non-intrusive operation, safety and their low cost, low power characteristics. We describe a prototype gear condition monitoring system incorporating wireless sensors. Measurements of strain on gear teeth, vibration and temperature were undertaken using strain gage, accelerometer, and thermistors, respectively. The sensors interface to a sensor board that is connected to a microprocessor and a radio. Gear faults diagnosis using conventional classification techniques such as principle component analysis (PCA), Fisher linear discriminant analysis (LDA) and Nearest-Neighbor Rule (NNR) is studied in this paper. Two sets of vibration data, one set of strain data, and three sets of temperature data are used to classify a running gear under normal condition and a running gear with simulated crack teeth. Feature level data fusion is used to test the classification performance of simple but less effective features to study the fusion effects. The results show high performance of strain features, high quality of the classifier and obvious fusion effect which increases the classification performance.

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