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A new technique for an automated detection and diagnosis of rolling bearing faults is presented. The time-domain vibration signals of rolling bearings with different fault conditions are preprocessed using Laplace-wavelet transform for features’ extraction. The extracted features for wavelet transform coefficients in time and frequency domains are applied as input vectors to artificial neural networks (ANNs) for rolling bearing fault classification. The Laplace-Wavelet shape and the ANN classifier parameters are optimized using a genetic algorithm. To reduce the computation cost, decrease the size, and enhance the reliability of the ANN, only the predominant wavelet transform scales are selected for features’ extraction. The results for both real and simulated bearing vibration data show the effectiveness of the proposed technique for bearing condition identification with very high success rates using minimum input features.

Dimensional accuracy of workpart under machining is strongly influenced by the layout of the fixturing elements like locators and clamps. Setup or geometrical errors in locators result in overall machining error of the feature under consideration. Therefore it is necessary to ensure that the layout is optimized for the desired machining tolerance for a given deviation in the set up or geometry of the locator. Also, the locator layout should be capable of holding the workpart in a unique position during machining thus providing deterministic location. This paper proposes a Genetic Algorithm (GA) based optimization method to arrive at a layout of error containing locators for minimum machining error satisfying the tolerance requirements and providing deterministic location. A three dimensional workpiece under the 3-2-1 locating scheme is studied. Results indicate that by optimally placing the error containing locators the geometric error component of the machining error can be substantially reduced thus enabling compliance to overall dimensional requirements.

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