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SUPERVISED FAULT LEARNING USING RULE-GENERATED SAMPLES FOR MACHINE CONDITION MONITORING
SUPERVISED FAULT LEARNING USING RULE-GENERATED SAMPLES FOR MACHINE CONDITION MONITORING
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机译:使用规则生成的样本进行机器状态监测的监督性故障学习
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摘要
A machine fault diagnosis system is provided. The system combines a rule-based predictive maintenance strategy with a machine learning system. A simple set of rules defined manually by human experts is used to generate artificial training feature vectors to portray machine fault conditions for which only a few real data points are available. Those artificial training feature vectors are combined with real training feature vectors and the combined set is used to train a supervised pattern recognition algorithm such as support vector machines. The resulting decision boundary closely approximates the underlying real separation boundary between the fault and normal conditions.
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