This paper proposes a classification system for damage detection that combines the discrete wavelet transform, a statistical methodology, and neural networks. In the proposed system, vibration signals of compressors are acquired as initial system inputs. The condition features are extracted by statistical moments of wavelet coefficients before entering the networks. The proposed on-line classification system can automatically classify a normal or faulty compressor. The system has been implemented to increase product reliability and reduce downtime for a large production facility.
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