首页|Data from Zhejiang University Update Knowledge in Technology (The DMF: Fault Dia gnosis of Diaphragm Pumps Based on Deep Learning and Multi-Source Information Fu sion)
Data from Zhejiang University Update Knowledge in Technology (The DMF: Fault Dia gnosis of Diaphragm Pumps Based on Deep Learning and Multi-Source Information Fu sion)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New study results on technology have b een published. According to news reporting out of Hangzhou, People's Republic of China, by NewsRx editors, research stated, "Effective fault diagnosis for diaph ragm pumps is crucial." Financial supporters for this research include Natural Science Foundation of Sha ndong Province; Open Foundation of State Key Laboratory of Compressor Technology . The news correspondents obtained a quote from the research from Zhejiang Univers ity: "This paper proposes a diaphragm pump fault diagnosis method based on deep learning and multi-source information fusion (DMF). The time-domain features, fr equency-domain features, and modulation features are extracted from the vibratio n signals from eight different positions. After feature enhancement and data pre processing, the features are input into auto encoders (AE), convolutional neural networks (CNN), and support vector machines (SVM) to obtain the diagnostic resu lts. The results indicate that the DMF method achieves a fault diagnosis accurac y of 99.98%, which is on average 9.09% higher than us ing a single diagnostic model."
Zhejiang UniversityHangzhouPeople's Republic of ChinaAsiaTechnology
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