利用神經網絡技術建立信息融合中心, 對多傳感器數據進行融合處理, 通過多源互補信息減小故障診斷系統的不確定性。文中討論了神經網絡多傳感器信息融合方法中數據預處理與特征提取、特征向量維數壓縮與關聯、歸一化處理方法等, 同時, 對神經網絡的構造以及學習訓練等內容, 也作了較為詳細的討論。通過對柴油機振動監測數據、燃油壓力波動信息、以及兩者融合信息的故障診斷性能的比較, 表明神經網絡多傳感器信息融合方法用于復雜機械的故障診斷是可行和有效的。
關　鍵　詞: 多傳感器; 信息融合; 神經網絡; 故障診斷
Abstract: Th is paper deals w ith a mult isenso r system app lied to mechanical fault diagno sis. By using the info rmat ion fusion pat tern based on neural netwo rk, the uncertainty of the diagno sis system can be decreased great ly. The methods fo r data p rep rocessing, features ext ract ion and const ruct ion of the neural netwo rk are discussed. The app roach is show n to be p ract ical and effect ive th rough diagno sing the faults in the fuel inject ion system of a diesel engine.
Key words:M ult isenso r; Info rmat ion fusion;N eural netwo rk; Fault diagno sis