利用神經(jīng)網(wǎng)絡(luò)技術(shù)建立信息融合中心, 對多傳感器數(shù)據(jù)進行融合處理, 通過多源互補信息減小故障診斷系統(tǒng)的不確定性。文中討論了神經(jīng)網(wǎng)絡(luò)多傳感器信息融合方法中數(shù)據(jù)預(yù)處理與特征提取、特征向量維數(shù)壓縮與關(guān)聯(lián)、歸一化處理方法等, 同時, 對神經(jīng)網(wǎng)絡(luò)的構(gòu)造以及學習訓練等內(nèi)容, 也作了較為詳細的討論。通過對柴油機振動監(jiān)測數(shù)據(jù)、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(jīng)網(wǎng)絡(luò)多傳感器信息融合方法用于復(fù)雜機械的故障診斷是可行和有效的。
關(guān)　鍵　詞: 多傳感器; 信息融合; 神經(jīng)網(wǎng)絡(luò); 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