【摘要】：礦石破碎是露天礦采礦生產(chǎn)工藝的重要環(huán)節(jié),旋回式破碎機(jī)作為露天礦進(jìn)行破碎的大型設(shè)備,其故障的診斷通常較為復(fù)雜,難以利用主觀經(jīng)驗進(jìn)行精確及快速診斷。針對旋回式破碎設(shè)備的故障診斷問題,利用傳感器實時采集數(shù)據(jù),基于改進(jìn)后BP神經(jīng)網(wǎng)絡(luò)構(gòu)建故障診斷模型,以偏心套故障、軸承磨損、平行軸缺油、平行軸油溫異常作為故障類型,以回流油溫、潤滑油油壓、軸承振動頻率、軸承轉(zhuǎn)速作為故障特征參數(shù),利用已知故障類型和故障樣本數(shù)據(jù)對BP神經(jīng)網(wǎng)絡(luò)故障診斷模型進(jìn)行了訓(xùn)練和優(yōu)化,最后通過測試數(shù)據(jù)對優(yōu)化后的旋回式破碎機(jī)故障診斷模型進(jìn)行了驗證。結(jié)果表明:基于BP神經(jīng)網(wǎng)絡(luò)的故障診斷模型能夠?qū)π厥狡扑闄C(jī)故障狀態(tài)實時地做出有效判斷,實現(xiàn)以預(yù)防為主的故障診斷方式,滿足了露天礦大型旋回式破碎機(jī)的故障診斷的需求。
[Abstract]:Ore crushing is an important link in the mining production technology of open pit mine. As a large equipment for crushing open pit mine, the fault diagnosis of rotary crusher is usually complex, so it is difficult to make accurate and rapid diagnosis by using subjective experience. In order to solve the problem of fault diagnosis of circular crushing equipment, the fault diagnosis model is constructed based on the improved BP neural network by using the sensor to collect the data in real time, and the fault diagnosis model is constructed with eccentric sleeve fault, bearing wear and oil shortage of parallel shaft. The abnormal oil temperature of parallel shaft is taken as the fault type, and the return oil temperature, lubricating oil pressure, bearing vibration frequency and bearing speed are taken as the fault characteristic parameters. The fault diagnosis model of BP neural network is trained and optimized by using known fault types and fault sample data. Finally, the optimized fault diagnosis model of rotary crusher is verified by test data. The results show that the fault diagnosis model based on BP neural network can effectively judge the fault state of circular crusher in real time, and realize the fault diagnosis mode based on prevention. It meets the requirements of fault diagnosis of large cycle crusher in open pit mine.
【作者單位】： 西安建筑科技大學(xué)管理學(xué)院;中鋼集團(tuán)礦業(yè)開發(fā)有限公司;
【基金】：國家自然科學(xué)基金項目(編號:51774228,51404182) 陜西省自然科學(xué)基金項目(編號:2017JM5043) 陜西省教育廳專項科研計劃項目(編號:17JK0425)
【分類號】：TD451

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