林业机械与木工设备
林業機械與木工設備
임업궤계여목공설비
FORESTRY MACHINERY & WOODWORKING EQUIPMENT
2012年
4期
32-34
,共3页
小波神经网络%柴油机%故障诊断
小波神經網絡%柴油機%故障診斷
소파신경망락%시유궤%고장진단
wavelet neural network%diesel engine%fault diagnosis
通过建立带动量项小波神经网络理论模型,对柴油机进行故障诊断,并将其与传统的神经网络方法进行比较。实例对比分析表明该方法泛化能力强、准确度高、容错性强,还能在一定范围内诊断出柴油机从未出现过的故障,使故障诊断智能化和简单化。仿真结果表明,该方法用于柴油机常见故障诊断和预测有效、可行。
通過建立帶動量項小波神經網絡理論模型,對柴油機進行故障診斷,併將其與傳統的神經網絡方法進行比較。實例對比分析錶明該方法汎化能力彊、準確度高、容錯性彊,還能在一定範圍內診斷齣柴油機從未齣現過的故障,使故障診斷智能化和簡單化。倣真結果錶明,該方法用于柴油機常見故障診斷和預測有效、可行。
통과건립대동량항소파신경망락이론모형,대시유궤진행고장진단,병장기여전통적신경망락방법진행비교。실례대비분석표명해방법범화능력강、준학도고、용착성강,환능재일정범위내진단출시유궤종미출현과적고장,사고장진단지능화화간단화。방진결과표명,해방법용우시유궤상견고장진단화예측유효、가행。
Through the establishment of the theoretical model of a wavelet neural network with momentum item, the fault diagnosis of diesel engines is conducted, with comparison with traditional neural network methods made. Comparative analysis of examples shows that this method features strong generalization ability, high accuracy and strong fault tolerance and that it can diagnose faults that have never occurred before within a certain range to make fault diagnosis more intelligent and simplistic. The simulation results show that this method is a feasible one that can be used to diagnose and predict common faults of diesel engines.
