基于信息融合的机械设备故障诊断算法设计与应用

为提高大型复杂机械设备故障诊断的准确率和效率,针对故障诊断的多源信息综合分析问题,研究设计了基于信息融合的故障诊断算法,并应用于机械设备监测与故障诊断系统中。算法采用BP神经网络和D-S(Dempster-Shafer)证据理论,实现了对故障模式可信度的合理调配和多源信息的有机融合。文章以某舰船柴油机作为故障诊断对象,诊断结果分析表明,算法在机械设备故障诊断方面具有较高的准确度。

Design and Application of Machinery Fault Diagnosis Algorithm Based on Data Fusion

To improve the accuracy and efficiency of fault diagnosis for large and complex machinery, an algorithm of fault diagnosis based on data fusion was designed processing the problem of comprehensive analysis on multi-source data. And it is applied to monitoring and fault diagnosis system of mechanical equipment. BP Neural Networks and D-S （Dempster-Shafer） Evidence theory was used to achieve intelligent fusion of multi-source data. A ship a ship reasonable allocation of the credibility of the fault modes and diesel engine was taken as the fault diagnosis object .The experiment shows that this algorithm has higher accuracy in fault diagnosis of mechanical equipment.