基于深度置信网络的齿轮箱智能诊断方法

针对油田现场强背景噪声干扰下,难以实现齿轮箱故障精确诊断的问题,提出基于深度置信网络(Deep Belief Network,DBN)的齿轮箱智能诊断方法。首先运用变分模态分解(Variational Mode Decomposition,VMD)对齿轮箱振动信号分别进行分解；然后依据互相关准则对小于阈值的模态运用最大相关峭度解卷积(Maximum Correlated Kurtosis Deconvolution,MCKD)进行降噪滤波处理,并对降噪后的信号进行重构；最后构造故障特征集,实现基于DBN的故障特征自适应挖掘与故障模式智能识别。对现场的齿轮箱故障诊断表明,本文提出的方法具有自适应性,能显著提高故障分类准确率,为保障油田设备安全可靠运行提供了依据。

Gearbox Intelligent Diagnosis Method Based on Deep Belief Network

Aiming at the difficulty to accurately extract the gearbox fault features under strong background noise in oilfield, an intelligent diagnosis method of gearbox faults based on deep belief network (DBN) was proposed. Firstly, the vibration signals of gearbox were decomposed by variational mode decomposition (VMD). Then, according to the cross-correlation criterion, the components smaller than the threshold were denoised and filtered by maximum correlation kurtosis deconvolution (MCKD), and the denoised signals were reconstructed. Finally, the fault feature sets were constructed to realize adaptive fault feature mining and intelligent fault pattern recognition based on DBN. The gearbox diagnosis in oilfield shows that the proposed method has self-adaptability, significantly improves the accuracy of fault classification, and provides a basis for ensuring the safe and reliable operation of oilfield equipments.