混合信号振声源分离与多机组故障诊断

针对机房设备混合信号难以提取有用信息,提出了多参数的振声诊断方法。应用最小互信息梯度下降的盲分离算法,通过展开边缘熵和修正四阶累积量估计值的方法改善算法性能,在故障源数量未知且可能大于传感器数量的情况下,根据信息源之间的独立性测度关系依次提取最显著的特征值。仿真结果证明,改进算法估计误差减小且算法可靠。在诊断实例中,首先,分离机房内的混合噪声信号以确定主要故障来源;然后,采集故障源的振动信号进行非线性盲分离,提取热泵机组压缩机不对中、齿轮啮合不良和碰磨的故障特征;最后,根据分离的振源信号特征识别故障类型,建立基于盲源分离算法的大空间设备群的振声诊断方法。

Separation for Vibration and Acoustic Compound Signals and Multi-unit Fault Diagnosis

A diagnostic method based on vibration and acoustic signals is presented in order to resolve the problem of extraction and separation for the compound signals in an equipment room. The minimum mutually information gradient descent method is applied based on blind separation algorithm, and the algorithm performance is improved through the revision of fourth-order cumulative estimating value. When the quantity of fault sources is unkown, or more than the quantity of sensors, the most remarkable characteristics are extracted according to the independence relation between the information sources. The simultation product is proved that improved algorithm is reliable because the estimation error is reduced. In the diagnosis example, the acoustic signals are firstly separated in an equipment room to determine the major failure sources. And then vibration signals collected from these fault sources are processed based on non-linear blind separation model. The fault feathers are extracted including misalign, meshing failure and wearing fault from compressor of the heat pump. Finally, the fault types are recognized according to the separated signal features from vibration and accoustic signals. And the vibration and accoustic diagnosis method is established for multi-units in the big space based on blind source separation algorithm.