Reciprocating machinery has compact and complex structures, many moving parts, and numerous vibration excitation sources. Impact signals caused by mechanical part faults can easily produce time–frequency coupling with multi-source impact signals from components normal movements. At the same time, variable operating conditions, such as different speed and load will lead to nonlinear changes in the time–frequency characteristics of all collected vibration signals. These problems make it difficult to extract fault features. In this study, a multi-impact time-domain adaptive decomposition method for multigroup signal under variable operating conditions was proposed to separate fault features from multi-source impact signals. Firstly, in order to reduce the information loss of decomposition and improve impact extraction integrity, a decomposition target was established to minimise the loss of a reconstructed source signal, the sum of the inner product of a sub-signal, and the amplitude moment of a sub-signal relative to an impact centre. A new bilateral adaptive decomposition window with three control parameters including window center, bilateral shape and peak is designed to adapt to the characteristics of an impact shape in a time-varying state. Aiming at solving the problems of noise interference and initial model parameters setting, residual energy spectrum is applied to adaptively estimate a noise spectrum distribution and a multi-impact time domain centre. Furthermore, with the aim of large amounts of sensors signal synchronous decomposition at variable operating conditions, a multigroup signal adaptive decomposition parameter optimisation scheme integrating a fully connected network and an ADAM algorithm is designed to considerably improve computational efficiency. Numerical simulations and engine test data are studied to show that the proposed method shortens calculation time on the basis of realising multi-impact adaptive decomposition. The average processing time of a single group signal is 8.6 s, and the average processing time of four groups of signal synchronization is 20.0 s, which is significantly faster than those of the existing time domain impact decomposition method.

中文翻译：

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可变工况下多组数据的往复机多冲击时域自适应分解方法


往复式机械结构紧凑复杂，运动部件多，振动激励源多。由机械部件故障引起的冲击信号很容易与来自组件正常运动的多源冲击信号产生时频耦合。同时，可变的工作条件，例如不同的速度和负载，将导致所有收集的振动信号的时频特性发生非线性变化。这些问题使得提取故障特征变得困难。在本研究中，提出了一种可变工况下多组信号的多冲击时域自适应分解方法，将故障特征与多源冲击信号分离。首先，为了减少分解的信息损失，提高撞击提取的完整性，建立了一个分解目标，以最小化重建的源信号、子信号的内积之和以及子信号相对于撞击中心的振幅矩的损失。设计了一种新的双边自适应分解窗口，具有窗口中心、双边形状和峰值三个控制参数，以适应时变状态下撞击形状的特性。针对噪声干扰和初始模型参数设置问题，应用残余能谱自适应估计噪声频谱分布和多影响时域中心。此外，为了实现可变工况下大量传感器信号同步分解，设计了一种集成全连接网络和 ADAM 算法的多组信号自适应分解参数优化方案，以显著提高计算效率。 数值仿真和发动机试验数据研究表明，所提方法在实现多冲击自适应分解的基础上缩短了计算时间。单组信号的平均处理时间为 8.6 s，四组信号同步的平均处理时间为 20.0 s，明显快于现有的时域冲击分解方法。