Aiming at the issue of extracting the incipient single-fault and multi-fault of rotating machinery from the nonlinear and non-stationary vibration signals with a strong background noise, a new fault diagnosis method based on improved autoregressive-Minimum entropy deconvolution (improved AR-MED) and variational mode decomposition (VMD) is proposed. Due to the complexity of rotating machinery systems, the periodic transient impulses of single-fault and multiple-faults always emerge in the acquired vibration signals. The improved autoregressive minimum entropy deconvolution (AR-MED) technique can effectively deconvolve the influence of the background noise, which aims to enhance the peak value of the multiple transient impulses. Nevertheless, the envelope spectrum of simulation and experimental in this work shows that there are many interference components exist on both left and right of fault characteristic frequencies when the background noise is strong. To overcome this shortcoming, the VMD is thus applied to adaptively decompose the filtered output vibration signal into a number of quasi-orthogonal intrinsic modes so as to better detect the single- and multiple-faults via those sub-band signals. The experimental and engineering application results demonstrate that the proposed method dramatically sharpens the fault characteristic frequencies (FCFs) from the impacts of bearing outer race and gearbox faults compared to the traditional methods, which show a significant improvement in early incipient faults of rotating machinery.

Machining-induced residual stress distribution is strongly influenced by the machining process condition, tool geometry and workpiece material mechanical properties. The high temperature, large strain and high strain rate environment will promote the material micro-structural attribute changes. The material micro-structural attribute changes could directly affect the material mechanical properties. An analytical model is proposed for the residual stress prediction in the orthogonal turning by considering the material dynamical recrystallization induced grain growth effect. The grain size effect on the material flow stress behavior is included by adding a grain size dependent term into the traditional Johnson–Cook model. The Johnson–Mehl–Avrami–Kolmogorov model calculates the recrystallized volume fraction and grain size as a function strain, strain rate and time. The average grain size is calculated with a rule of mixture by volume. Then the modified Johnson–Cook model is embedded into a classic residual stress prediction model for the machining induced residual stress profile prediction on the machined workpiece surface. Experimental tests are conducted for the model validation. The predicted residual stress shows good approximation with the measurement in both the trend and magnitude of the residual stress. Also, the effects of cutting speed and feed rate on the residual stress profile are investigated.