Expulsion identification is of significance for welding quality assessment and control in resistance spot welding. In order to improve the identification accuracy, a novel wavelet decomposition and Back Propagation (BP) neural networks with the peak-to-peak amplitude and the kurtosis index were proposed to identify the expulsion from electrode force sensing signals. The rapid step impulse and resultant damping vibration of electrode force was determined as a robust indication of expulsion, and this feature was extracted from the electrode force waveform by seven-layer wavelet decomposition with Daubechies5 wavelets. Then, the energy distribution proportion of the decomposed detail signals were calculated, and the highest-energy one was selected as the target signal. Two statistical indexes were introduced in this paper to measure the target signal in overall situation and volatility. The bigger the peak-to-peak amplitude is, the more violent the fluctuation is. Moreover, the higher the kurtosis index is, the stronger the impact is, and the lower the dispersion degree of the data is. Experimental analysis showed that neither the peak-to-peak amplitude nor the kurtosis index could accurately judge the expulsion defect individually, because of the early signal fluctuation, likely affected by the work-piece clamping, work-piece clearance, or the oxide film thickness. Therefore, the BP neural networks were introduced to identify the expulsion defects, which is a mature and stable non-linear pattern recognition method. Testing experiments presented good results with the trained networks and improved the evaluable accuracy effectively in the quality assessment of the resistance spot welding.

This paper presents a new method for a composite dictionary matching pursuit algorithm, which is applied to vibration sensor signal feature extraction and fault diagnosis of a gearbox. Three advantages are highlighted in the new method. First, the composite dictionary in the algorithm has been changed from multi-atom matching to single-atom matching. Compared to non-composite dictionary single-atom matching, the original composite dictionary multi-atom matching pursuit (CD-MaMP) algorithm can achieve noise reduction in the reconstruction stage, but it cannot dramatically reduce the computational cost and improve the efficiency in the decomposition stage. Therefore, the optimized composite dictionary single-atom matching algorithm (CD-SaMP) is proposed. Second, the termination condition of iteration based on the attenuation coefficient is put forward to improve the sparsity and efficiency of the algorithm, which adjusts the parameters of the termination condition constantly in the process of decomposition to avoid noise. Third, composite dictionaries are enriched with the modulation dictionary, which is one of the important structural characteristics of gear fault signals. Meanwhile, the termination condition of iteration settings, sub-feature dictionary selections and operation efficiency between CD-MaMP and CD-SaMP are discussed, aiming at gear simulation vibration signals with noise. The simulation sensor-based vibration signal results show that the termination condition of iteration based on the attenuation coefficient enhances decomposition sparsity greatly and achieves a good effect of noise reduction. Furthermore, the modulation dictionary achieves a better matching effect compared to the Fourier dictionary, and CD-SaMP has a great advantage of sparsity and efficiency compared with the CD-MaMP. The sensor-based vibration signals measured from practical engineering gearbox analyses have further shown that the CD-SaMP decomposition and reconstruction algorithm is feasible and effective.