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The local mean decomposition (LMD) is a new method of adaptive time-frequency analysis, which was proposed by Jonathan S. Smith and applied to the analysis of electroencephalogram and achieved a desired result (Smith, 2005). The LMD method decomposes a complex multi-component signal into series of Product Functions (PFs), and the instantaneous frequency of each PF has physical meaning. Each PF is the product of an envelope signal and a frequency modulated signal, the envelope signal is instantaneous amplitude of the PF, and the instantaneous frequency can be derived from the frequency modulated signal. Combining the envelope and the instantaneous frequency of all PFs, a time-frequency distribution of the original signal can be obtained. The PFs decomposed by LMD method are actually simple component amplitude and frequency modulated signals, which contain different frequencies of the original signal. The LMD method is very suitable for the analysis of non-stationary signals, which essentially decomposes a multi component non-stationary signal into a series of simple component stationary signals (Cheng, et al., 2009 ). The arc electrical signals in square wave alternating current submerged arc welding contains rich welding quality information, which is a common source signal in welding quality monitoring. The welding arc current and voltage signals has characteristics of short circle and high frequency with reasonable physical meaning, which are easy to be collected and also directly related to the welding quality. Reference (Li, et al., 2000) realized the identification of welding defects through arc sensing, which can be used in the on-line monitoring of welding process. Reference (Li, et al., 2012) and reference (He, et al., 2013a) used the wavelet energy entropy and Lyapunov exponent as quantitative index to realize the evaluation of the arc stability based on welding current signals in submerged arc welding. Because of the uncertainty influencing factors of the welding quality and complex noise background during the collection process, the actually collected welding arc signal has the characteristic of nonlinear and time-varying, which is a typical non-stationary signal (He, et al., 2012, He, et al., 2013b, He, et al., 2016). Therefore, the local mean decomposition is

Arc signal analysis of square wave alternating current submerged arc welding using local mean decomposition