基于电荷耦合器件(CCD)的火花光谱仪是一种用于元素成分分析的光谱仪, 其输出信号是高频的CCD有效信号和低频的背景噪声叠加在一起的复合信号, 火花光谱的有效信息主要集中在信号的较高频段, 很容易被背景噪声淹没和干扰, 因此获取完整有效的光谱信息, 需要对信号进行有效处理。 经验模态分解(EMD)方法可以自适应分析信号, 不需要设置参数, 但存在模态混叠的问题, 信号中不同频率的成分可能会混淆; 集合平均经验模态分解(EEMD)成功地解决了EMD方法中模态混叠的问题, 能更加清晰地将信号中的不同频率成分分解出来, 因此更加适合光谱信号的研究。 使用火花光谱仪对不锈钢标准样品(选取短波段、 中波段和长波段代表性元素碳C、 锰Mn、 镍Ni、 铬Cr和铝Al)进行采集, 获得了标准样品的火花光谱原始信号。 通过EEMD方法进行自适应的分析和处理, 每个CCD信号均获得了11阶固有模态函数(IMF), 根据信号的幅频特性, IMF1-IMF2表征为特征信号部分, 最后一阶IMF11为背景噪声成分。 通过重构上述处理信号, 结合基于连续小波变换的惩罚最小二乘法进行了二次处理, 获得了最终处理后的信号。 将处理后的信号导入仪器处理软件中, 获得了碳、 锰、 镍、 铬和铝元素的含量梯度曲线, 结果显示采用EEMD方法处理的信号和原处理方法效果相当, 但省去了额外采集空白噪声段的环节, 大大节省了分析的时间, 从而提高了仪器的运行效率。

The spark spectrometer based on Charge Coupled Device (CCD) is a kind of spectrometer which is used for element composition analysis. Its output signal is a composite signal of high-frequency CCD effective signal and low-frequency background noise. The effective information of spark spectrum is mainly concentrated in the higher frequency band of signal, which is easy to be submerged and interfered by background noise. Therefore, in order to obtain complete and effective spectral information, it requires effective signal processing. Empirical Mode Decomposition (EMD) method can analyze signals adaptively without setting parameters, but there is the problem of mode mixing, and the components of different frequencies in the signal may be confused; Ensemble Empirical Mode Decomposition (EEMD) successfully solves the problem of mode mixing in EMD method, It can more clearly decompose the different frequency components in signal, so it is more suitable for processing spark spectrum signal with dispersive frequency components. In this paper, the spark spectrometer is used to excite and collect the stainless steel standard samples (Carbon C, Manganese Mn, Nickel Ni, Chromium Cr and Aluminum Al, which are representative elements in the short, medium and long band), and the original spark spectrum signals of the standard samples are obtained. Through the adaptive analysis and processing of EEMD method, each CCD signal is obtained 11 order Intrinsic Mode Function (IMF). According to the amplitude and frequency characteristics of the signal, IMF1-IMF2 is characterized as the high-frequency characteristic signal component, and the last IMF11 is the low-frequency background noise component. By reconstructing the processed signal and combining with the continuous wavelet transform-penalizedleast squares, the final processed signal is obtained. The processed signal is introduced into the instrument data processing software, and the content gradient curve of Carbon, Manganese, Nickel, Chromium and Aluminum elements is obtained. The results show that the signal processed by EEMD method is equivalent to the original instrument processing method, but the additional link of collecting blank noise section is omitted, and the analysis time is largely saved, so the operation efficiency of the instrument is improved.