## 1 Introduction  
With the booming development of miniaturization and integration of electronic devices, micro enameled wires have been increasingly used in the manufacture of various electronic products, such as electroacoustic devices, micro coils, micro delay and chip inductor et al. [1, 2]. The micro resistance spot welding (MRSW) of enameled wire to pad is a common production process, since the removal of insulation coating and the joining of wire to pad can be accomplished efficiently in a welding process. Because of advantages such as low cost, high efficiency and ease of automation, MRSW is an important micro joining technology for the manufacture of micro devices, which is also termed small-scale resistance spot welding [3, 4]. In view of the demand of reliable signal transmission in electronic components, especially the high fidelity requirement of electroacoustic devices in smartphones and earphones, it is important to ensure the quality of each MRSW weld. However, quality control still relies heavily on manual inspection, which requires experienced people to do it with great attention and still overlooks some defects. It is essential to realize on-line quality monitoring. Several studies have recently focused on the quality control of MRSW [4, 5, 6, 7, 8]. [PERSON] et al. [4] developed a weld quality evaluation model based on electrode voltage recognition for the MRSWof 0.4-mm-thick titanium alloy sheets. [PERSON] et al. [5] compared the quality assessment performance between back propagation neural network (BPNN) and probabilistic neural network (PNN) in MRSW, and it was concluded that BPNN was more appropriate for failure load estimation and PNN was more effective at quality classification. [PERSON] et al. [6] established regression equations between welding parameters and weld quality with response surface methodology for foil metal joining. [PERSON] et al. [7] designed multi-performance optimization for MRSW with a hybrid approach. [PERSON] et al. [8] optimized the welding parameters by Taguchi design to obtain low electrical contact resistance for joining a nickel tab to an inner aluminum casing in a cylindrical cell. However, despite the great demand in industry, there is a lack of investigation into quality monitoring in the MRSW for micro enameled wire joining. Quality assessment for resistance spot welding (RSW) has been studied extensively [9; 10]. Different process signals are analyzed for feature extraction, such as dynamic resistance [11], welding power [12], electrode force [13], electrode displacement [14] and multiple signals [15]. Dynamic resistance has wide application because of the low cost and convenient installation of sensors. In recent years, machine learning models have been found to be effective for predicting weld quality, including artificial network [16; 17], decision tree [18], random forest [19] and SVM [20]. The rapid development of machine learning technology provides effective decision support tools for weld quality evaluation. Despite comprehensive studies on the weld quality of MRSW and RSW, specific applications require further investigation. First, compared with RSW or MRSW of metal sheets, MRSW of enameled wire to pad is quite a different physical process and has rarely been explored before. Additionally, a class imbalance problem exists in the quality classification of MRSW in the production. This refers to data within which one or more of the classes (majority class) have a much greater number of samples than the others (minority class) [21]. As a result of quality control, abnormal welds are generally far fewer than normal welds in the manufacture of micro components by MRSW. Traditional classification models cannot provide good performance for class-imbalanced data, which requires imbalanced data processing methods like anomaly detection algorithms [21]. Based on an imbalanced dataset from industry, this work focuses on the quality monitoring for MRSW of micro enameled wire to pad.