## 1 Introduction  
### Problem Statements  
With the rapid development of manufacturing industry and scientific technology, meeting customer's diverse demands and improving service quality have become the main concern when manufacturing enterprises adjust their strategies [1]. Therefore, quality becomes one of the key factors in determining the core competitiveness of a manufacturing enterprise. More and more enterprises choose to take advanced quality monitoring and management technologies or methods to improve product quality for meeting the demands of customers, such as quality evaluation [2; 3], quality improvement [4], and quality diagnosis [5]. Almost all of the quality information generated from the manufacturing process, thus the quality control of the manufacturing process becomes an effective method and key policy for enterprises to improve the quality of products [6]. Quality defects diagnosis is one of the quality control methods that can effectively deal with the quality problems of products in the manufacturing process, and it has aroused more and more attention among the academic community [5; 7; 8]. Different from other quality control methods that include quality inspection, quality evaluation, and quality evolution prediction during the manufacturing process, quality defects diagnosis can not only find out the causes of defects in time but can also optimize manufacturing environment and avoid subsequent product quality problems, thus can enhance product quality. Make-to-order is the common mode for large equipment enterprises [5]; some diagnosis methods of quality defects for the manufacturing process of products have been developed. In previous studies, case-based reasoning (CBR), mathematical models, and artificial intelligence methods were mainly applied. For instance, several CBR-based methods were proposed [9; 10] for the diagnosis of cam grinding's contour error and the fault of loaders. The typical mathematical models and artificial intelligence methods that include statistical control methods, rough set, fuzzy theory, and neural network were developed. For example, [PERSON] et al. [11] proposed a new dynamic partial least squares (PLS) model to deal with the quality-related fault diagnosis issue for dynamic processes. [PERSON] et al. [12] proposed a framework for the quality-based fault detection and diagnosis of nonlinear batch processes in the multimode operating environment. To solve the problem of multi-causes process quality diagnosis in manufacturing, [PERSON] et al. [13] developed an algorithm based on the binary tree of quality cause set and intermediate quality. In those methods, statistical control and mathematical models are the main approaches. Aiming at the problem of the diagnosis of dimensional failures, [PERSON] et al. [14] proposed a rough set-based fault diagnosis method. [PERSON] et al. [15] and [PERSON] et al. [16] introduced fuzzy theory to quality management and diagnosis, and improved enterprises' quality control levels. Focusing on the problem of monitoring the quality of the small scale resistance spot welding of titanium alloy, [PERSON] et al. [17] proposed different neural network models for weld quality prediction. [PERSON] et al. [18] introduced a new fault diagnosis method for the multifunctional spoiler (MFS) by using artificial neural networks and discrete wavelet transform. In addition, some hybrid methods were developed. For instance, [PERSON] et al. [19] proposed a hybrid rough sets and support vector machine method to deal with the issue of the fault diagnosis for the hydroelectric generator unit. To address the problem of the fault diagnosis of electronic systems, [PERSON] et al. [20] developed a hybrid dependency matrix diagnosis and fuzzy diagnosis method. [PERSON] et al. [21] proposed a hybrid method based on ontology and signal analysis to solve the problem of the fault diagnosis of mechanical components. The methods mentioned above can effectively solve a large number of quality diagnosis problems.