###### Abstract  
In the era of Industry 3.0, product fault detection systems became important auxiliary systems for factories. These systems efficiently monitor product quality, and as such, substantial amounts of capital were invested in their development. However, with the arrival of Industry 4.0, high-volume low-mix production modes are gradually being replaced by low-volume high-mix production modes, reducing the applicability of existing systems. The extent of investment has prompted factories to seek upgrades to tailor existing systems to suit new production modes. In this paper, we propose an approach to upgrading based on the concept of transfer learning. The key elements are (1) using a framework with a basic model and an add-on model rather than fine-tuning parameters and (2) designing a radial basis function deep neural network (RBF-DNN) to extract important features to construct the basic and add-on models. The effectiveness of the proposed approach is verified using real-world data from a spring factory. Industry 4.0; fault detection systems; deep learning models Article Using Transfer Learning and Radial Basis Function Deep Neural Network Feature Extraction to Upgrade Existing Product Fault Detection Systems for Industry 4.0: A Case Study of a Spring Factory Article Using Transfer Learning and Radial Basis Function Deep Neural Network Feature Extraction to Upgrade Existing Product Fault Detection Systems for Industry 4.  
fault detection from scratch. These approaches increase the accuracy of fault detection by incorporating data fusion or hybrid recognition models [7; 8; 9; 10]. The third and fourth categories are tailored to the low-volume, high-mix production modes of Industry 4.0. Specifically, methods in the third category explore means of establishing highly accurate fault detection with little training data. [PERSON] et al. [11] and [PERSON] et al. [12] both emphasized the generalizability of this type of approach. However, these methods focus on rebuilding rather than upgrading, which is less cost-effective. The fourth category exploits the high-mix of new production modes, in which similar products of the same basic structure but slightly different details are manufactured in low volumes. For example, consider Figure 1. A clothes-hanger manufacturer will first develop a basic version and then modify it into several different products based on the needs of downstream vendors, such as bottoms of different lengths, notches in the top bars, or non-slip strips on the hanger. This last category of product fault detection depends on transfer learning. A system is first developed for the basic product, and then additional systems for new products are created by fine-tuning the parameters of the existing system. [PERSON] et al. [13], for instance, investigated the application of fuzzy logic and transfer learning to industrial environments without labeled data, while [PERSON] et al. [14] considered industrial robots working in different environments. The application of transfer learning to upgrade existing systems is favored by factories because it requires the least financial and time investments. However, this method still suffers from two major shortcomings. First, the formats of machine operating data may not be the same for similar products, thereby preventing the fine-tuning of existing systems. For example, in Figure 1, the basic version and the two modified products all need only a wire bending machine; however, the third product requires an additional machine to apply the non-slip strip. Thus, the formats for collecting data on the quality of this product differ from those of the other products. Parameter fine-tuning to upgrade the fault detection system is thus not possible. The second shortcoming is associated with how factories manage their fault detection systems. Generally speaking, once a factory establishes a fault detection system, details of the system will be kept in a database for future use.