###### Abstract
However, if the fault detection system of a basic product is established using deep learning, then the fault detection of the modified product will likely be based on deep learning as well. In the case of production modes with high-mix products, the fault detection database will expand infinitely because it must store many deep learning models with similar structures but slightly different parameters; this will increase the factory's operating costs. Thus, while the concept of transfer learning for upgrading product fault detection systems hold promise, some issues must still be overcome. Figure 1: An example of applying the concept of transfer learning to a factory in the Industry 4.0 era. This study proposes two approaches to remedy these issues: (1) While previous methods have realized transfer learning using parameter fine-tuning, we propose creating an add-on model for the basic model. The basic model determines the quality of the basic product, whereas the add-on model assesses the quality of the parts where the new product differs. (2) Existing product fault detection systems use all possible features for modeling; we propose identifying key features that can present product faults before establishing the fault detection model. Figure 2a displays a schematic of the first approach. It does not alter any of the parameters in the basic model but rather corrects the output results of the basic model using the add-on model. In the example shown in Figure 2b, inputting the collected features \((x,y)\) and \((x,z)\) of the first item into the basic model and the add-on model, respectively, produces results in which the item is faultless and the basic model needs no calibrations. We can therefore determine that there is nothing wrong with the product corresponding to the first item. For the second item, inputting the same features into the two models produces results in which the product is faultless and the basic model needs calibrations. We can therefore determine that something is wrong with the product of the second item of data. Furthermore, with the proposed method, the fault detection models of different products can all be extended based on knowledge of the basic model, as shown in Figure 3.  
The proposed approaches overcome the shortcomings discussed above as follows. First, input features of the two models can be designed independently; the basic model only needs to consider the features of the basic product, and the add-on model only needs to consider the features that are unique to the modified product. This circumvents the problem of differing data formats. Next, the proposed approach does not adjust the parameters of the basic model but includes an add-on model, so the database will ultimately only  
Figure 3: Concept of extending the basic fault detection models to various modified products. Figure 2: Concept of basic and add-on models proposed in this work: (**a**) schematic, (**b**) example. contain one basic model and multiple add-on models. This is more efficient than deep learning, as while the basic model must consider a large quantity of information to detect faults in the basic product, the add-on models only consider variations. In other words, the proposed approach effectively reduces the number of model parameters stored in the database. Furthermore, the second approach only uses key features to establish the fault detection model. Existing systems include all features to help clarify the complex associations between machine features and product faults. However, inputting all features into the basic and add-on models not only greatly increases the size of the two models but, more importantly, causes the add-on model to learn information that the basic model already has, which increases training costs unnecessarily. This paper is the first to explore the inclusion of these approaches to upgrading product fault detection systems. We designed a radial basis function deep neural network (RBF-DNN) and identified key features through training and analysis of the RBF parameters.