# Improving Deep Learning Anomaly Diagnostics with a Physics-Based Simulation Model
## 5 Discussion  
This study proposed a SEAD method, with the benefits of utilizing the simulation model not only intended for the detection anomalies but also for the improvement of failure classification capability of a deep learning algorithm to diagnose identified deviating behavior in the manufacturing process. The simulation model was successfully utilized to detect variances in torque performance, thus connecting the machine behavior with the recognized anomalistic symptoms. Following the failure signal detection, the model-based approach was used to create a normal operational baseline to differentiate torque-origin symptoms in the machine's behavior during the support-element failure. Subsequently, the simulation model was utilized to create additional failure data for the training of the Long Short-Term Memory (LSTM) autoencoder to overcome a data scarcity problem. The Comparative validation demonstrated a notably higher level of success in the model's classification capabilities when simulated data was incorporated. The evaluation metric of the area under the curve (AUC) increased from the baseline of 0.774 to 0.928 with the inclusion of the simulated data. Thus, this study has empirically demonstrated that the employment of simulated models offers a robust and scalable approach for amplifying identified anomalistic symptom behavior, consequently augmenting the classification accuracy of the deep neural network. By following the SEAD method stages, including anomalistic behavior identification and operational data acquisition, it appears that physics-based modeling with deep neural network integration may result in more accurate and reliable anomaly detection and diagnostics. This is particularly pertinent in the peripheral milling context, where reliable system performance is paramount and unplanned production stops are not tolerated. The use of simulated models to amplify anomalistic symptoms, as demonstrated in this study, provides an excellent tool for enhancing the precision of deep neural network classification and averting costly unplanned production stops. Furthermore, the implementation of SEAD is feasible in real-life scenarios, for instance, as an edge-computing component or as an embedded element in machine automation systems. The SEAD method presented in this study may be generalized to encompass peripheral milling machines by extending its current failure diagnostic function. In future research,  
Figure 10: Illustration of the classification result improvement from Diagnostic validation results (**a**,**b**) to Comparative validation (**c**,**d**). the scope of the simulation model should be extended to include other failure mechanisms by creating a more comprehensive failure library for classification algorithms to identify different types of deviating behavior. This presents an opportunity for collaborative efforts and knowledge sharing within the research community. While this study suggests the development of such a library, it is essential to consider potential initiatives for open-sourcing these resources. The creation of open-source efforts related to the milling machine failure library would not only enhance available prediction capabilities but would also foster collaboration and accelerate advancements in the field. However, to apply SEAD across various machinery or industrial processes, it is essential to meticulously analyze the distinctive characteristics and failure modes inherent in each system. The individual nature of various anomalies may include complex characteristics associated with the deviating behavior, thus limiting the physical modeling of a comprehensive failure library. Despite this, the use of the SEAD certainly has the potential to diagnose various peripheral milling machine failures more robustly and accurately, thereby facilitating earlier detection and more effective maintenance of any potential malfunctions that are generally represented by physical modeling and simulations.