# A Combined Anomaly and Trend Detection System for Industrial Robot Gear Condition Monitoring
## 4 Discussion  
The presented results highlight some interesting aspects that will be discussed in this section. We will justify our initial choice of models and highlight some aspects of the models' performance on the synthetic data. Then, we will explain the models' parameter choice and end with organisational thoughts regarding the integration of CATS in a real world production site.  
Figure 12: Results of selected anomaly detection models for accelerated wear test 1.  
Figure 13: Results of selected anomaly detection models for accelerated wear test 2.  
As emphasised in Section 1.1.2, a comprehensive comparison of AD and TD models is not feasible due to the high variety of existing models. Our motivation for selecting models from different categories as presented in [18] was to test how their underlying detection mechanisms cope with the different characteristics of time series. The fact that AD and TD models were found that detect the trends and anomalies in the accelerated wear test data reliably, strengthens the argument that the comparison of the selected models is sufficient for the application. From our point of view, the results of the AD model comparison based on synthetic data set 2 clearly highlights the limitations of anomaly detection models in general. High noise levels in the data make it difficult for such models to detect anomalies. Figure 14 shows a typical time series of this data set. Even as a human operator, it is difficult to identify the anomalies. However, from our experience, such extreme noise does not appear in the HI time series as shown in Figure 9 or Figure 13 for the accelerated wear tests. When deploying AD or TD models in real world applications, suitable model parameters must be chosen. For this, from our point of view, the parameters have to be configured for the individual robot considering the common trade-off between false alarms (higher FPR) and undetected faults (lower TPR). If no ideal anomaly or trend detection model can be used considering the ROC curves, this trade-off can be tackled by considering a maintenance score for an individual robot. This maintenance score can be influenced for example by the position of the robot in the production systems in respect of the distance to buffers or the effort required to exchange the robot. Other criteria could be the required calibration effort after the replacement or the response time of the maintenance team if a replacement is required. For robots with a higher maintenance score, model parameters with high TPR and higher FPR should be chosen. For robots with a lower maintenance score, model parameters with lower TPR and low FPR should be selected. This principle is also depicted in Figure 15. The reconfiguration of such models might also be required if the FPR or TPR do not meet the expected behaviour over time. Finally, the implications that the formulated assumptions in Section 2.1.1 yield must be discussed. To meet these assumptions, two aspects must be considered in a real world application. First of all, a measurement trajectory must be used for data acquisition so that the HI data is comparable and has a low noise level. Secondly, CATS must be extended by mechanisms to ensure that anomalies or trends in the HI data are only due to wear and not changing environmental conditions, new robot programs or faulty data acquisition systems.