## 5 Conclusions and Future Work  
It is crucial to diagnose the condition of industrial robots to prevent losses caused by downtime in manufacturing plants. In this paper, we proposed a methodology to conduct a system-level diagnosis for wafer transfer robots that consists of two critical components (harmonic drive and timing belt), the degradation of which leads to the failure of the system, characterized by end-effector vibration. The procedure is summarized as follows: First, informative features are extracted from the motor control signal through signal processing techniques which apply the WPD for feature extraction and the RPN formula of hybrid score for feature selection. Second, an ANN is established to model the relation between extracted features and the HI of components. The model achieves RMS errors of 0.0275 and 0.0902 for the HD and TB, for the HI in the range of 0 to 1. Third, GPR is applied to build the relationship between HIs of components and the system. Before construction, it is found that the interaction between the two components, the HD and TB, is significant: the F-value 5.38 in ANOVA for the interaction is similar in magnitude to 4.09 for the TB. Finally, the ANN and GPR models are applied to the new robot in operation, in which the current state of health of the two components, as well as the system performance, are estimated, and a maintenance strategy is determined by evaluating how well the system performance is restored by the replacement of each component. Using the example mentioned in Section 4.4 as an illustration, the HIs of the two components and the system are 0.62, 0.87, and 0.65. After the replacement of each component to a new one, the system HI is restored to 0.04 and 0.45, respectively, which indicates that replacing the HD leads to three times better system health. The contributions of the proposed methodology can be stated as follows: First, the authors have examined multiple components and their fault severities, which can account for the interaction relationship in degradation between the components and system. Second, motor control signals are exploited, which are acquired automatically during operation with no additional use of sensors. Third, a hybrid score and an RPN-based formula are applied to select the most appropriate features for the diagnosis in a more objective way. Last, but not least, a system-level approach is proposed, which evaluates the state of health of the components, conditional on the system performance, as opposed to the traditional component-level approach. Thanks to this, a proper maintenance policy can be developed to decide which component to replace and how well the system health is restored. However, several critical challenges remain in the implementation of this study: First, the model development requires costly and time-consuming experiments for fault seeding and operation for a variety of degradation conditions. In this study, only two components with three or four severity levels are considered, which have operated under 12 different conditions. The number of cases will increase exponentially for more components and severity levels, which requires an efficient strategy for the design of experiments. Second, in the fault seeding process, a good domain knowledge is critical, which includes the type of failure modes, ways of fault seeding, and the degree of degradation. Unless this is properly addressed, the result will be significantly different from those in the field, and hence, will be useless. In this study, the artificial faults are based on the literature on robot faults, which have, however, been empirical or intuitive. Third, the proposed model is developed based on a specific operation profile such as a simple round trip of a wafer at a certain speed. If the profile changes or becomes more complex, the resulting features and subsequent ANN and GPR models will be changed accordingly. Since we cannot do this every time the profile changes, a more general strategy is needed to address this issue.