_applied sciences_  
_Article_  
**System-Level Fault Diagnosis for an Industrial Wafer Transfer Robot with Multi-Component Failure Modes**  
**[PERSON] 1\({}^{\copyright}\)**, [PERSON] 1\({}^{\copyright}\)**, [PERSON] 2, [PERSON] 3 and [PERSON] 4\({}^{\copyright}\)**  
induced by removing grease artificially. [PERSON] et al. [5] introduced a sliding window convolutional deformation autoencoder (SWCVAE) that can realize robot anomaly detection. While these papers have contributed significantly to the diagnoses of robot failures, their common limitation is that the object is a single component with binary fault conditions, which cannot capture the fault severity. Others, such as [6], diagnosed small, intermediate, and high gearbox backlash in a joint robot using DWT signal preprocessing and an ANN. [PERSON] et al. [7] proposed a method to diagnose the severity of control cable wire damage. [PERSON] et al. [8] diagnosed soft and hard pitting gears based on a critical information map (CIM). These studies improved diagnoses considering different fault severities, but studied only a single component, not multiple components. In fact, a robot is composed of several components, each of which has their own failure modes. The issue of multiple fault diagnosis (MFD) has been addressed by several authors. [PERSON] et al. [9] designed a level-based learning swarm optimizer-extreme learning machine (LLSO-ELM) model to further improve the generalization performance of ELM by combining it with LLSO, and classified a total of six failure modes for two components. [PERSON] et al. [10] diagnosed five failure modes for three components by combining the generalized frequency response function (GFRF) spectrum and a convolutional neural network (CNN). [PERSON] et al. [11] proposed an attitude-data-based sparse auto-encoder-support vector machine (SAE-SVM) approach to diagnose transmission faults, implemented with eight failure modes for multi-joint industrial robots. [PERSON] et al. [12] diagnosed two failure modes, the wear of gear teeth and bearings, using motor current signature analysis (MCSA) with discrete wavelet transform (DWT) to analyze the signals in the time-frequency domain. In those studies, the drawbacks are that the faults are assumed to be mutually exclusive and are considered in a binary manner, i.e., normal and fault. However, failure modes of components can occur interactively, which complicates the problem and may lead to misleading results unless this is accounted for. This is also addressed in the recent paper by [13], in which the same challenge is stated for the PHM of industrial robots. In fields other than robotics, [PERSON] et al. [14] proposed a methodology based on a multi-scale convolutional neural network-long short-term memory neural network (MSCNN-LSTMNet) to diagnose the failure of multi-cylinder diesel engines under interactive failure. Summing up the above literature on industrial robots, the common finding is that most of the studies have focused on the diagnosis at the component level with faults of binary or multiple severities, and with regard to a single or multiple components. As mentioned in the beginning, the degradation of each component affects the performance of the robot system, not in an additive, but in an interactive manner. This means that the interrelation between the system and components should be accounted for, which we call diagnosis at the system level. In fact, the degradation of a component cannot be defined on its own but should be defined as conditional on the failure of the system, meaning that as long as the system performs its function well, the components have not yet failed. Motivated by this issue, this paper proposes a procedure of how to conduct a system-level diagnosis for a system with multiple components. The method is demonstrated by applying it to a wafer transfer robot with two components, a harmonic drive (HD) and timing belt (TB), which are known as the most critical components responsible for robot failure [15].