# A Combined Anomaly and Trend Detection System for Industrial Robot Gear Condition Monitoring
## 1 Introduction
### State of the Art
#### 1.1.1 Industrial Robot Condition Monitoring  
Different approaches for the CM of industrial robots exist in the literature. These can be classified by the type of model used, i.e., supervised or unsupervised machine learning models or the raw data used, which are mainly acceleration sensor data or robot controller data. In the field of supervised models and robot controller data, several models such as XGBoost and different neural networks based on both joint specific data such as speed and torque and operational specific data (e.g., number of emergency stops) were compared from a fleet of 6000 robots. A maximum AUC value (area under the curve) of 0.87 could be achieved for a neural network model for fault detection in axis 2 [4]. A similar model comparison for logistic regression, support vector machines, random forests and ensemble stacking was performed in [5]. Here, angle, angle speed, acceleration and torque data were used from 26 robots to classify gear faults. The best AUC value of 0.77 was reached by the random forest classifier. Fault detection for loose gear belts was performed with a decision tree, a gradient booster and a random forest and statistical features derived from current data. Here, the random forest performed best with F1-scores around 0.9 [6]. In the section of unsupervised models and robot controller data, a kernel density estimator was used to detect faults based on motor angle, angle velocity and torque in combination with the Kullbach-Leibler divergence. Data from accelerated wear tests show a clear increase in the health indicator [7]. In another publication, the transferability of models was investigated for a combination of principle component analysis and Q-residuals. Anomalies were assumed if the distance measure was above a set threshold. The study shows that the use of the differences between measured and set quantities such as torques as raw data perform best in terms of transferability. In this context, transferability describes the training of the model based on the data of only one robot and then also using this model for other robots [8]. A model based on the deviations of a dynamic equation of a robot relative to actual measurements of the robot is combined with [PERSON]'s T\({}^{2}\) test statistic to determine robot faults [9]. A sliding-window convolutional variational autoencoder was used to detect anomalies in pick-and-place operations of a robot simulated by little strikes on the robot. The method outperforms benchmark models with an F1-score of 0.89 [10]. A long short-term memory neural network was successfully used to detect anomalies within the grinding process of an industrial robot based on speed, position and torque data. Anomalies were generated by applying a force to the robot hand during the process [11]. Turning to supervised learning approaches based on acceleration sensor data, multiple methods are worthy of note. A sparse autoencoder was trained with data from an attitude sensor (collecting acceleration and velocity signals at 100 Hz) attached to the tool centre point of the robot. The sensor collected data from normal behaviour and different fault conditions such as pitting and broken teeth of a gear. The classification results showed accuracy values of 90 percent [12]. Wavelet-based features in combination with a neural network were used to classify backlash faults for a six axis industrial robot [13]. Multiple supervised models such as a support vector machine, neural networks, gaussian processes and random forests were combined with different dimensionality reduction methods based on data from acceleration sensors attached to the gear caps for gear fault classification. The SVM and GP showed the best performance with accuracy values over 91 percent [14]. In the area of unsupervised models and acceleration sensor data, a gaussian mixture model was used based on health indicators derived from time and the time-frequency domain to differentiate measurements from a degreased robot from normal measurements of the robot.