Data-based ensemble approach for semi-supervised anomaly detection in machine tool condition monitoring

Data-based methods are capable to monitor machine components. Approaches for semi-supervised anomaly detection are trained using sensor data that describe the normal state of machine components. Thus, such approaches are interesting for industrial practice, since sensor data do not have to be labeled in a time-consuming and costly way. In this work, an ensemble approach for semi-supervised anomaly detection is used to detect anomalies. It is shown that the ensemble approach is suitable for condition monitoring of ball screws. For the evaluation of the approach, a data set of a regular test cycle of a ball screw from automotive industry is used

Application of Mahalanobis-Taguchi System in Full Blood Count of Methadone Flexi Dispensing Program

Patient under methadone flexi dispensing (MFlex) program are required to do blood tests like full blood count (FBC). A doctor assesses 3 parameters like haemoglobin, platelet count, and fasting blood sugar to ensure the patient has FBC problem. Consequently, the existing system does not have a stable ecosystem towards classification and optimization. The objective is to apply Mahalanobis-Taguchi system (MTS) in the MFlex program. The data is collected at Bandar Pekan clinic with 34 parameters. Two types of MTS methods are used like RT-Method and T-Method for classification and optimization respectively. The average Mahalanobis distance (MD) of healthy is 1.0000 and unhealthy is 187.0555. Positive degree of contribution has 19 parameters. 15 unknown samples have been diagnosed. Type 5 of 6 modifications has been selected as the best proposed solution. In conclusion, a pharmacist from Bandar Pekan clinic confirmed that MTS able to solve problem in classification and optimization of MFlex program