Högskolan i Skövde

The health of a machine tool directly affects its ability to produce components with high precision. Therefore, monitoring and diagnosing early faults can enhance its reliability resulting in an improvement in manufacturing throughput and overall product quality. This paper concerns condition monitoring of the ballscrew drive, a machine tool component that transforms rotary motion of the drive shaft into linear motion of the work table along the guideways. The degradation of the ballscrew drive is often characterized by backlash, which results in imprecise linear motion and, therefore, affects the position of guideways during machining operations. Many physical characteristics of the ballscrew drive, such as required torque, viscous friction, and Coulomb friction, change with the degradation of the ballscrew during its lifetime. The paper proposes a condition monitoring methodology consisting of four main steps: data collection, data preprocessing and feature engineering, model building, and anomaly detection. The machine tool drive system is operated under no-load condition at regular intervals to capture health data using Siemens Analyze MyCondition instrumentation. Subsequently, the data is preprocessed and features are extracted from raw signals using the wavelet transform approach. The unsupervised machine learning technique, principal component analysis, is used to reduce the dimensionality of the dataset and find feature combinations that capture most of the variation in the data. Next, Hotelling’s T2 statistic is computed for each sample on a rolling basis, and anomalous behavior is detected based consistent deviations beyond the moving median of Hotelling’s T2 statistic. The proposed methodology is applied on condition monitoring data from a Swedish automotive manufacturer and the health assessments are validated against backlash measurements obtained from a different conditional monitoring test. This shows that the health status of a ballscrew can be derived directly from its physical characteristics.