Abstract
Unforeseen machine tool component failures cause considerable losses. This study presents a new approach to unsupervised machine component condition identification. It uses test cycle data of machine components in healthy and various faulty conditions for modelling. The novelty in the approach consists of the time series representation as features, the filtering of the features for statistical significance, and the use of this feature representation to train a clustering model. The benefit in the proposed approach is its small engineering effort, the potential for automation, the small amount of data necessary for training and updating the model, and the potential to distinguish between multiple known and unknown conditions. Online measurements on machines in unknown conditions are performed to predict the component condition with the aid of the trained model. The approach was exemplarily tested and verified on different healthy and faulty states of a grinding machine axis. For the accurate classification of the component condition, different clustering algorithms were evaluated and compared. The proposed solution demonstrated encouraging results as it accurately classified the component condition. It requires little data, is straightforward to implement and update, and is able to precisely differentiate minor differences of faults in test cycle time series.
Highlights
Failures and unplanned maintenance of machine tools cause severe productivity losses
For the prediction of a time series sample of an unknown machine condition, the following steps are conducted: (1) the time series is split into the defined regions of interest (ROI), (2) the retained features of the model are selected and calculated, (3) the resulting features are normalized with the model scaler, and (4) the trained HDBSCAN model is applied to the unknown feature set
The commutation offset error in (c) was introduced by manipulating the encoder offset in the drive unit of the motor
Summary
Failures and unplanned maintenance of machine tools cause severe productivity losses. Based on this cleaned feature representation, previously recorded healthy or failure states can be grouped in clusters of their feature values This model, consisting of selected features and grouped clusters of different healthy and faulty conditions, serves for the further predictive assessment of machine components in unknown conditions. The novelty of the proposed approach lies within (i) the representation of time series for condition monitoring as features for clustering, (ii) the fact that raw values of selected features are used rather than e.g., principal component analysis (PCA), (iii) the detection of both formerly known and unknown conditions of a component, and (iv) the universal applicability of the approach to different natures (constant, controlled-constant and varying) and types (linear, rotatory) of components. This study focuses on the steps related to domain understanding, raw data collection, and emphasizes especially data cleaning and transformation, and model building and testing
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
