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.

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Today, an operator performs experiments to adaptively select grinding process parameters using observations, expert knowledge, and rules of thumb. Self-optimizing grinding machines cannot use operator observations and must therefore extract enough information out of the grinding process. In this study, a holistic sensor setup as foundation for self-optimizing machines are presented exemplarily for cup wheel grinding machines. In-process detection of grinding burn, based on temperature and gas measurements, is tested and compared. Afterwards, the influence of input variables such as feed rate and cutting speed on grinding cost, grinding burn, and surface roughness are investigated.

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