Introduction to Service Analytics Minitrack

N/

Towards predictive part quality and predictive maintenance in industrial machining - a data-driven approach

Programs such as Industry 4.0 and Internet of Things contain the promise of intelligent production with smart services . In fact, great advances have already been made in sensor technology and machine connectivity. Production plants continuously generate and communicate large amounts of data and have become cyber-physical systems . However, the task of gaining knowledge from these large amounts of data is still challenging. Data generated by numerical control (NC) and programmable logic controllers (NC) comes in a raw format that doesn’t allow the application of analytical methods directly. Extensive preprocessing and feature engineering has to be applied to structure this data for further analysis. An important application is the timely detection of deviations in the production process which allows immediate reactions and adjustments of production parameters or indicates the necessity of a predictive maintenance action. In our research, we aimed at the identification of special deviant behavior of a grinding machine based on NC data. One finding wast the distinguishing the warm-up program from regular production and the other to recognize imprecise identification of the grinding process window. Both tasks could be solved with extensive preprocessing of the raw data, appropriate feature extraction and feature reduction, and the subsequent application of a clustering algorithm

Analysis of a Debt Collection Process Using Bayesian Networks

Many companies rely on professional debt-collection agencies to handle their outstanding debts. These agencies conduct a debt collection process consisting of successive, escalating actions with the aim of getting a debtor to settle an overdue claim. The sequence of actions is administered by agents who often have to make decisions on a case-by-case basis. This requires understanding of complex data and making decisions under uncertainty. This decision-making process has hardly been investigated so far. We are proposing Bayesian networks as the analytical basis for a decision support system. Bayesian networks are strong in dealing with uncertainties. They can be used for both predicting the success of a case and making recommendations on actions. The evaluation shows that Bayesian networks have a very good predictive performance which gets even better as the process evolves. With this instrument, the agents can make better-informed decisions in the debt collection process