Dariusz Mazurkiewicz

Nowadays, systems are more complex and require high reliability for their components, especially critical system components. Therefore, to avoid serious damage, system are often replaced before the actual failure. The replaced parts are considered to have “soft failure”, and the limit in which the parts are replaced is known as the critical level of the degradation process. Determining the appropriate value of the critical level for a product is an important problem in their exploitation, as well as for predicting the Mean Time to Failure (MTTF) or Remaining Useful Lifetime (RUL) of this product based on the degradation data by the mathematical models. In this article, an approach in determining the critical levels based on failure data from an accelerated test is introduced. This approach is applied with the degradation process of Light-Emitting Diodes (LED) in an accelerated test and a type of Wiener process-based model is used to predict the MTTF or RUL of LED based on their degradation data and the found critical level.

An advanced milling machine multi-sensor measurement system as a condition monitoring tool was presented. It was assumed that the data collected from the 3-axis force and torque sensor can be used as a new approach and an alternative to the typical vibration signal based health monitoring and remaining useful life prediction (RUL), when integrated with machine learning techniques that are regarded as a powerful solution. Measurement system integration with the proposed signal processing method based on decision trees with different types and levels of wavelets for the cutter reliability decision-making process was presented together with proving their ability to trace the tool condition accurately. Prediction errors achieved with the use of different signal sources and data processing methods were presented and compared.