RESEARCH PAPER
Integrating advanced measurement and signal processing for reliability decision-making
| 1 | Lublin University of Technology, Faculty of Management, ul. Nadbystrzycka 38, 20-618 Lublin, Poland |
| 2 | Rzeszow University of Technology, Faculty of Mechanical Engineering and Aeronautics, ul. Powstańców Warszawy 8, 35-959, Rzeszów, Poland |
| 3 | Lublin University of Technology, Mechanical Engineering Faculty, ul. Nadbystrzycka 36, 20-618 Lublin, Poland |
| 4 | Rzeszow University of Technology, Faculty of Electrical and Computer Engineering, ul. W. Pola 2, 35-959 Rzeszów, Poland |
Publication date: 2021-12-31
Eksploatacja i Niezawodność – Maintenance and Reliability 2021;23(4):777–787
HIGHLIGHTS
- Force and torque sensors analysed as an alternative to the vibration measurement.
- Effective condition prediction when integrated with adequate signal processing.
- Decision trees with various types of wavelets selected for predictive models.
- High accuracy method proposed to trace tool condition in real-time.
KEYWORDS
ABSTRACT
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.
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