Machine Learning-Based Unbalance Detection of a Rotating Shaft Using Vibration Data
| Autor: | Willi Neudeck, Olaf Enge-Rosenblatt, André Schneider, Oliver Mey |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Signal Processing (eess.SP)
FOS: Computer and information sciences 0209 industrial biotechnology Computer Science - Machine Learning Computer science 02 engineering and technology Machine learning computer.software_genre Convolutional neural network Fault detection and isolation Machine Learning (cs.LG) 020901 industrial engineering & automation 0202 electrical engineering electronic engineering information engineering Range (statistics) FOS: Electrical engineering electronic engineering information engineering Electrical Engineering and Systems Science - Signal Processing Hidden Markov model Artificial neural network business.industry Random forest Vibration 020201 artificial intelligence & image processing Artificial intelligence Reduction (mathematics) business computer |
| Zdroj: | ETFA |
| DOI: | 10.48550/arxiv.2005.12742 |
| Popis: | Fault detection at rotating machinery with the help of vibration sensors offers the possibility to detect damage to machines at an early stage and to prevent production downtimes by taking appropriate measures. The analysis of the vibration data using methods of machine learning promises a significant reduction in the associated analysis effort and a further improvement in diagnostic accuracy. Here we publish a dataset which is used as a basis for the development and evaluation of algorithms for unbalance detection. For this purpose, unbalances of various sizes were attached to a rotating shaft using a 3D-printed holder. In a speed range from approx. 630 RPM to 2330 RPM, three sensors were used to record vibrations on the rotating shaft at a sampling rate of 4096 values per second. A development and an evaluation dataset are available for each unbalance strength. Using the dataset recorded in this way, fully connected and convolutional neural networks, Hidden Markov Models and Random Forest classifications on the basis of automatically extracted time series features were tested. With a prediction accuracy of 98.6 % on the evaluation dataset, the best result could be achieved with a fully-connected neural network that receives the scaled FFT-transformed vibration data as input. Comment: Contribution at IEEE ETFA 2020 (25th International Conference on Emerging Technologies and Factory Automation, Vienna) |
| Databáze: | OpenAIRE |
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