Abstract:
Assessment of present and future condition of industrial machinery is one of the core ideas that constitute Industry 4.0 paradigm. Predictive maintenance depends on integrated sensors and machine learning algorithms to achieve this assessment based on the internal parameters of machinery. This type of maintenance could save plant costs and improve efficiency while reducing fatal defects in machinery. It automates the maintenance process and reduces the number of periodic checks. Bearings are used in rotating machinery extensively. However, bearing faults are common and could cause time and financial loss if they occur unexpectedly. Machine learning could be used in predictive maintenance framework to predict the health status of a bearing. Bearing fault analysis research has been traditionally conducted on its vibration signature. Due to nature of data, each bearing should be modelled separately or machine learning algorithms should be robust against environment or different machinery settings. In the present work unsupervised novelty detection framework on streaming vi bration data is proposed. The framework is built in an unsupervised manner since each bearing is considered individually and building models for each of them is impractical. Since faulty samples are not available initially, novelty detection methods are applied on bearing degradation data. The results show that detection of bearing faults and other state changes can be made using novelty detection methods. Detection could be achieved earlier than conventional methods for some cases.
