Publications
Chosen filters:
Chosen filters:
A probabilistic novelty detection methodology based on the order-frequency spectral coherence KU Leuven
© Springer Nature Switzerland AG 2019. The purpose of this paper is to develop a methodology that utilises the order-frequency spectral coherence to detect novel (i.e. unobserved) second-order cyclostationary components. In the novelty detection methodology, a probabilistic model of the healthy data is utilised to detect, localise and trend novelties in the form of damage that manifest as second-order cyclostationary components in vibration ...
A fleet-wide approach for condition monitoring of similar machines using time-series clustering KU Leuven
© Springer Nature Switzerland AG 2019. The application of machine learning to fault diagnosis allows automated condition monitoring of machines, leading to reduced maintenance costs and increased machine availability. Traditional approaches train a machine learning algorithm to identify specific faults or operational settings. Therefore, these approaches cannot always cope with a dynamic industrial environment. However, an industrial ...
Cyclo-non-stationary based bearing diagnostics of planetary gearboxes KU Leuven
Condition monitoring of rotating machinery is a field of intensive research being closely related to the technological evolution in the area of energy, manufacturing and transport. The fault detection and diagnosis of planetary gearbox bearings present an interesting challenge even under steady operating conditions as often the relatively weak bearing signals are masked by other components such as the gear meshing. However, planetary gearboxes ...
Remaining Useful Life Prediction of Rolling Element Bearings based on Unscented Kalman Filter KU Leuven
© Springer Nature Switzerland AG 2019. A data-driven methodology is considered in this paper focusing towards the Remaining Useful Life (RUL) prediction. Firstly, diagnostic features are extracted from training data and an analytical function that best approximates the evolution of the fault is determined and used to learn the parameters of an Unscented Kalman Filter (UKF). UKF is based on the recursive estimation of the Classic Kalman Filter ...
KDamping: A stiffness based vibration absorption concept KU Leuven
The KDamper is a novel passive vibration isolation and damping concept, based essentially on the optimal combination of appropriate stiffness elements, which include a negative stiffness element. The KDamper concept does not require any reduction in the overall structural stiffness, thus overcoming the corresponding inherent disadvantage of the ‘‘Quazi Zero Stiffness’’ (QZS) isolators, which require a drastic reduction of the structure load ...
Application of Cyclo- Nonstationary Indicators for Bearing Monitoring Under Varying Operating Conditions KU Leuven
Condition monitoring assesses the operational health of rotating machinery, in order to provide early and accurate warning of potential failures such that preventative maintenance actions may be taken. To achieve this target, manufacturers start taking on the responsibilities of engine condition monitoring, by embedding health-monitoring systems within each engine unit and prompting maintenance actions when necessary. Several types of condition ...
Cyclostationary-based bearing diagnostics under electromagnetic interference KU Leuven
Rolling element bearings are common but crucial components of rotating machines, being the interface between stationary and rotating parts and are responsible for many machine failures and breakdowns. Condition monitoring of machinery using vibration analysis often provides insight and early detection of damage on the bearings. The most widespread method for detection of bearing faults is Envelope Analysis, where the raw signal is first filtered ...
A comparison of different features for discrepancy analysis-based bearing diagnostics KU Leuven
© Proceedings of ISMA 2018 - International Conference on Noise and Vibration Engineering and USD 2018 - International Conference on Uncertainty in Structural Dynamics. All rights reserved. Discrepancy analysis is a novelty detection technique which can be used for bearing diagnostics under varying speed conditions. In discrepancy analysis, features are extracted from the vibration signal and used with a model of the healthy features to generate ...
Cyclostationary-based tools for bearing diagnostics KU Leuven
© Proceedings of ISMA 2018 - International Conference on Noise and Vibration Engineering and USD 2018 - International Conference on Uncertainty in Structural Dynamics. All rights reserved. Rolling element bearings are among the most common components which lead to machinery breakdown. Lately, focus has been targeted to cyclostationary-based tools that show a good performance in describing and detecting the bearing vibration signatures and early ...