Topic
Condition monitoring
About: Condition monitoring is a research topic. Over the lifetime, 13911 publications have been published within this topic receiving 201649 citations.
Papers published on a yearly basis
Papers
More filters
TL;DR: A classifier with a deep architecture that consists of a stacked autoencoder (SAE) and a support vector machine is proposed for gearbox fault classification using extracted fault features.
Abstract: Fault diagnosis of drivetrain gearboxes is a prominent challenge in wind turbine condition monitoring. Many machine learning algorithms have been applied to gearbox fault diagnosis. However, many of the current machine learning algorithms did not provide satisfactory fault diagnosis results due to their shallow architectures. Recently, a class of machine learning models with deep architectures called deep learning has received more attention, because it can learn high-level features of inputs. This paper proposes a new fault diagnosis method for the drivetrain gearboxes of the wind turbines equipped with doubly-fed induction generators (DFIGs) using DFIG rotor current signal analysis. In the proposed method, the instantaneous fundamental frequency of the rotor current signal is first estimated to obtain the instantaneous shaft rotating frequency. Then, the Hilbert transform is used to demodulate the rotor current signal to obtain its envelope, and the resultant envelope signal contains fault characteristic frequencies that are in proportion to the varying DFIG shaft rotating frequency. Next, an angular resampling algorithm is designed to resample the nonstationary envelope signal to be stationary based on the estimated instantaneous shaft rotating frequency. After that, the power spectral density analysis is performed on the resampled envelope signal for the gearbox fault detection. Finally, a classifier with a deep architecture that consists of a stacked autoencoder and a support vector machine is proposed for gearbox fault classification using extracted fault features. Experimental results obtained from a DFIG wind turbine drivetrain test rig are provided to verify the effectiveness of the proposed method.
81 citations
TL;DR: A new signal reconstruction modeling technique is proposed using support vector regression that demonstrates improved performance in detecting wind turbine faults, and controlling false and missed alarms.
81 citations
TL;DR: A review of the machine learning algorithm applications in fault detection in induction motors and the future prospects and challenges for an efficient machine learning based fault detection systems are presented.
Abstract: Fault detection prior to their occurrence or complete shut-down in induction motor is essential for the industries. The fault detection based on condition monitoring techniques and application of machine learning have tremendous potential. The power of machine learning can be harnessed and optimally used for fault detection. The faults especially in induction motor needs to be addressed at a proper time for avoiding losses. Machine learning algorithm applications in the domain of fault detection provides a reliable and effective solution for preventive maintenance. This paper presents a review of the machine learning algorithm applications in fault detection in induction motors. This paper also presents the future prospects and challenges for an efficient machine learning based fault detection systems.
81 citations
TL;DR: An approach for bearing fault prognostics that employs Renyi entropy based features that exploits the idea that progressing fault implicates raising dissimilarity in the distribution of energies across the vibrational spectral band sensitive to the bearing faults is proposed.
81 citations
TL;DR: In this article, the application of the synchronous signal averaging methodology to electric drive signals, by synchronizing stator current signals with a shaft position estimated from current and voltage measurements is proposed.
80 citations