Condition monitoring

About: Condition monitoring is a research topic. Over the lifetime, 13911 publications have been published within this topic receiving 201649 citations.

An Overview of Condition Monitoring Techniques for Capacitors in DC-Link Applications

Abstract: Capacitors are widely used in dc links of power electronic converters to balance power, suppress voltage ripple, and store short-term energy. Condition monitoring (CM) of dc-link capacitors has great significance in enhancing the reliability of power converter systems. Over the past few years, many efforts have been made to realize CM of dc-link capacitors. This article gives an overview and a comprehensive comparative evaluation of them with emphasis on the application objectives, implementation methods, and monitoring accuracy when being used. First, the design procedure for the CM of capacitors is introduced. Second, the main capacitor parameters estimation principles are summarized. According to these principles, various possible CM methods are derived in a step-by-step manner. On this basis, a comprehensive review and comparison of CM schemes for different types of dc-link applications are provided. Finally, application recommendations and future research trends are presented.

Condition monitoring of slow-speed rolling element bearings using stress waves:

Abstract: Condition monitoring of rolling element bearings through the use of vibration analysis is an established technique for detecting early stages of component degradation. However, this success...

A neuro-fuzzy approach to automatic diagnosis and location of stator inter-turn faults in CSI-fed PM brushless DC motors

Abstract: The paper presents a neuro-fuzzy-based perspective to the automation of diagnosis and location of stator-winding interturn short circuits in CSI-fed brushless dc motors. Performance of the drive under normal and short-circuit conditions are obtained through classical lumped-parameter network models. Waveforms of the electromagnetic torque and summation of phase voltages are monitored to develop two independent diagnostic algorithms. Diagnostic indices derived from the characteristic waveforms using discrete Fourier transform (DFT) lead to identifying the number of shorted turns. Fault location is achieved through a different set of indices extracted by the short-time Fourier transform (STFT). Adaptive neuro-fuzzy inference systems (ANFIS) are trained based on simulation results to automate the diagnostic process. ANFIS testing along with the good agreement between simulated and measured waveforms show the effectiveness of the proposed techniques.

An artificial neural network-based condition monitoring method for wind turbines, with application to the monitoring of the gearbox

Abstract: Major failures in wind turbines are expensive to repair and cause loss of revenue due to long downtime. Condition-based maintenance, which provides a possibility to reduce maintenance cost, has been made possible because of the successful application of various condition monitoring systems in wind turbines. New methods to improve the condition monitoring system are continuously being developed. Monitoring based on data stored in the supervisory control and data acquisition (SCADA) system in wind turbines has received attention recently. Artificial neural networks (ANNs) have proved to be a powerful tool for SCADA-based condition monitoring applications. This paper first gives an overview of the most important publications that discuss the application of ANN for condition monitoring in wind turbines. The knowledge from these publications is utilized and developed further with a focus on two areas: the data preprocessing and the data post-processing. Methods for filtering of data are presented, which ensure that the ANN models are trained on the data representing the true normal operating conditions of the wind turbine. A method to overcome the errors from the ANN models due to discontinuity in SCADA data is presented. Furthermore, a method utilizing the Mahalanobis distance is presented, which improves the anomaly detection by considering the correlation between ANN model errors and the operating condition. Finally, the proposed method is applied to case studies with failures in wind turbine gearboxes. The results of the application illustrate the advantages and limitations of the proposed method.

Artificial neural network and empirical mode decomposition based imbalance fault diagnosis of wind turbine using TurbSim, FAST and Simulink

Abstract: In this study, an artificial neural network (ANN) and empirical mode decomposition (EMD) based condition monitoring approach of a wind turbine using Simulink, FAST (fatigue, aerodynamics, structures and turbulence) and TurbSim is presented. The complete dynamics of a permanent magnet synchronous generator (PMSG) based wind turbine [i.e. wind turbine generator (WTG)] model is simulated in an amalgamated domain of Simulink, FAST and TurbSim under six distinct conditions, i.e. aerodynamic asymmetry, rotor-furl imbalance, tail-furl imbalance, blade imbalance, nacelle-yaw imbalance and normal operating scenarios. The simulation results in time domain of the PMSG output stator current are decomposed into the intrinsic mode functions using EMD method then RapidMiner-based principal component analysis method is used to select most relevant input variables. An ANN model is then proposed to differentiate the normal operating scenarios from five fault conditions. The analysed results proclaim the effectiveness of the proposed approach to identify the different imbalance faults in WTG. The presented work renders initial results that are helpful for online condition monitoring and health assessment of WTG.