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: In this paper, the authors classified the capacitors condition monitoring methods into three categories and summarized the state-of-the-art research and the future opportunities targeting for industry applications.
Abstract: Capacitors are one type of reliability-critical components in power electronic systems. In the last two decades, many efforts in academic research have been devoted to the condition monitoring of capacitors to estimate their health status. Industry applications are demanding more reliable power electronics products with preventive maintenance. Nevertheless, most of the developed capacitor condition monitoring technologies are rarely adopted by industry due to the complexity, increased cost, and other relevant issues. An overview of the prior-art research in this area is therefore needed to justify the required resources and the corresponding performance of each key method. It serves to provide a guideline for industry to evaluate the available solutions by technology benchmarking, as well as to advance the academic research by discussing the history development and the future opportunities. Therefore, this paper first classifies the capacitor condition monitoring methods into three categories, then the respective technology evolution in the last two decades is summarized. Finally, the state-of-the-art research and the future opportunities targeting for industry applications are given.
159 citations
TL;DR: In this article, data mining algorithms and statistical methods are applied to analyze the jerk data obtained from monitoring the gearbox of a wind turbine and two types of analyses are performed-failure component identification and monitoring vibration excitement.
Abstract: Data mining algorithms and statistical methods are applied to analyze the jerk data obtained from monitoring the gearbox of a wind turbine. Two types of analyses are performed-failure component identification and monitoring vibration excitement. In failure component identification, the failed stages of the gearbox are identified in time-domain analysis and frequency-domain analysis. In the time domain, correlation coefficient and clustering analysis are applied. The fast Fourier transformation with time windows is utilized to analyze the frequency data. To monitor the vibration excitement of the gearbox in its high-speed stage, data mining algorithms and statistical quality control theory are combined to develop a monitoring model. The capability of the monitoring model to detect changes in the gearbox vibration excitement is validated by the collected data.
158 citations
TL;DR: In this article, a new technique for pre-whitening has been proposed, based on cepstral analysis, which seems a good candidate to perform the intermediate pre-whiteening step in an automatic damage recognition algorithm.
158 citations
TL;DR: The results showed that the method was able not only to detect the failure in an incipient stage but also to identify the location of the defect and qualitatively assess its evolution over time.
157 citations
02 Sep 2011
TL;DR: A pattern recognition system for detecting road condition from accelerometer and GPS readings and proposes a speed dependence removal approach for feature extraction and demonstrates its positive effect in multiple feature sets for the road surface anomaly detection task.
Abstract: The objective of this research is to improve traffic safety through collecting and distributing up-to-date road surface condition information using mobile phones. Road surface condition information is seen useful for both travellers and for the road network maintenance. The problem we consider is to detect road surface anomalies that, when left unreported, can cause wear of vehicles, lesser driving comfort and vehicle controllability, or an accident. In this work we developed a pattern recognition system for detecting road condition from accelerometer and GPS readings. We present experimental results from real urban driving data that demonstrate the usefulness of the system. Our contributions are: 1) Performing a throughout spectral analysis of tri-axis acceleration signals in order to get reliable road surface anomaly labels. 2) Comprehensive preprocessing of GPS and acceleration signals. 3) Proposing a speed dependence removal approach for feature extraction and demonstrating its positive effect in multiple feature sets for the road surface anomaly detection task. 4) A framework for visually analyzing the classifier predictions over the validation data and labels.
157 citations