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
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TL;DR: Fault diagnosis using thermal images for rotating machines can be applied to industrial areas as a novel intelligent fault diagnostic method with plausible accuracy and can be also proposed as a unique non-contact method to analyze rotating systems in mass production lines within a short time.
Abstract: Feature-based classification techniques consist of data acquisition, preprocessing, feature representation, feature calculation, feature selection, and classifiers They are useful for online, real-time condition monitoring and fault diagnosis / features, which are now available with the development of information technologies and various measurement techniques In this paper, an intelligent feature-based fault diagnosis is suggested, developed, and compared with vibration signals and thermal images Fault diagnosis is performed using thermal imaging along with support vector machine (SVM) classification to simulate machinery faults, resulting in an accuracy level comparable to vibration signals The observed results show that fault diagnosis using thermal images for rotating machines can be applied to industrial areas as a novel intelligent fault diagnostic method with plausible accuracy It can be also proposed as a unique non-contact method to analyze rotating systems in mass production lines within a short time
60 citations
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TL;DR: In this article, the authors presented the concept of the proposed composite spectrum which was applied to a laboratory test rig with different simulated faults; healthy and three faulty cases named misalignment, crack shaft, and shaft rub.
60 citations
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TL;DR: A novel approach is proposed which employs an information theoretic approach to feature subset selection of modulation spectra features and leverages information regarding the chronological order of data samples for dimensionality reduction.
Abstract: While automated condition monitoring of rotating machines often use vibration signals for defect detection, diagnosis, and residual life predictions, in this paper, the acoustic noise signal (<; 25 kHz), acquired via non-contact microphone sensors, is used to predict the remaining useful life (RUL). Modulation spectral (MS) analysis of acoustic signals has the potential to provide additional long-term information over more conventional short-term signal spectral components. However, the high dimensionality of MS features has been cited as a limitation to their applicability in this area in the literature. Therefore, in this study, a novel approach is proposed which employs an information theoretic approach to feature subset selection of modulation spectra features. This approach does not require information regarding the spectral location of defect frequencies to be known or pre-estimated and leverages information regarding the chronological order of data samples for dimensionality reduction. The results of this study show significant improvements for this proposed approach over the other commonly used spectral-based approaches for the task of predicting RUL by up to 19% relative over the standard envelope analysis approach used in the literature. A further 16% improvement was achieved by applying a more rigorous approach to labeling of acoustic samples acquired over the lifetime of the machines over a fixed length class labeling approach. A detailed misclassification analysis is provided to interpret the relative cost of system errors for the task of residual life predictions of rotating machines used in industrial applications.
60 citations
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TL;DR: In this article, the authors present a survey about the most important and updated condition monitoring techniques based on non-destructive testing and methods applied to wind turbine blades, and analyze the future trends and challenges of structural health monitoring systems.
60 citations
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TL;DR: An approach for RUL estimation from heterogeneous fleet data based on an homogeneous discrete-time finite-state semi-markov model is proposed and results show the effectiveness of the proposed approach in predicting the RUL and its superiority compared to a fuzzy similarity-based approach of literature.
60 citations