AE Condition Monitoring: Challenges and Opportunities

Sikorska, Joanna

doi:10.1007/978-1-84628-814-2_14

Cited by 6 publications

(2 citation statements)

References 46 publications

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“…To create a better understanding of the initiation, growth and interaction of the different types of damage, damage monitoring during mechanical loading is very important. Acoustic emission (AE) is the only non-destructive test technique capable of detecting all of the above mentioned damage types in composites [14].…”

Section: Introductionmentioning

confidence: 99%

Effect of Multiple Impacts on GFRP Laminates Exposed to Hydrolytic Ageing using Acoustic Emission Monitoring

Ramesh¹,

Stanley

Arumugam

et al. 2014

Int. J. Vehicle Structures and Systems

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In this study, pristine, single impacted and double impacted GFRP specimens were subjected to ageing by immersion in seawater at ambient temperature for different periods of time. The dominating failure mode of the aged specimens was found using acoustic emission monitoring. The water absorption capacity and flexural strength of specimen subjected to ageing were assessed to study the trend of variation. A reduction in residual strength was observed between single and double impacted specimens. Stress cycle plots from AE monitoring indicate that the onset of failure occurs sooner as the ageing time progresses. From those of impacted specimens, it can be seen that the onset of failure and damage initiation occurs sooner in double-impacted specimens than in single-impacted counterparts. Parametric peak frequency analysis shows that fiber-matrix debonding was the predominant mode of failure.

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Section: Introductionmentioning

confidence: 99%

Effect of Multiple Impacts on GFRP Laminates Exposed to Hydrolytic Ageing using Acoustic Emission Monitoring

Ramesh¹,

Stanley

Arumugam

et al. 2014

Int. J. Vehicle Structures and Systems

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“…These errors may either be three phases in nature connecting all three phases in a balanced manner or may be unbalanced where typically only one or two phases may be engaged (Li et al, 2005). That is the cause why ball bearing errors are not simple to identify (Sikorsk and Mba, 2006;Sugumaran et al, 2007). Due to insulation breakdown errors happen in a power system, flashover, physical spoil or human fault (Lei et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

HAIWF-based fault detection and classification for industrial machine condition monitoring

Sathish¹,

Karthick

2018

PIE

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Fault detection and classification is based on the idea that can detect changing conditions within equipment. The techniques for detecting faults can be distinguished as a pattern matching from the values of a sensor and the difference between the sensor readings and expected value. Researchers also interested in the field of fault detection of rotating machinery using artificial neural networks (ANNs). But, ANN suffer from data diversity and training complexity. In this paper, an approach is presented to prevent the complexities of ANN. The proposed system uses the inertia weight firefly (IWF) algorithm for training the neural network. The efficiency of the Hybrid ANN IWF (HAIWF) in detecting and classifying machine faults is compared with conventional techniques. The proposed techniques achieved 11-14% more than the conventional techniques. Ultimately the proposed IWF based ANN is suggested to effectively predict the industrial machine fault detection.

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Acoustic Emission for Diesel Engine Monitoring: A Review and Preliminary Analysis

Kim

Tan

Yang

2012

Engineering Asset Management and Infrastructure Sustainability

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AE Condition Monitoring: Challenges and Opportunities

Cited by 6 publications

References 46 publications

Effect of Multiple Impacts on GFRP Laminates Exposed to Hydrolytic Ageing using Acoustic Emission Monitoring

Effect of Multiple Impacts on GFRP Laminates Exposed to Hydrolytic Ageing using Acoustic Emission Monitoring

HAIWF-based fault detection and classification for industrial machine condition monitoring

Acoustic Emission for Diesel Engine Monitoring: A Review and Preliminary Analysis

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