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

Sathish, T.; Karthick, S.

doi:10.1504/pie.2018.10016961

Cited by 26 publications

(15 citation statements)

References 25 publications

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“…Due to this, the dive mode or the speed of vehicle can influence the control of AC system. These type of AC system is more complicated than conventional system, but is mostly used to its features [6].…”

Section: Introductionmentioning

confidence: 99%

K-nearest neighbour technique for the effective prediction of refrigeration parameter compatible for automobile

Thangavel¹,

Vellingiri²,

Rajendrian³

et al. 2020

THERM SCI

120

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Manufacturing simulation is an encouraging research area in resent decade. Creation or development of better simulation tool or technique is one of the major intension in manufacturing simulation. In resent research most of the manufacturing processes are simulated successfully. But some processes are not yet simulated effectively, especially automatic air conditioning (AC) system or refrigeration system. The automatic AC system for the passenger vehicle are not yet effectively simulated. Hence in this paper a machine learning technique is adopted for the effective prediction of parameter of automatic AC system. The proposed system uses k-nearest neighbour technique for the prediction of parameter will less error and high accuracy. The proposed system is implemented using MATLAB and its performance is compared with the support vector machine and ANN in terms of mean square error and accuracy. The proposed technique outperforms the conventional technique and suggest that the k-nearest neighbour become the most suitable technique for the modelling and performance analysis of automatic AC system.

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Section: Introductionmentioning

confidence: 99%

K-nearest neighbour technique for the effective prediction of refrigeration parameter compatible for automobile

Thangavel¹,

Vellingiri²,

Rajendrian³

et al. 2020

THERM SCI

120

View full text Add to dashboard Cite

show abstract

“…Agriculture was the key evolution in the emergence of secondary human civilization. It represents one third of the gross domestic product (GDP) [27][28][29][30][31]. India is an agricultural country and about 70% of the population depends on agriculture.…”

Section: Introductionmentioning

confidence: 99%

Image Processing System for Detecting and Recovering from Cotton Leaf Disease using The Android Application

Deepa¹,

Nagarajan²,

Rajan³

et al. 2019

IJITEE

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A plant is the heart of an agricultural field and it improves source of energy as well as financial income. We discuss several types of leaf diseases in the plants and their identification process, symptoms of disease and remedy measures are explained. There is a huge source of plants, but due to the effect of environmental condition or as weather, climate change, many non-native diseases occur on plants and cause social, ecological and financial losses. Many of the farmers are unable to recognize new disease. So the farmer needs an expert person who recognizes the disease and symptoms appearing on the plants, this will increase the cost of farming. To reduce farming, we introduce Android Application which diagnosis and identify the type of disease and symptoms of disease on plant leaves. The Android application performs basic operations like color transformation, thresholding and edge detection. Our system work on such plant leaves which are affected by many diseases that is a virus, fungi, excess of nutrients and insects, etc.We use image processing to detect and classify these diseases. Implement image processing operation on an android mobile device. It identifies the actual type of disease and its symptoms. Finally, using SVM-GA classifier to diagnosis the cotton leaf disease. Using Android Application type of disease, symptoms, remedy measures and recovery suggestions are given at a very less time and low cost.

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“…Reference may be made to a US patent publication US2004/0234803 to Catherine Joyce [27]. The innovator reveals a multi fiber composite comprising of a combinant structure of cellulosic wood or polymeric strands blended with non-stringy materials [28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Reinforced Composite Material from Garbage to Biomaterials

Kumar¹,

Haris²,

Rahaman³

et al. 2019

IJITEE

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A hybrid reinforced composite material which fundamentally contains horticulture squander materials like banana stem outer layer (Musa sepientum) modified to powder form, egg shell modified to fine powder ,the said powders (powder have a size in a range of 10 to 50 microns) being mixed in a matrix of bio epoxy resin using a catalyst to effect complete mixing of the said powdered fibers to yield the said hybrid reinforced composite, the said hybrid composite capable of being molded to any geometrical object, the said composite having a low water retention property with retention being in a range of 14-23% with a soaking time of 120 hrs. Before and after chemical treatment in both with and without moisture, the youngs modulus varies from 12321.5241 N/mm2 to 25779.2532 N/mm2 , the flexural values varies from 17.4 N/mm2 to 25.5 N/mm2 ,impact strength varies from 12.6 J to 20.9 J , % of elongation varies from 5.6% to 10.1%. The composite has negligible change in tensile properties up to a load of 270 Kg also flexural properties and impact strength of the composite were not be affected by under moisture conditions.

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

Cited by 26 publications

References 25 publications

K-nearest neighbour technique for the effective prediction of refrigeration parameter compatible for automobile

K-nearest neighbour technique for the effective prediction of refrigeration parameter compatible for automobile

Image Processing System for Detecting and Recovering from Cotton Leaf Disease using The Android Application

Hybrid Reinforced Composite Material from Garbage to Biomaterials

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