A general purpose fuzzy logic code

Alang-Rashid, N.K.; Heger, A.S.

doi:10.1109/fuzzy.1992.258748

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…For each architecture, the data was trained for 100 epochs with a batch size of 8. Graphs were plotted to illustrate the performance of each model during the training and the testing phase, as shown in Figures 14,15,16,17,18,and 19. The overall performance of each architecture has been tabulated in Table 4.…”

Section: Results Of Cnn and Image Encodingmentioning

confidence: 99%

“…Technically, fuzzy logic refers to variables that may be any real number between 0 and 1 inclusive. It is based on the concept of partial truth, where the true value of a variable may range from being either true or false [17]. The fuzzy logic technique employs a form of reasoning like humans since its decision-making process involves intermediaries between yes and no [18].…”

Section: Application Of Fuzzy Logicmentioning

confidence: 99%

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Intelligent Vibration Analysis of Industrial Cooling Fans

Sharrar

Danapalasingam

2022

Elektrika

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Industrial cooling fans are responsible for maintaining stable temperatures for delicate components. Therefore, a cooling system failure can certainly lead to machine downtime. Fault Condition Monitoring (FCM) is a predictive maintenance method that can be applied to cooling fans for fault prediction. As the components of a cooling fan wear off, its vibration tends to vary. Thus, this paper uniquely elaborates on three intelligent vibration analysis techniques that are applicable in the FCM of cooling fans. In this research, 1) image encoding with convolutional neural network (CNN), 2) moving average, and 3) fuzzy logic techniques are designed, employed, and their potentials as FCM tools are compared. The vibration data is collected from an experimental test bench that consists of a fan, an accelerometer, and a microcontroller, among others. Once enough data is obtained, the three vibration analysis techniques are applied using Python and MATLAB. The results reported in this paper demonstrate the effectiveness of these intelligent vibration analysis techniques in the FCM of cooling fans and possibly other industrial equipment. The novelty of the research revolves around the fan fault classification techniques that are being compared. The image-encoding technique described in this paper has yet to be applied for fault classification. Additionally, while fuzzy logic and moving average are popular methods, this is the first time that they are being used for vibration analysis of cooling fans. Furthermore, this is also a novel comparative study of different vibration analysis techniques.

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Section: Results Of Cnn and Image Encodingmentioning

confidence: 99%

Section: Application Of Fuzzy Logicmentioning

confidence: 99%