Enhanced Fault Diagnosis in Milling Machines Using CWT Image Augmentation and Ant Colony Optimized AlexNet DOI Creative Commons

N. Ullah,

Muhammad Umar,

Jae‐Young Kim

et al.

Sensors, Journal Year: 2024, Volume and Issue: 24(23), P. 7466 - 7466

Published: Nov. 22, 2024

A method is proposed for fault classification in milling machines using advanced image processing and machine learning. First, raw data are obtained from real-world industries, representing various types (tool, bearing, gear faults) normal conditions. These converted into two-dimensional continuous wavelet transform (CWT) images superior time-frequency localization. The then augmented to increase dataset diversity techniques such as rotating, scaling, flipping. contrast enhancement filter applied highlight key features, thereby improving the model’s learning detection capability. enhanced fed a modified AlexNet model with three residual blocks efficiently extract both spatial temporal features CWT images. architecture particularly well-suited identifying complex patterns associated different types. deep optimized ant colony optimization reduce dimensionality while preserving relevant information, ensuring effective feature representation. classified support vector machine, effectively distinguishing between conditions high accuracy. provides significant improvements outperforming state-of-the-art methods. It thus promising solution industrial diagnosis has potential broader applications predictive maintenance.

Language: Английский

A harmonic-based musical scaling method with natural number frequencies DOI Open Access
Selma Özaydın

rast müzikoloji dergisi, Journal Year: 2025, Volume and Issue: 13(1), P. 19 - 37

Published: March 30, 2025

General acceptance arises from the most convincing method among available options. Similarly, while Western chromatic scale is widely used system today, it has limitations in representing harmonious intervals, microtonal performances, and weak resonant effects of fractional frequencies This study introduces Safir method, a novel approach to redefining musical note within an octave interval. Unlike traditional scales, employs natural number-based values, ensuring more intervals enhanced tuning consistency. A key strength lies its ability overcome conventional systems. The enhances spectral coherence by aligning with harmonic distribution Fourier series strengthening resonance effect through frequencies. significant potential for various applications including music, speech signal processing, leakage reduction, healthcare. Four advantages are alignment series, , strong derived numbers, suppression dissonant higher across band, linear spacing octave, which ensures minimal deviation compatible even divisions. represents major advancement scales. By providing precise, harmonious, frequency system, addresses shortcomings scales opens new possibilities both theoretical practical domains.

Language: Английский

Citations

0
Enhanced Fault Diagnosis in Milling Machines Using CWT Image Augmentation and Ant Colony Optimized AlexNet DOI Creative Commons

N. Ullah,

Muhammad Umar,

Jae‐Young Kim

et al.

Sensors, Journal Year: 2024, Volume and Issue: 24(23), P. 7466 - 7466

Published: Nov. 22, 2024

A method is proposed for fault classification in milling machines using advanced image processing and machine learning. First, raw data are obtained from real-world industries, representing various types (tool, bearing, gear faults) normal conditions. These converted into two-dimensional continuous wavelet transform (CWT) images superior time-frequency localization. The then augmented to increase dataset diversity techniques such as rotating, scaling, flipping. contrast enhancement filter applied highlight key features, thereby improving the model’s learning detection capability. enhanced fed a modified AlexNet model with three residual blocks efficiently extract both spatial temporal features CWT images. architecture particularly well-suited identifying complex patterns associated different types. deep optimized ant colony optimization reduce dimensionality while preserving relevant information, ensuring effective feature representation. classified support vector machine, effectively distinguishing between conditions high accuracy. provides significant improvements outperforming state-of-the-art methods. It thus promising solution industrial diagnosis has potential broader applications predictive maintenance.

Language: Английский

Citations

3