Fast Fourier processing and real-time transformation system for a dynamic vibration signal

Popa, Bogdan; Roman, Monica; Constantinescu, Radu Lucian

doi:10.1109/carpathiancc.2019.8766039

Cited by 7 publications

(2 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…During the last years, SoC computing power has been improved through a technology that allows the incorporation and seamless integration of heterogeneous resources. SoC-based FPGAs have been used in many research and development areas such as a control [4,24,36], power electronics [1,18,42,48], signal processing [37,44,45], image processing [9,10,14,28], virtualization [47] among others. Recently, SoC-based FPGAs have been used also for boosting the performance of Machine Learning (ML) applications.…”

Section: Introductionmentioning

confidence: 99%

Efficient traversal of decision tree ensembles with FPGAs

Molina

Loor

Gil-Costa

et al. 2021

Journal of Parallel and Distributed Computing

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

Efficient traversal of decision tree ensembles with FPGAs

Molina

Loor

Gil-Costa

et al. 2021

Journal of Parallel and Distributed Computing

View full text Add to dashboard Cite

“…Several algorithms have been developed to efficiently calculate the FFT, paving the way for the implementation of high-performance FFT processors. The Fourier transform essentially converts a signal's representation from the time domain to the frequency domain [33][34][35]:…”

mentioning

confidence: 99%

Machine Learning-based Predictive Maintenance for Fault Detection in Rotating Machinery: A Case Study

Khalil,

Rostam

2024

Eng. Technol. Appl. Sci. Res.

View full text Add to dashboard Cite

In the realm of industrial production, condition monitoring plays a pivotal role in ensuring the reliability and longevity of rotating machinery. Since most of the production facilities rely heavily on vibration analysis, it has become the cornerstone of condition monitoring practices. However, manual analysis of vibration signals is a time-consuming and expertise-intensive task, often requiring specialized domain knowledge. The current research addresses the aforementioned challenges by proposing a novel semi-automated diagnostics system. The approach leverages historical vibration data in the form of Fast Fourier Transform (FFT) spectrums. The system extracts energy features from the frequency domain by dividing the frequency range into a predefined number of bins and summing the energy values within each bin. Subsequently, each datapoint is labeled based on the corresponding machine condition, enabling the system to learn diagnostic patterns by employing machine learning models. This approach facilitates efficient and accurate diagnostics with minimal manual intervention. The resulting dataset effectively represents and provides an interpretable result. Support Vector Machines (SVM), and ensemble algorithms are utilized to diagnose the faults instantaneously and with minimal error rates. The proposed system is capable of providing early warnings and thus prevents further deterioration and unplanned downtimes. Experimental validation using real-world data demonstrates the system's efficacy, achieving an accuracy of over 90%.

show abstract