Feature Extraction of Time-Series Data Using DWT and FFT for Ballscrew Condition Monitoring

Alegeh, Nurudeen; Thottoli, Munavar; Mian, Naeem S.; Longstaff, Andrew P.; Fletcher, Simon

doi:10.3233/atde210069

Cited by 6 publications

(2 citation statements)

References 11 publications

(13 reference statements)

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“…The algorithm is based on the divide-and-conquer principle, which breaks down the DFT computation into smaller sub-problems and combines their solutions to obtain the final result. The FFT is a popular method for extracting features from time series [32,34], and there are multiple frameworks for computing the FFT [26].…”

Section: Discrete Fourier Transformmentioning

confidence: 99%

From Individual Device Usage to Household Energy Consumption Profiling

Tolas,

Portase,

Potolea

2024

Electronics

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This paper presents a novel approach for electricity consumption profiling in households through the fusion of usage data for individual smart devices. The novelty of the approach consists of leveraging the data representing the usage of individual appliances rather than using direct measurements of energy consumption. Our methodology focuses on merging signals representing the interaction of the user with the device to compute patterns in the total energy consumption per household. Subsequently, we apply data mining techniques—specifically, unsupervised clustering—to analyze the resulting time-series data representing daily energy consumption. Through this approach, we aim to identify and characterize patterns in energy usage within households, enabling insights for energy optimization strategies and resource allocation. This information can be further used in practical tasks, such as flattening energy consumption. The proposed approach offers an alternative to the direct measurement of energy usage, considering the potential for sensor failure or malfunction. This underscores the importance of implementing a complementary method for verifying and validating energy consumption data.

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Section: Discrete Fourier Transformmentioning

confidence: 99%

From Individual Device Usage to Household Energy Consumption Profiling

Tolas,

Portase,

Potolea

2024

Electronics

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“…Unlike the described approaches centered on acoustic emissions or vibration analysis in the time domain, the proposed AFI method evaluates the complete bandwidth of the spectrum, encompassing signals beyond mere vibrations [21,22]. The technique eliminates transmission path effects by correlating the frequency band amplitude sums to an unfiltered total baseline, enhancing the diagnostic accuracy.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of a Condition Monitoring Algorithm for Early Bearing Fault Detection

Gruber,

Fuchs,

Bader

2024

Sensors

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Roller bearings are critical components in various mechanical systems, and the timely detection of potential failures is essential for preventing costly downtimes and avoiding substantial machinery breakdown. This research focuses on finding and verifying a robust method that can detect failures early, without creating false positive failure states. Therefore, this paper introduces a novel algorithm for the early detection of roller bearing failures, particularly tailored to high-precision bearings and automotive test bed systems. The featured method (AFI—Advanced Failure Indicator) utilizes the Fast Fourier Transform (FFT) of wideband accelerometers to calculate the spectral content of vibration signals emitted by roller bearings. By calculating the frequency bands and tracking the movement of these bands within the spectra, the method provides an indicator of the machinery’s health, mainly focusing on the early stages of bearing failure. The calculated channel can be used as a trend indicator, enabling the method to identify subtle deviations associated with impending failures. The AFI algorithm incorporates a non-static limit through moving average calculations and volatility analysis methods to determine critical changes in the signal. This thresholding mechanism ensures the algorithm’s responsiveness to variations in operating conditions and environmental factors, contributing to its robustness in diverse industrial settings. Further refinement was achieved through an outlier detection filter, which reduces false positives and enhances the algorithm’s accuracy in identifying genuine deviations from the normal operational state. To benchmark the developed algorithm, it was compared with three industry-standard algorithms: VRMS calculations per ISO 10813-3, Mean Absolute Value of Extremums (MAVE), and Envelope Frequency Band (EFB). This comparative analysis aimed to evaluate the efficacy of the novel algorithm against the established methods in the field, providing valuable insights into its potential advantages and limitations. In summary, this paper presents an innovative algorithm for the early detection of roller bearing failures, leveraging FFT-based spectral analysis, trend monitoring, adaptive thresholding, and outlier detection. Its ability to confirm the first failure state underscores the algorithm’s effectiveness.

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Time series analysis and forecasting of cholera disease using discrete wavelet transform and seasonal autoregressive integrated moving average model

Amshi

Prasad

2023

Scientific African

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Feature Extraction of Time-Series Data Using DWT and FFT for Ballscrew Condition Monitoring

Cited by 6 publications

References 11 publications

From Individual Device Usage to Household Energy Consumption Profiling

From Individual Device Usage to Household Energy Consumption Profiling

Evaluation of a Condition Monitoring Algorithm for Early Bearing Fault Detection

Time series analysis and forecasting of cholera disease using discrete wavelet transform and seasonal autoregressive integrated moving average model

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