Acoustic signal‐based approach for fault detection in motorcycles using chaincode of the pseudospectrum and dynamic time warping classifier

Anami, Basavaraj S.; Pagi, Veerappa B.

doi:10.1049/iet-its.2012.0086

Cited by 5 publications

(1 citation statement)

References 18 publications

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“…For feature extraction, frequency domain-based signal processing techniques such as Discrete Fourier Transform (DFT) and short Time Fourier Transform (STFT) is used for detecting faults in the automobiles [6]. Anami et al [7] adopted spectral based techniques by exploiting the variations in spectral behavior together with the chain code of the pseudo spectrum of the sound signal. Zhou et al [8] adopted Fourier Transform based Mel-Frequency Cepstral Coefficients (MFCC) for recognizing the faults.…”

Section: Introductionmentioning

confidence: 99%

Sound Signal Based Fault Classiﬁcation System in Motorcycles Using Hybrid Feature Sets and Extreme Learning Machine Classiﬁers

Jayasree¹,

Ananth²

2020

Sound&Vibration

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Vehicles generate dissimilar sound patterns under different working environments. These generated sound patterns signify the condition of the engines, which in turn is used for diagnosing various faults. In this paper, the sound signals produced by motorcycles are analyzed to locate various faults. The important attributes are extracted from the generated sound signals based on time, frequency and wavelet domains which clearly describe the statistical behavior of the signals. Further, various types of faults are classified using the Extreme Learning Machine (ELM) classifier from the extracted features. Moreover, the improved classification performance is obtained by the combination of feature sets in different domains. The simulation results clearly demonstrate that the proposed hybrid feature set together with the ELM classifier gives more promising results with higher classification accuracy when compared with the other conventional methods.

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Section: Introductionmentioning

confidence: 99%

Sound Signal Based Fault Classiﬁcation System in Motorcycles Using Hybrid Feature Sets and Extreme Learning Machine Classiﬁers

Jayasree¹,

Ananth²

2020

Sound&Vibration

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Degree-Pruning Dynamic Programming Approaches to Central Time Series Minimizing Dynamic Time Warping Distance

Sun

Liu

et al. 2017

IEEE Trans. Cybern.

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The central time series crystallizes the common patterns of the set it represents. In this paper, we propose a global constrained degree-pruning dynamic programming (g(dp)2) approach to obtain the central time series through minimizing dynamic time warping (DTW) distance between two time series. The DTW matching path theory with global constraints is proved theoretically for our degree-pruning strategy, which is helpful to reduce the time complexity and computational cost. Our approach can achieve the optimal solution between two time series. An approximate method to the central time series of multiple time series [called as m_g(dp)2] is presented based on DTW barycenter averaging and our g(dp)2 approach by considering hierarchically merging strategy. As illustrated by the experimental results, our approaches provide better within-group sum of squares and robustness than other relevant algorithms.

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Rational-Dilation Wavelet Transform Based Torque Estimation from Acoustic Signals for Fault Diagnosis in a Three-Phase Induction Motor

Hemamalini

2019

IEEE Trans. Ind. Inf.

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Acoustic signal‐based approach for fault detection in motorcycles using chaincode of the pseudospectrum and dynamic time warping classifier

Cited by 5 publications

References 18 publications

Sound Signal Based Fault Classiﬁcation System in Motorcycles Using Hybrid Feature Sets and Extreme Learning Machine Classiﬁers

Sound Signal Based Fault Classiﬁcation System in Motorcycles Using Hybrid Feature Sets and Extreme Learning Machine Classiﬁers

Degree-Pruning Dynamic Programming Approaches to Central Time Series Minimizing Dynamic Time Warping Distance

Rational-Dilation Wavelet Transform Based Torque Estimation from Acoustic Signals for Fault Diagnosis in a Three-Phase Induction Motor

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