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Highlights • Time delay estimated by EMD ML in low SNR and reverberant underwater environment. • In EMD ML method, estimation of impulse response function is been avoided. • Among TDE methods EMD ML is suitable in low signal strength, low frequency source. Abstract In recent times, time delay estimation (TDE) has received significant practical importance in sonar, radar, GPS, and various other fields. In a passive sonar system, estimation of time delay for low frequency and low signal to noise (SNR) acoustic source is a difficult task. If the source and receivers are kept inside the reverberation environment, time delay estimation becomes more difficult because of sound source echo's. This research work proposes a new TDE approach named empirical mode decomposition maximum likelihood time delay estimation (EMD ML TDE) method, for the low-frequency and low SNR underwater machinery acoustic signal in a reverberant environment. EMD ML TDE method is based on maximum likelihood (ML) method, and it is using empirical mode decomposition (EMD) denoising technique to estimate acoustic sound signal and noise from the noisy reverberant signal. The experimental results are provided that this new approach is better to estimate time delay in reverberant environments. [ABSTRACT FROM AUTHOR]

Detection of spark ignition engine firing is an important parameter to understand proper and improper combustion inside the engine cylinder. The engine firing can be detected by direct measurement of in-cylinder pressure by using piezo-electric sensor. However, the cost of the sensor is really high. Therefore, in this paper, engine firing has been detected by analysis of instantaneous angular speed (IAS) signal from output shaft measured by cheap device like rotary encoder. The direct short time Fourier transform (STFT) is unable to detect engine firing clearly as the signal contains various frequencies (rotational frequency and its harmonics) other than engine firing frequency. Therefore, an algorithm has been developed based on complementary ensemble empirical mode decomposition (CEEMD) and followed by STFT and applied on IAS signal to detect engine firing. Moreover, a comparative study has been performed between laser vibrometer signal and IAS signal to understand the efficacy of these signals. [ABSTRACT FROM AUTHOR]

This paper describes the interpretations of underwater noise spectra of rainfall on water bodies. It uses a simple methodology of measurement of bubbles of various sizes up to 10 mm in diameter and the comparison of resonating frequencies of the above bubbles with theoretical calculations. The underwater noise of rainfall is a combined effect of drop impacts and bubble bursting. The rain drop size during actual rainfall varies between 0.8 and 5 mm in diameter and the underwater bubble sizes can vary from 0.5 mm to 10 mm in diameter. The detailed description of the experimental results of the underwater spectrum of natural light rain and artificially generated rain are presented. By analyzing the resonating frequencies of underwater bubbles for different type of rainfall, it is plausible to interpret the underwater noise spectrum of rainfall on water bodies and then predict the amount of rainfall. [ABSTRACT FROM AUTHOR]

• Explosive sound source positioning by using modified Levenberg Marquardt algorithm. • A comparative analysis of different positioning methods. • Modified LMA performance validation through simulation and experimental studies. • A short and wide range of sound source positioning by varying r between 0.002 and 2. • Sound source localization for both indoor and outdoor environments. In this paper, a modified Levenberg-Marquardt algorithm (MLMA) is proposed to localize the 'point of burst' of an explosive sound source over the range of (0.5–2500) m. The objective function for minimization is formulated through the time difference of arrival based multilateration approach. The developed method uses four exclusive steps to satisfy global convergence along with fast computational speed. The performance of the proposed method is validated through both indoor-outdoor experiments and simulation studies and compared to other well-known methods. The experimental results show that the non-iterative approaches perform satisfactorily only if the ratio of microphone spacing to source range r is greater than 0.30. However, the iterative approaches outperform non-iterative approaches for any r. It is also observed that the MLMA converges globally at least five times faster than other algorithms. The numerical simulation results also demonstrate that the MLMA provides optimal solutions at lower and higher noise thresholds. [ABSTRACT FROM AUTHOR]

Faults in a rotor-bearing system due to bearings and unbalance have been classified using support vector machines (SVMs). Vibration signals on a rotor-bearing system were measured simultaneously at five different rotating speeds using seven transducers. The most sensitive feature of the vibration signals has been determined using compensation distance evaluation technique. Multi-class SVMs classification algorithm was then implemented for classification of the faults by considering SVMs created by the possible combinations of the most two sensitive features for each type of fault. By using optimal SVM parameters, the effective location of transducer among seven transducers for best classification of the faults has been investigated and found that any transducer provides a classification of 75% or better and this classification rate increases when more transducers are considered. This paper provides a robust SVM based technique using only time domain data without any additional preprocessing for classifying bearing and unbalance faults. [ABSTRACT FROM AUTHOR]