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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]

A number of techniques for detection of faults in rolling element bearing using frequency domain approach exist today. For analysing non-stationary signals arising out of defective rolling element bearings, use of conventional discrete Fourier transform (DFT) has been known to be less efficient. One of the most suited time–frequency approach, wavelet transform (WT) has inherent problems of large computational time and fixed-scale frequency resolution. In view of such constraints, the Hilbert–Huang Transform (HHT) technique provides multi-resolution in various frequency scales and takes the signal''s frequency content and their variation into consideration. HHT analyses the vibration signal using intrinsic mode functions (IMFs), which are extracted using the process of empirical mode decomposition (EMD). However, use of Hilbert transform (HT)-based time domain approach in HHT for analysis of bearing vibration signature leads to scope for subjective error in calculation of characteristic defect frequencies (CDF) of the rolling element bearings. The time resolution significantly affects the calculation of corresponding frequency content of the signal. In the present work, FFT of IMFs from HHT process has been incorporated to utilise efficiency of HT in frequency domain. The comparative analysis presented in this paper indicates the effectiveness of using frequency domain approach in HHT and its efficiency as one of the best-suited techniques for bearing fault diagnosis (BFD). [Copyright &y& Elsevier]