2016
DOI: 10.1016/j.ymssp.2015.10.037
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Extraction of fault component from abnormal sound in diesel engines using acoustic signals

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Cited by 38 publications
(14 citation statements)
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“…Therefore, engineers are doing continuous researches to find optimal fault detection system for diesel engine in order to monitor the health condition of the engine. Several types of signals can be measured for monitoring purpose including rotational angle speed [1], temperature [2], fuel and oil quality [3] [4], and vibration [5] [6] [7].…”
Section: Introductionmentioning
confidence: 99%
“…Therefore, engineers are doing continuous researches to find optimal fault detection system for diesel engine in order to monitor the health condition of the engine. Several types of signals can be measured for monitoring purpose including rotational angle speed [1], temperature [2], fuel and oil quality [3] [4], and vibration [5] [6] [7].…”
Section: Introductionmentioning
confidence: 99%
“…The traditional signal denoising method uses a Fourier transform-based filter to filter the frequency components of noise but has apparent defects in the case of non-stationary signals and short-term transient signals. Currently, popular methods of noise reduction include wavelet transform (WT) [5], empirical mode decomposition (EMD) [6], Savitzky-Golay (S-G) filter [7], dislocation superposition method (DSM) [8] et al…”
Section: Introductionmentioning
confidence: 99%
“…But when the SNR is low, the noise reduction effect of the S-G method is not good enough. Ning et al [8] proposed the DSM in 2015. Compared with other methods, DSM only has time-domain calculation.…”
Section: Introductionmentioning
confidence: 99%
“…However, similar to the EMD, the LMD and ITD have the disadvantage of mode mixing. In 2015, Dayong et al [25] proposed a dislocation superimposed method (DSM) on the basis of the random decrement technique. In comparison with other methods, the DSM is simple and will not change the concerned component in the mixed signal.…”
Section: Introductionmentioning
confidence: 99%