The Use of Denoising and Analysis of the Acoustic Signal Entropy in Diagnosing Engine Valve Clearance

Figlus, Tomasz; Gnap, Jozef; Skrúcaný, Tomáš; Šarkan, Branislav; Stokłosa, J.

doi:10.3390/e18070253

Cited by 22 publications

(21 citation statements)

References 39 publications

(58 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The capacity of the employed LiFePO4 battery is reduced by 8% for each 10°C drop in temperature from the reference value of 20°C [20]. Additionally, the manufacturer did not engineer any thermal conditioning means.…”

Section: Analysis Of the Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Light Electric Vehicle Powertrain Analysis

Łebkowski

2017

SJSUT.ST

View full text Add to dashboard Cite

Andrzej ŁEBKOWSKI 1 LIGHT ELECTRIC VEHICLE POWERTRAIN ANALYSISSummary. This paper describes the structure of a light electric vehicle known as the Mia Electric vehicle. The vehicle parameters and exploitation properties are presented, while the advantages and disadvantages of the vehicle's technical solutions are discussed, along with possible ideas for their improvement. Vehicle test results on a roller dyno and under actual driving conditions are presented. The data recorded during tests form the basis of an analysis of the vehicle powertrain, whose findings are described in the summary along with testing conclusions.

show abstract

Section: Analysis Of the Resultsmentioning

confidence: 99%

“…In addition, they have a high recycling level (95% in the case of Mia) and emit less noise, especially at velocities of less than 60 km/h, which in turn positively impacts on the general population's health [20][21][22]. Meanwhile, they generate less vibration than conventional vehicles [23][24][25][26][27][28][29][30].…”

Section: Discussionmentioning

confidence: 99%

Light Electric Vehicle Powertrain Analysis

Łebkowski

2017

SJSUT.ST

View full text Add to dashboard Cite

show abstract

“…Moreover, an appropriate threshold gives significant efficiency of the EMD-based noise elimination process. In order to solve the above problems, the denoising thresholds in Equation (7) or (8) are determined automatically through the improved FOA instead of Equations (5) and (6). The flow of the proposed EMD-IFOA can be summarized as follows:…”

Section: Flow Of the Proposed Denoising Methodsmentioning

confidence: 99%

“…The noisy signal could be decomposed as x = imf1 + imf2 + imf3 + … + imf12 + res. Then, the shrinkage threshold of each IMF was determined according to Equations (5) and (6). Soft threshold function was applied and the denoised signal was reconstructed as Equation (9).…”

Section: Signal Decomposition and Denoisingmentioning

confidence: 99%

“…In addition, it involves a lower cost and simpler structure than the laser vibrometry measuring system. Unfortunately, one of the most important unfavorable factors for the acoustic-based monitoring system is the substantial noise component in the initial sound signal [6]. Thus, the denoising operation is essential and the performance of noise elimination directly influences the effect of subsequent processing.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Denoising Method for an Acoustic-Based System through Empirical Mode Decomposition and an Improved Fruit Fly Optimization Algorithm

Wang

Tan

et al. 2017

Applied Sciences

View full text Add to dashboard Cite

Generally, the sound signal produced by transmission unit or cutting unit contains abundant information about the working state of a machine. The acoustic-based diagnosis system presents some distinct advantages in some severe conditions particularly due to its unique non-contact measurement and unlimited use at the installation site. However, the original acoustic signal collected from manufacture process is always polluted by various background noises. In order to eliminate noise components from machinery sound effectively, an empirical mode decomposition (EMD) threshold denoising method optimized by an improved fruit fly optimization algorithm (IFOA) is launched in this paper. The acoustic signal was first decomposed by the adaptive EMD to obtain a series of intrinsic mode functions (IMFs). Then, the soft threshold function was applied to shrink the IMF coefficients. While the threshold of each IMF was determined by statistical estimation and empirical value for traditional EMD denoising, the denoising effect was often not desired and time-consuming. To solve these disadvantages, fruit fly optimization algorithm (FOA) was introduced to search global optimal threshold of each IMF. Moreover, to enhance the group diversity during production of the next generation of fruit flies and balance the local and global searching ability, a variation coefficient and a disturbance coefficient was introduced to the basic FOA. Then, a piece of simulated acoustic signal produced by the train was applied to validate the proposed EMD and IFOA threshold denoising (EMD-IFOA). The simulation results, which decreased 35.40% and 18.92% in mean squared error (MSE) and percent root mean square difference (PRD) respectively, and increased 40.36% in signal-to-noise ratio improvement (SNR imp ) compared with basic EMD denoising scheme at SNR = 5 dB, illustrated the effectiveness and superiority of the proposed approach. Finally, the proposed EMD-IFOA was conducted on an actual acoustic-based diagnosis system for cutting state recognition of the coal mining shearer to demonstrate the practical effect.

show abstract