Effective Vibration Analysis of Ic Engines Using Cyclostationarity. Part Ii—new Results on the Reconstruction of the Cylinder Pressures

Antoni, Jérôme; Danière, J.; Guillet, François; Randall, R.B.

doi:10.1006/jsvi.2002.5063

Cited by 82 publications

(45 citation statements)

References 10 publications

(20 reference statements)

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“…Increase in engine's efficiency and life time as well as reduction in pollutants are the main purposes of engine developers. So a lot of studies have been performed in this field [1]. Merola and Vaglieco [2] have implemented modeling and detection of knock intensity in a SI engine by using spectroscopy.…”

Section: Introductionmentioning

confidence: 99%

Spark plug fault recognition based on sensor fusion and classifier combination using Dempster–Shafer evidence theory

et al. 2015

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Section: Introductionmentioning

confidence: 99%

Spark plug fault recognition based on sensor fusion and classifier combination using Dempster–Shafer evidence theory

et al. 2015

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“…Therefore, vibration signals collected from rotating machinery are often mixed with external interference that is independent of fault information. The fault feature is weak when the external interference is strong [16,17]. The traditional time domain and frequency domain analysis method is unable to diagnose weak faults.…”

Section: Improved Wavelet Scalogrammentioning

confidence: 99%

“…Therefore, weak impact feature identification in rolling element bearings is significant to reduce downtime and safety hazards during the running period. Nowadays, a large number of modern approaches have been proposed, such as kurtograms [11,12], improved kurtograms and spectral kurtosis [13][14][15], cyclostationary analysis [16,17], empirical mode decomposition [18], synchronous averaging technology [19,20], sparse models [21,22], stochastic resonance [23], wavelet spectra [24], etc. When the impact characteristics of a signal are relatively weak or submerged in fault-independent interference components, they require further analysis [13].…”

Section: Introductionmentioning

confidence: 99%

Investigation on Planetary Bearing Weak Fault Diagnosis Based on a Fault Model and Improved Wavelet Ridge

Yang

Wang

et al. 2018

Energies

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Abstract:Rolling element bearings are of great importance in planetary gearboxes. Monitoring their operation state is the key to keep the whole machine running normally. It is not enough to just apply traditional fault diagnosis methods to detect the running condition of rotating machinery when weak faults occur. It is because of the complexity of the planetary gearbox structure. In addition, its running state is unstable due to the effects of the wind speed and external disturbances. In this paper, a signal model is established to simulate the vibration data collected by sensors in the event of a failure occurred in the planetary bearings, which is very useful for fault mechanism research. Furthermore, an improved wavelet scalogram method is proposed to identify weak impact features of planetary bearings. The proposed method is based on time-frequency distribution reassignment and synchronous averaging. The synchronous averaging is performed for reassignment of the wavelet scale spectrum to improve its time-frequency resolution. After that, wavelet ridge extraction is carried out to reveal the relationship between this time-frequency distribution and characteristic information, which is helpful to extract characteristic frequencies after the improved wavelet scalogram highlights the impact features of rolling element bearing weak fault detection. The effectiveness of the proposed method for weak fault recognition is validated by using simulation signals and test signals.

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“…The acceleration signals are the result of excitations due to many sources: for example, in the case of an internal combustion engine [4,5], the acceleration signal acquired by means of an accelerometer installed on a precise position on the engine is caused by the gas combustion, the reciprocating motion of the pistons, the contacts' forces in the cam mechanism, and so on. Therefore, the acquired acceleration signals contain contributions due to all these phenomena.…”

Section: Introductionmentioning

confidence: 99%

A Methodology Based on Cyclostationary Analysis for Fault Detection of Hydraulic Axial Piston Pumps

Casoli¹,

Bedotti²,

Campanini³

et al. 2018

Energies

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Condition monitoring has been an active area of research in many industrial fields during the last decades, particularly in fluid power systems. This paper presents a solution for the fault diagnosis of a variable displacement axial-piston pump, which is a critical component in many hydraulic systems. The proposed methodology follows a data-driven approach including data acquisition and feature extraction and is based on the analysis of acceleration signals through the theory of cyclostationarity. An experimental campaign was carried out on a laboratory test bench with the pump in the flawless state and in faulty states. Different operating conditions were considered and each test was repeated several times in order to acquire a suitable population to verify data repeatability. Results showed the capability of the proposed approach of detecting a typical fault related to worn slippers. Future works will include tests in order to apply the approach to a wider set of faults and the development of a classifier for accurate fault identification.

show abstract

Effective Vibration Analysis of Ic Engines Using Cyclostationarity. Part Ii—new Results on the Reconstruction of the Cylinder Pressures

Cited by 82 publications

References 10 publications

Spark plug fault recognition based on sensor fusion and classifier combination using Dempster–Shafer evidence theory

Spark plug fault recognition based on sensor fusion and classifier combination using Dempster–Shafer evidence theory

Investigation on Planetary Bearing Weak Fault Diagnosis Based on a Fault Model and Improved Wavelet Ridge

A Methodology Based on Cyclostationary Analysis for Fault Detection of Hydraulic Axial Piston Pumps

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