Fault Severity Estimation of Rotating Machinery Based on Residual Signals

Jiang, Fan; Li, Wei; Wang, Zhong-Qiu; Zhu, Zhencai

doi:10.1155/2012/518468

Cited by 12 publications

(7 citation statements)

References 20 publications

(21 reference statements)

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“…Moshou et al (2010) have used self-organizing maps (SOM) for the estimation of defect severity of faults in a rotating mechanical system. Jiang et al (2012) have conducted an experimental investigation for estimation of defect severity of rotating machinery. The multi frequency band energies (MFBEs) are extracted from the vibration signals for feature selection, and statistics and residual signals are used for estimating the defect severity.…”

Section: Introductionmentioning

confidence: 99%

Feature extraction and fault severity classification in ball bearings

Sharma

Amarnath

Kankar

2014

Journal of Vibration and Control

116

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The present study attempts to diagnose severity of faults in ball bearings using various machine learning techniques, like support vector machine (SVM) and artificial neural network (ANN). Various features are extracted from raw vibration signals which include statistical features such as skewness, kurtosis, standard deviation and measures of uncertainty such as Shannon entropy, log energy entropy, sure entropy, etc. The calculated features are examined for their sensitivity towards fault of different severity in bearings. The proposed methodology incorporates extraction of most appropriate features from raw vibration signals. Results revealed that apart from statistical features uncertainty measures like log energy entropy and sure entropy are also good indicators of variation in fault severity. This work attempts to classify faults of different severity level in each bearing component which is not considered in most of the previous studies. Classification efficiency achieved by proposed methodology is compared to the other methodologies available in the literature. Comparative study shows the potential application of proposed methodology with machine learning techniques for the development of real time system to diagnose fault and it’s severity in ball bearings.

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Section: Introductionmentioning

confidence: 99%

Feature extraction and fault severity classification in ball bearings

Sharma

Amarnath

Kankar

2014

Journal of Vibration and Control

116

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“…in which the matrices are diagonal with common elements KP = 3×10 6 N/m, KI = 3×10 5 N/s/m and KD = 5×10 3 Ns/m, respectively, as proportional, integral and derivative gains. Detailed information about control and magnetic bearings properties and functioning is given in [28].…”

Section: Magnetic Bearing Modellingmentioning

confidence: 99%

“…Jiang et al [3] developed a new method for obtaining the severity of faults in rotor dynamic systems. The authors used the FFT to obtain the spectral signal of the time series and used a multifrequency band method to extract the crucial components of the frequency spectrum.…”

Section: Introductionmentioning

confidence: 99%

Investigation of Fault Modeling in the Identification of Bearing Wear Severity

Alves

Machado

Tuckmantel

et al. 2021

Journal of Tribology

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Recent research into machines involved in the power generation process has demanded deep investigation of model-based techniques for fault diagnosis and identification. The improvement of critical fault characterisation is crucial in the maintenance process effectiveness, hence in time/costs saving, increasing performance and productivity of the whole system. Consequently, this paper deals with a common fault in hydrodynamically lubricated bearings assembled in rotating systems, namely, that of abrasive wear. Research on this topic points to an interesting query about the significance of model detail and complexity and the identification of its characteristic parameters for the important stages of fault diagnosis and fault identification. For this purpose, two models are presented and analysed in their completeness concerning the fault signature by vibration measurements, as well as the identification of fault critical parameters which determine the machine lifetime estimation, maintenance procedures and time costs regarding performance and productivity. From this study, the detailing in fault modelling has a substantial impact on fault parameter identification, even if its improvement is not so expressive in fault diagnosis procedures involving standard signal processing techniques of vibration signatures.

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“…Table 1 shows the theoretical characteristic frequencies at rated load based on the geometric structure of the bearing used in this work. These frequencies are Fundamental Train Frequency (FTF), Ball Spin Frequency (BSF), Outer Raceway Fault (ORF), and Inner Raceway Fault (IRF) frequencies and have been calculated as follows: [28][29].…”

Section: Bearing Damage Index Based Envelope Power Spectrummentioning

confidence: 99%

Condition monitoring of induction motor bearing based on bearing damage index

Patel

Giri

2017

Archives of Electrical Engineering

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Abstract:The rolling element bearings are used broadly in many machinery applications. It is used to support the load and preserve the clearance between stationary and rotating machinery elements. Unfortunately, rolling element bearings are exceedingly prone to premature failures. Vibration signal analysis has been widely used in the faults detection of rotating machinery and can be broadly classified as being a stationary or non-stationary signal. In the case of the faulty rolling element bearing the vibration signal is not strictly phase locked to the rotational speed of the shaft and become "transient" in nature. The purpose of this paper is to briefly discuss the identification of an Inner Raceway Fault (IRF) and an Outer Raceway Fault (ORF) with the different fault severity levels. The conventional statistical analysis was only able to detect the existence of a fault but unable to discriminate between IRF and ORF. In the present work, a detection technique named as bearing damage index (BDI) has been proposed. The proposed BDI technique uses wavelet packet node energy coefficient analysis method. The well-known combination of Hilbert transform (HT) and Fast Fourier Transform (FFT) has been carried out in order to identify the IRF and ORF faults. The results show that wavelet packet node energy coefficients are not only sensitive to detect the faults in bearing but at the same time they are able to detect the severity level of the fault. The proposed bearing damage index method for fault identification may be considered as an 'index' representing the health condition of rotating machines.

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Fault Severity Estimation of Rotating Machinery Based on Residual Signals

Cited by 12 publications

References 20 publications

Feature extraction and fault severity classification in ball bearings

Feature extraction and fault severity classification in ball bearings

Investigation of Fault Modeling in the Identification of Bearing Wear Severity

Condition monitoring of induction motor bearing based on bearing damage index

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