Induction Motor Bearing Fault Detection with Non-stationary Signal Analysis

Yang, Da-Wei

doi:10.1109/icmech.2007.4279981

Cited by 12 publications

(6 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Literature survey (Yang and Tse, 2003; Yang, 2007) revealed that DRTs can be effectively used to form a compact feature set by selecting a few sensitive features from a large data set. This can be effectively used for bearing condition classification.…”

Section: Singular Value Decompositionmentioning

confidence: 99%

“…They verified the effectiveness of the proposed scheme by simulated and experimental results. Yang (2007) used CWT as a signal pre-processor and SVD as feature extraction technique to determine bearing fault features. The simulated and experimental bearing results showed that the proposed approach is effective in identifying bearing faults.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Classification of rolling element bearing fault using singular value

Kumar¹,

Pai²,

Sriram³

2019

JQME

View full text Add to dashboard Cite

Purpose The purpose of this paper is to classify different conditions of the rolling element bearing (REB) using vibration signals acquired from a customized bearing test rig. Design/methodology/approach An effort has been made to develop health index (HI) based on singular values of the statistical features to classify different conditions of the REB. The vibration signals from the normal bearing (N), bearing with defect on ball (B), bearing with defect on inner race (IR) and bearing with defect on outer race (OR) have been acquired from a customized bearing test rig under variable load and speed conditions. These signals were subjected to “modified kurtosis hybrid thresholding rule” (MKHTR)-based denoising. The denoised signals were decomposed using discrete wavelet transform. A total of 17 statistical features have been extracted from the wavelet coefficients of the decomposed signal. Findings Singular values of the statistical features can be effectively used for REB classification. Practical implications REB are critical components of rotary machinery right across the industrial sectors. It is a well-known fact that critical bearing failures causes major breakdowns resulting in untold and most expensive downtimes that should be avoided at all costs. Hence, intelligently based bearing failure diagnosis and prognosis should be an integral part of the asset maintenance and management activity in any industry using rotary machines. Originality/value It is found that singular values of the statistical features exhibit a constant value and accordingly can be assigned to each type of bearing fault and can be used for fault characterization in practical applications. The effectiveness of this index has been established by applying this to data from Case Western Reserve University data base which is a standard bench mark data for this application. HIs minimizes the computation time when compared to fault diagnosis using soft computing techniques.

show abstract

Section: Singular Value Decompositionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Classification of rolling element bearing fault using singular value

Kumar¹,

Pai²,

Sriram³

2019

JQME

View full text Add to dashboard Cite

show abstract

“…It divulges the combination with advanced digital signal processing (DSP), Fuzzy logic, Neural network, computerized data acquisition and processing. The illustration is the present way of analysing the fault diagnosis through spectral analysis of acquired signal (Mohamed EI Hachemi, 2000;Yang, 2007). This scheme has the merits like low cost and easy operation.…”

Section: Induction Motor Faultmentioning

confidence: 99%

Industrial drive fault diagnosis through vibration analysis using wavelet transform

Jayakumar

Thangavel²

2015

Journal of Vibration and Control

View full text Add to dashboard Cite

The fault diagnosis of electrical machines is significant to reduce the costs of maintenance through early detection of faults, which could be expensive to service. The idea of the research is that the advanced signal processing techniques called wavelet transform is exercised to haul out the faults from the vibration and current signal and a spectral component is being received to diagnose the condition of the machine. The existing system used wavelet transforms for the analysis of the stator faults and rotor faults are taken as a case study to prove the wavelet techniques for fault diagnosis. The proposed investigation of vibration analysis for the induction machines is done through frequency pattern using the decomposition of wavelet packet. The wavelet coefficients for the vibration have been extracted over a wide range of signals and the analysis could be percolated on the frequency domain through HAAR wavelet. In this paper, healthy and unhealthy motor with two broken ball bearings are used to estimate the vibration and current spectrum. Experimentally observed vibration and current signals are transformed into power spectral density through approximate and detailed coefficients with the help of MATLAB tools and the steady state rotor frequency was used to introduce a new frequency pattern for fault diagnosis.

show abstract

“…The method is discussed in &5. However it should be noted that most studies that feature application of PCA for fault detection apply the standard procedure and the use of the modified one is an enhancement which makes the fault recognition possible [23][24][25]27,32].…”

Section: And1 Introductionmentioning

confidence: 99%

An automated procedure for detection and identification of ball bearing damage using multivariate statistics and pattern recognition

Trendafilova

2010

Mechanical Systems and Signal Processing

View full text Add to dashboard Cite

The Strathprints institutional repository (https://strathprints.strath.ac.uk) is a digital archive of University of Strathclyde research outputs. It has been developed to disseminate open access research outputs, expose data about those outputs, and enable the management and persistent access to Strathclyde's intellectual output. Αβστραχτ.Τηισ παπερ συγγεστσ αν αυτοmατεδ αππροαχη φορ φαυλτ δετεχτιον ανδ χλασσιφιχατιον ιν ρολλερ βεαρινγσ, ωηιχη ισ βασεδ ον παττερν ρεχογνιτιον ανδ πρινχιπαλ χοmπονεντσ αναλψσισ οφ τηε mεασυρεδ ϖιβρατιον σιγναλσ. Τηε σιγναλσ ρεχορδεδ αρε πρε−προχεσσεδ αππλψινγ α ωαϖελετ τρανσφορm ιν ορδερ το εξτραχτ τηε αππροπριατε ηιγη φρεθυενχψ (δεταιλεδ) αρεα νεεδεδ φορ βαλλ βεαρινγ φαυλτ δετεχτιον. Τηισ ισ φολλοωεδ βψ α παττερν ρεχογνιτιον (ΠΡ) προχεδυρε υσεδ το ρεχογνισε βετωεεν σιγναλσ χοmινγ φροm ηεαλτηψ βεαρινγσ ανδ τηοσε γενερατεδ φροm διφφερεντ βεαρινγ φαυλτσ. Φουρ χατεγοριεσ οφ σιγναλσ αρε χονσιδερεδ, ναmελψ νο φαυλτ σιγναλσ (φροm α ηεαλτηψ βεαρινγ) ιννερ ραχε φαυλτ, ουτερ ραχε φαυλτ ανδ ρολλινγ ελεmεντ φαυλτ σιγναλσ. Τηε ΠΡ προχεδυρε υσεσ τηε φιρστ σιξ πρινχιπαλ χοmπονεντσ εξτραχτεδ φροm τηε σιγναλσ αφτερ α προπερ πρινχιπαλ χοmπονεντ αναλψσισ (ΠΧΑ). Ιν τηισ ωορκ α mοδιφιεδ ΠΧΑ ισ συγγεστεδ ωηιχη ισ mυχη mορε αππροπριατε φορ χατεγοριχαλ δατα. Τηε χοmβινατιον οφ τηε mοδιφιεδ ΠΧΑ ανδ τηε ΠΡ mετηοδ ενσυρεσ τηατ τηε φαυλτ ισ αυτοmατιχαλλψ δετεχτεδ ανδ χλασσιφιεδ το ονε οφ τηε χονσιδερεδ φαυλτ χατεγοριεσ. Τηε mετηοδ συγγεστεδ δοεσ νοτ ρεθυιρε τηε κνοωλεδγε/ 1 δετερmινατιον οφ τηε σπεχιφιχ φαυλτ φρεθυενχιεσ ανδ/ορ ανψ εξπερτ αναλψσισ: ονχε τηε σιγναλ φιλτερινγ ισ δονε ανδ τηε ΠΧ'σ αρε φουνδ τηε ΠΡ mετηοδ αυτοmατιχαλλψ γιϖεσ τηε ανσωερ ιφ τηερε ισ α φαυλτ πρεσεντ ανδ ιτσ τψπε.Κεψωορδσ: φαυλτ δετεχτιον, ϖιβρατιον−βασεδ mονιτορινγ, βαλλ βεαρινγ φαυλτ δετεχτιον, σιγναλ αναλψσισ, παττερν ρεχογνιτιον, πρινχιπαλ χοmπονεντ αναλψσισ 2

show abstract

Induction Motor Bearing Fault Detection with Non-stationary Signal Analysis

Cited by 12 publications

References 11 publications

Classification of rolling element bearing fault using singular value

Classification of rolling element bearing fault using singular value

Industrial drive fault diagnosis through vibration analysis using wavelet transform

An automated procedure for detection and identification of ball bearing damage using multivariate statistics and pattern recognition

Contact Info

Product

Resources

About