Detection of abnormality in the electrocardiogram without prior knowledge by using the quantisation error of a self-organising map, tested on the European ischaemia database

E. A. Fernández; P. Willshaw; C. A. Perazzo; R. J. Presedo; S. Barro

doi:10.1007/bf02345288

Detection of abnormality in the electrocardiogram without prior knowledge by using the quantisation error of a self-organising map, tested on the European ischaemia database

Medical & Biological Engineering & Computing ◽

10.1007/bf02345288 ◽

2001 ◽

Vol 39 (3) ◽

pp. 330-337 ◽

Cited By ~ 5

Author(s):

E. A. Fernández ◽

P. Willshaw ◽

C. A. Perazzo ◽

R. J. Presedo ◽

S. Barro

Keyword(s):

Prior Knowledge ◽

Self Organising Map ◽

Quantisation Error

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Intelligent bearing performance degradation assessment and remaining useful life prediction based on self-organising map and support vector regression

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1177/0954406217700180 ◽

2017 ◽

Vol 232 (6) ◽

pp. 1118-1132 ◽

Cited By ~ 21

Author(s):

Akhand Rai ◽

Sanjay H Upadhyay

Keyword(s):

Support Vector Regression ◽

Performance Degradation ◽

Remaining Useful Life ◽

Support Vector ◽

Rolling Element Bearings ◽

Health Index ◽

Rolling Element ◽

Useful Life ◽

Self Organising Map ◽

Quantisation Error

Rolling element bearings are critical components of rotating machines since the failure of rolling element bearings may cease the functioning of the entire equipment. The damages observed due to bearing failures are expeditious in nature and hence the need to develop an effective prognostic methodology becomes a requisite to prevent the sudden machinery breakdown. The performance degradation assessment and accurate determination of remaining useful life are the two key issues in prognostics of rolling element bearings. This paper proposes a degradation indicator based on self-organising map for the performance degradation assessment of bearings and later support vector regression is utilised to estimate the remaining useful life of bearings. The time-domain and frequency domain features extracted from the raw bearing vibration signals are supplied to the self-organising map classifier to achieve the degradation index termed as self-organising map-minimum quantisation error evolution in the paper. For estimating the remaining useful life of bearings, first the central trend of minimum quantisation error is extracted to achieve the feature vector defined as bearing health index in this work. The bearing health index is then used as input and the life percentage of the bearing is set to output in order to build the support vector regression prediction model for remaining useful life estimation of bearings. The proposed method is validated on the vibration signatures collected in a bearing test rig. The results show that the advocated method can efficiently track the evolution of deterioration and predict the remaining useful life of bearings.

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