Combining Relevance Vector Machines and exponential regression for bearing residual life estimation

Maio, Francesco Di; Tsui, Kwok Leung; Zio, Enrico

doi:10.1016/j.ymssp.2012.03.011

Cited by 137 publications

(63 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In particular, in this work we consider the possibility of installing up to accelerometers in different locations of the motor housing and motor supporting base plate, and the possibility of extracting from each vibrational signal, features, including statistical indicators (Di Maio et al, 2012b), Discrete Wavelet Transform (DWT) (Baraldi et al, 2012) and Wavelet Packet Transform (WPT) (Chebil, 2009). These different types of features have been considered since they have been already used in bearing diagnostic problems and they have been shown to contain information correlated with the bearing degradation.…”

Section: The Hierarchical Diagnostic Modelmentioning

confidence: 99%

Hierarchical k-nearest neighbours classification and binary differential evolution for fault diagnostics of automotive bearings operating under variable conditions

Baraldi

Cannarile

Maio

et al. 2016

Engineering Applications of Artificial Intelligence

Self Cite

113

View full text Add to dashboard Cite

show abstract

Section: The Hierarchical Diagnostic Modelmentioning

confidence: 99%

Hierarchical k-nearest neighbours classification and binary differential evolution for fault diagnostics of automotive bearings operating under variable conditions

Baraldi

Cannarile

Maio

et al. 2016

Engineering Applications of Artificial Intelligence

Self Cite

113

View full text Add to dashboard Cite

show abstract

“…27 Predicting the RUL is a very important part of condition-based maintenance. We must design the maintenance schedule based on the RUL result.…”

Section: Health Index Equation Of the Turbofan Enginementioning

confidence: 99%

A kernel principal component analysis–based degradation model and remaining useful life estimation for the turbofan engine

Feng

Xiao

Liu

et al. 2016

Advances in Mechanical Engineering

View full text Add to dashboard Cite

Remaining useful life estimation of the prognostics and health management technique is a complicated and difficult research question for maintenance. In this article, we consider the problem of prognostics modeling and estimation of the turbofan engine under complicated circumstances and propose a kernel principal component analysis-based degradation model and remaining useful life estimation method for such aircraft engine. We first analyze the output data created by the turbofan engine thermodynamic simulation that is based on the kernel principal component analysis method and then distinguish the qualitative and quantitative relationships between the key factors. Next, we build a degradation model for the engine fault based on the following assumptions: the engine has only had constant failure (i.e. no sudden failure is included), and the engine has a Wiener process, which is a covariate stand for the engine system drift. To predict the remaining useful life of the turbofan engine, we built a health index based on the degradation model and used the method of maximum likelihood and the data from the thermodynamic simulation model to estimate the parameters of this degradation model. Through the data analysis, we obtained a trend model of the regression curve line that fits with the actual statistical data. Based on the predicted health index model and the data trend model, we estimate the remaining useful life of the aircraft engine as the index reaches zero. At last, a case study involving engine simulation data demonstrates the precision and performance advantages of this prediction method that we propose. At last, a case study involving engine simulation data demonstrates the precision and performance advantages of this proposed method, the precision of the method can reach to 98.9% and the average precision is 95.8%.

show abstract

“…σ is a constant in interval (1,10). After determining the training samples and BPNN construction, BPNN is trained.…”

Section: Degradation and Rul Prediction For An Individual Componentmentioning

confidence: 99%

“…Works on this area are few and limited. Di Maio et al [1] proposed a relevance vector machines and exponential regression method to estimate bearing remaining useful life. The results show that the prediction result can be more accurate if more data is collected.…”

Section: Introductionmentioning

confidence: 99%

Joint optimization of replacement and spare ordering for critical rotary component based on condition signal to date

Chen¹,

Xu²,

Xiao³

2016

EiN

View full text Add to dashboard Cite

show abstract

Combining Relevance Vector Machines and exponential regression for bearing residual life estimation

Cited by 137 publications

References 36 publications

Hierarchical k-nearest neighbours classification and binary differential evolution for fault diagnostics of automotive bearings operating under variable conditions

Hierarchical k-nearest neighbours classification and binary differential evolution for fault diagnostics of automotive bearings operating under variable conditions

A kernel principal component analysis–based degradation model and remaining useful life estimation for the turbofan engine

Joint optimization of replacement and spare ordering for critical rotary component based on condition signal to date

Contact Info

Product

Resources

About