There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

A review on machinery diagnostics and prognostics implementing condition-based maintenance

Remaining useful life estimation - A review on the statistical data driven approaches

A survey of maintenance policies of deteriorating systems

Gearbox failure diagnosis based on vector autoregressive modelling of vibration data and dynamic principal component analysis

This paper comprehensively investigates the parametric effects of differently located revolute clearance joints on the dynamic behavior of planar deployable structure based on scissor-like element

Dynamic characteristics of planar linear array deployable structure based on scissor-like element with differently located revolute clearance joints:

Comparison of two maintenance policies for a multi-unit system considering production and demand rates

Due to the advancements in data measurement and computer technology, automated data collection from multiple sensors has become common in recent years. However, very few papers have dealt with the cost-optimal early fault detection of gearboxes, condition based maintenance policy, and remaining useful life prediction when multiple sensors are used for data collection under varying load. The novel approach presented here is based on vector autoregressive vibration signal modeling and continuous time hidden Markov modeling using the optimal Bayesian control technique. System condition is modeled using a continuous time Markov chain with three states, namely, unobservable healthy state 0, unobservable warning state 1 and observable failure state 2. Model parameters are calculated using the expectation-maximization algorithm. The optimal control policy for the three-state model is represented by a Bayesian control chart for a multivariate observation process. The chart monitors the posterior probability that ...

Viliam Makis

Papers

Gearbox failure diagnosis based on vector autoregressive modelling of vibration data and dynamic principal component analysis

Dynamic characteristics of planar linear array deployable structure based on scissor-like element with differently located revolute clearance joints:

Comparison of two maintenance policies for a multi-unit system considering production and demand rates

Optimal Bayesian maintenance policy and early fault detection for a gearbox operating under varying load

A maintenance model with minimal and general repair