120,652 research outputs found
Overview of Remaining Useful Life prediction techniques in Through-life Engineering Services
Through-life Engineering Services (TES) are essential in the manufacture and servicing of complex engineering products. TES improves support services by providing prognosis of run-to-failure and time-to-failure on-demand data for better decision making. The concept of Remaining Useful Life (RUL) is utilised to predict life-span of components (of a service system) with the purpose of minimising catastrophic failure events in both manufacturing and service sectors. The purpose of this paper is to identify failure mechanisms and emphasise the failure events prediction approaches that can effectively reduce uncertainties. It will demonstrate the classification of techniques used in RUL prediction for optimisation of products’ future use based on current products in-service with regards to predictability, availability and reliability. It presents a mapping of degradation mechanisms against techniques for knowledge acquisition with the objective of presenting to designers and manufacturers ways to improve the life-span of components
Data-driven modeling of the olfactory neural codes and their dynamics in the insect antennal lobe
Recordings from neurons in the insects' olfactory primary processing center,
the antennal lobe (AL), reveal that the AL is able to process the input from
chemical receptors into distinct neural activity patterns, called olfactory
neural codes. These exciting results show the importance of neural codes and
their relation to perception. The next challenge is to \emph{model the
dynamics} of neural codes. In our study, we perform multichannel recordings
from the projection neurons in the AL driven by different odorants. We then
derive a neural network from the electrophysiological data. The network
consists of lateral-inhibitory neurons and excitatory neurons, and is capable
of producing unique olfactory neural codes for the tested odorants.
Specifically, we (i) design a projection, an odor space, for the neural
recording from the AL, which discriminates between distinct odorants
trajectories (ii) characterize scent recognition, i.e., decision-making based
on olfactory signals and (iii) infer the wiring of the neural circuit, the
connectome of the AL. We show that the constructed model is consistent with
biological observations, such as contrast enhancement and robustness to noise.
The study answers a key biological question in identifying how lateral
inhibitory neurons can be wired to excitatory neurons to permit robust activity
patterns
Major challenges in prognostics: study on benchmarking prognostic datasets
Even though prognostics has been defined to be one of the most difficult tasks in Condition Based Maintenance (CBM), many studies have reported promising results in recent years. The nature of the prognostics problem is different from diagnostics with its own challenges. There exist two major approaches to prognostics: data-driven and physics-based models. This paper aims to present the major challenges in both of these approaches by examining a number of published datasets for their suitability for analysis. Data-driven methods require sufficient samples that were run until failure whereas physics-based methods need physics of failure progression
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