First-order Layer in Artificial Pain Pathway

Abstract

The neural mechanisms involved in pain perception consist of a pathway which carry signals from the periphery to the cerebral cortex. First-order pain neurons transduce the potentially damaging stimuli detected by the sensorial extremes into long-ranging electrical signals that are transmitted to higher order neurons where the organisation is more heterarchical, especially in the cerebral cortex. However, the first order neurones, as their name states, have a degree of branching which clearly identifies them as hierarchical elements in the arrangement of pain pathway. This research aims to develop an artificial neural pain pathway that mimics this biological process, in particular the first order neurones. First, the research proposes the periodogram method on the condition monitoring data with a minor malfunction and operational damage. As the pain is associated with actual or potential tissue damage, using such data from a machinery system can provide insights which can be used to improve the computational effectiveness. Then, a one-dimensional convolutional neural network model is introduced to represent the second and third orders of the pain pathway. The research findings found clear support for studying the similarities between the major components of biological information processing of tissue damage and statistical signal processing for damage estimation.