Dr. Ngnassi Djami Aslain Brisco
Fault Detection and Control Systems Electric Power Systems and Control Wind Turbine Control Systems Machine Fault Diagnosis Techniques Electric Motor Design and Analysis Advanced Statistical Process Monitoring Advanced Multi-Objective Optimization Algorithms Petri Nets in System Modeling Reliability and Maintenance Optimization Multi-objective optimization Topology Optimization in Engineering Control Systems in Engineering Doubly fed electric machine Technical Engine Diagnostics and Monitoring Robot Manipulation and Learning Predictive maintenance Automotive Engineering Civil and Structural Engineering Computational Theory and Mathematics Control and Systems Engineering Electrical and Electronic Engineering Industrial and Manufacturing Engineering Mechanical Engineering Safety, Risk, Reliability and Quality Statistics, Probability and Uncertainty

Educational Journey

Université de Ngaoundéré

Doctorat/PhD in Génie Mécanique • Génie Mécanique

2021

Experience

Enseignant -Chercheur

2020 - Present • Sciences fondamentales et techniques de l'ingénieur

Editors Role

Reviewer

GJRE

2024 - Present

Research

Machine Reliability Optimization by Genetic Algorithm Approach

Article September 24, 2020

To define the reliability network of a system (machine), we start with a set of components arranged in an appropriate topology (series, parallel, or parallel-series), choose the best terms of the ratio performance / cost, and gather by links with the aim to combine them. This process requires a long time and effort, given the very large number of possible combinations, which becomes tedious for the analyst. For this reason, it is essential to use an appropriate optimization approach when designing any product. However, before trying to optimize, it is necessary to have a reliability assessment method. The objective of this paper is to display a meta-heuristic method, which is sustained on the genetic algorithm (GA) to improve the machines reliability. To achieve this objective, a methodology that consists of presenting the functionalities of genetic algorithms is developed. The result achieved is the proposal of a reliability network for the optimal solution.