This paper presents a methodology for model based robust fault diagnosis and a methodology for input design to obtain optimal diagnosis of faults.
The results of application in fault diagnosis of car assembly line show the hybrid diagnosis model based on RWv-SVC and PSO is effective and feasible, the comparison between the method proposed in this paper and other ones is also given, which proves this method is better than v-SVC and Wv-SVC.
A novel intelligent diagnosis model based on wavelet support vector machine (WSVM) and immune genetic algorithm (IGA) for gearbox fault diagnosis is proposed.
Modern medicine is currently undergoing a paradigm shift from conventional disease treatments based on the diagnosis of a generalized disease state to a more personalized, customized treatment model based on molecular-level diagnosis.
Lastly, an intelligent fault diagnosis model based on MS-HO-SSA and GA-VPMCD is put forward and utilized for rotor fault diagnosis.
A novel intelligent fault diagnosis model based on multi-kernel support vector machine (MSVM) with chaotic particle swarm optimization (CPSO) for roller bearing fault diagnosis is proposed.
In this study a model based technique for fault diagnosis of rotor bearing system is described.
We propose a complete methodology for the design of a model based fault detection and diagnosis (FDD) system, which allows the reliable early detection of OFC characterized by low amplitude oscillations in a given frequency range.
