Jianshe Kang

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A new feature extraction method for gear fault diagnosis and prognosis. eksploatacja i niezawodnosc – Maintenance and reliability 2014; 16 (2): 295–300. Robust features are very critical to track the degradation process of a gear. They are key factors for implementing fault diagnosis and prognosis. This has driven the need in research for extracting good(More)
A novel version of multi-class classification method based on fruit fly optimization algorithm (FOA) and relevance vector machine (RVM) is proposed. The one-against-one-against-rest (OAOAR) classification model based on the traditional one-against-one (OAO) and one-against-rest (OAR) algorithm is aimed at combining the advantages of them and translates the(More)
In order to solve the failure prognostics problem of electronic system, a method of fast relevance vector machine (FRVM) based on improved fruit fly optimization algorithm (FOA) is proposed. Grey data generation operation is introduced to process the original data and the output data for enhancing the regularity and reducing the randomness. Furthermore, the(More)
It is difficult to estimate the parameters of Weibull mixtures precisely when using these distributions to analyze the reliability of equipment parts. As to this problem, an optimization model of the Weibull mixtures based on the Bayes theorem is proposed, and the cuckoo search is used to solve the optimization model. An case makes the diesel injector as(More)
In order to solve the problem of fault data with small sample and nonlinear in fault diagnosis and improve support vector machine, a fault diagnostic approach based on the multi-class classification method of One-Against-Rest (OAR) algorithm and decision tree is proposed combined with relevance vector machine. The above classification method modifies the(More)
Tracking degradation of mechanical components is very critical for effective maintenance decision making. Remaining useful life (RUL) estimation is a widely used form of degradation prediction. RUL prediction methods when enough run-to-failure condition monitoring data can be used have been fully researched, but for some high reliability components, it is(More)