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Fault diagnosis is a major concern of the prognostics and health management of rotating machinery. Current practice in fault diagnosis is often challenged by the non-normality, multimodality, and nonlinearity of machinery health monitoring signals and their extracted features. A single classifier used in fault diagnosis fails when all these challenges exist. Thus, in this paper a hybrid ensemble learning...
Rolling bearing is one of the most commonly used components in rotating machinery. It is easy to be damaged which can cause mechanical fault. Thus, it is significance to study fault diagnosis technology on rolling bearing. This paper presents a Deep Boltzmann Machines (DBM) model to identify the fault condition of rolling bearing. A data set with seven fault patterns is collected to evaluate the performance...
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