File Name: intelligent diagnosis and prognosis of industrial networked systems .zip
Intelligent Diagnosis and Prognosis of Industrial Networked Systems proposes linear mathematical tool sets that can be applied to realistic engineering systems.
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As is known to us bearing is one of the most important components used in modern engineering machinery. Once the bearing fails, it will lead to serious consequences such as equipment damage and great economic loss. Fault diagnosis and prognosis for bearing are very important, which can effectively prevent unexpected failures and assist engineering technicians to implement targeted equipment maintenance [ 1 , 2 , 3 , 4 ]. Fault diagnosis is used for identifying its symptom and fault conditions, and prognosis approach is generally employed to implement the remaining life prediction by existing information and knowledge. Before implementing fault diagnosis and prognosis approaches, it is key for us to effectively extract the fault features of bearing signals, which have direct effects on the diagnosis precision and prediction of bearing. Therefore, the selection for signal features of bearing can comprehensively and concretely reflect the information condition of bearing from different levels [ 5 , 6 ].
System health monitoring is a set of activities performed on a system to maintain it in operable condition. Monitoring may be limited to the observation of current system states, with maintenance and repair actions prompted by these observations. Alternatively, monitoring of current system states is being augmented with prediction of future operating states and predictive diagnosis of future failure states. Such predictive diagnosis or prognosis is motivated by the need for manufacturers and other operators of complex systems to optimize equipment performance and reduce costs and unscheduled downtime. Prognosis is a difficult task requiring precise, adaptive and intuitive models to predict future machine health states. Numerous modeling techniques have been proposed in the literature and implemented in practice. This paper reviews the philosophies and techniques that focus on improving reliability and reducing unscheduled downtime by monitoring and predicting machine health.
In an era of intense competition where plant operating efficiencies must be maximized, downtime due to machinery failure has become more.
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To cut operating costs and increase revenues, industries have an urgent need to predict fault progression and remaining lifespan of industrial machines, processes, and systems. An engineer who mounts an acoustic sensor onto a spindle motor wants to know when the ball bearings will wear out without having to halt the ongoing milling processes. A scientist working on sensor networks wants to know which sensors are redundant and can be pruned off to save operational and computational overheads.
Request PDF | Intelligent Diagnosis and Prognosis of Industrial Networked Systems | In an era of intense competition where plant operating.Reuben M. 04.05.2021 at 00:01
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