Network diagnosis refers to the process of identifying the cause of faults in the network. A cause

4 TWO-STEP FUSION DIAGNOSIS ALGORITHM

The two-step fusion diagnosis algorithm block diagram is the figure 2. The electric actuator’s fault diagnosis system of the cascade control system includes three modules: data level fusion module, neural network diagnosis module and D-S evidence theory fusion diagnosis module. At first, the data collected in the field engineering gets into the faults diagnosis module in the computer after A/D transformation, and each sensor’s characteristic signal is gotten, then the characteristic signal is sent into the neural network diagnosis module. The well trained network classed the fault pattern and identified initiatively by use of the diagnosis inference knowledge accumulated in the network structure, weight value and threshold value, the signal is mapped into various faults basic probability assignment value by the neural network, the gets into the D-S evident theory fusion module to fuse at the final step, and the fusion result combines the expert knowledge to judge the system’s operating state, if there is some fault, the corresponding process order is sent out.

Monitoring

What are the key benefits of Network Monitoring?

The most important benefit of network monitoring is visibility. Having an easy-to-understand, at-a-glance picture of all connected devices across the organization is important, but visualizing the flow of data across devices and networks enables network administrators to quickly identify and remediate any problems – emergent or potential – before outages occur.
Automating this critical task leads to a higher utilization of critical IT resources. Time not spent putting out network fires can be spent working on projects that create bottom-line value for the organization.
Network monitoring also provides an early warning indication of the utilization of existing infrastructure, giving IT a heads-up indicating the need for upgrading or adding capacity to given network components.
Finally, by comparing traffic to known baselines for a given time of day and season, network monitoring tools can identify unexpected spikes in network traffic that can indicate a problem brewing, whether due to increased demand or cyberattack.