Network Equipment Failure
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For telecom firms, minimizing network downtown is a constant challenge. More than often, equipment failure at various locations and rectification works are required on ad-hoc basis. Due to highly competitive industry, the providers needs to be deliver uninterrupted, high quality connectivity to its customers. Hence, huge budget and resources are allocated to avoid network failures.
Prediction and further rectification of such failures will reduce the downtime. Furthermore, it will increase the efficiency in regards to management of budget and resources.
By studying network equipments activity and operation parameters data, many of the equipment related failures can be predicted to ensure minimal downtime.
Due to massiveness of data generated at very high velocity, an adaptive algorithm built on a rule based analysis with regression techniques and best fit line method have performed well to predict the equipment failure. The warning occurrence pattern can be stored and a threshold for alarm signal triggering can be set. Symptoms of a failure starting as early as one week before the failure can be achieved.