Fault Detection in Mobile Communication Networks Using Data Mining Techniques with Big Data Analytics

Abstract

A collection of datasets is Big data so that it to be To process huge and complex datasets becomes difficult. so that using big data analytics the process of applying huge amount of datasets consists of many data types is the big data on-hand theoretical models and technique tools. The technology of mobile communication introduced low power ,low price and multi functional devices. A ground for data mining research is analysis of data pertaining to mobile communication is used. theses mining frequent patterns and clusters on data streams collaborative filtering and analysis of social network. The data analysis of mobile communication has been often used as a background application to motivate many technical problem in data mining research. This paper refers in mobile communication networking to find the fault nodes between source to destination transmission using data mining techniques and detect the faults using outliers. outlier detection can be used to find outliers in multivariate data in a simple ensemble way. Network analysis with R to build a network.

Recent Advances in Intelligent Algorithms for Fault Detection and Diagnosis

“…To simulate the failures of the instrumentation used, such as transmitters and valves, simulated deviations are introduced in their parameters as additive disturbances of step type and an amplitude of 10% in the instrument parameters, such as failures in the transmitter span (FST), Transmitter Span Dead Zone Failure (ZMST), Transmitter Zero Calibration Failure (FCCT), Transmitter Zero Dead Zone Failure (FZMCT), Valve Span Failure (FSV), and failure in the valve span dead zone (FZMSV) [ 7 , 25 , 32 , 33 ]. After simulating the deviations in the parameters and with the specified amplitudes, the outputs of each of the previously found models are obtained, measuring the absolute value of the error and thus obtaining the failure code to determine if there is any abnormality in the process [ 34 , 35 , 36 ]. If the failures occur over some variations in values in which they have been modeled, there are offline models, and they will will only be used if the main models fail to detect the origin of the existing failure, according to the failure code returned by the system’s detection [ 7 , 21 , 37 , 38 , 39 , 40 , 41 ].…”

“…One further thing that should be taken into consideration for future research is the incorporation of non-linear valves or transmitters that have a temporal delay and the investigation of their behavior. Taking into consideration the aforementioned, the study in question suggests using the approach of resilient transition structure for defect identification and diagnostics [ 36 , 55 , 57 , 58 ]. Transition structures are recommended as an option for defect detection because of the features of the technique and the systems to which it is applied (change in the operating regime).…”