Fault Detection Engine for Big Data Analytics and Its Applications

Abstract

Due to their reliance on several distributed nodes, big data systems are notoriously fragile. Maintaining availability, reliability, and continuous performance in the face of failures is the primary function of fault-tolerant systems. Fault prediction is an important subject for industry because it enables businesses to make major time and expense savings by offering efficient methods for predictive maintenance. To that end, there were two objectives: first to develop prediction techniques that would detect failures in doors from diagnostic data at an early stage; and second, to describe failures in terms of characteristics that differentiate them from normal behaviour. Some elements of the solution suggested merit special consideration. They provide the foundation for an efficient data pre-processing technique in which the action of a system is described in a specific timeframe by a set of appropriate statistics. This method significantly mitigates problems relating to data noise and errors, allowing an efficient outer detection. In our opinion, all of this is the basis of a general approach for advanced prognostic systems. In no default scenarios, so whether the percentage goes up or down, the outcome of the percentage is consistent with the rational regression percentage curve. The defect can be detected with a visualized data representation and with the percentage variation. We notice that ignition timer cylinders 2 and 3 in time interval 20–23 are not read in conformity with the sample regression form, and that the other sensors read on the graph intersect in calculation to get better accuracy.