A novel approach of fault detection using artificial neural network ( ANN )

It is very much required to detect and diagnose fault of machine in process industry. It will help to maintenance of machine. There are many output parameters which directly affects due to fault. It can be any form like waves, numerical values etc. It is possible to achieve this by artificial intelligence methods. The methods like artificial neural network (ANN), fuzzy logic or may be hybrid model which includes combination of two or more methods. The Neuro-fuzzy system (NFS), after training with machine condition data, is employed as a prognostic model to forecast the evolution of the machine fault state with time. NFS residuals between the actual and predicted condition data. Statistical methods with ANN or fuzzy is also possible to use to detect and diagnose fault. In this Artificial neural network (ANN) have used to detect fault. Continuously analyzing the data produces by machine it may be form of table, graph or any other form. The defect or fault of machine must be affects on result it produces so by reading or by identifying some parameters it is possible to detect fault. Multilayer backpropagation algorithm of ANN will travels the input through different layers and produces output if error is there then back propagate and change weights accordingly. ANN provide behavior of different parameters by continuously analyzing the result of machine and determine there is any effect on components of machine by using parameter values. Keywords—Artificial neural network, backpropagation algorithm,