Steam boilers are considered as a core of any steam power plant. Boilers are subjected to various types of trips leading to shut down of the entire plant. The tube leakage is the worse among the common boiler faults, where the shutdown period lasts for around four to five days. This paper describes the rules of the Artificial Intelligent Systems to diagnosis the boiler variables prior to tube leakage occurrence. An Intelligent system based on Artificial Neural Network was designed and coded in MATLAB environment. The ANN was trained and validated using real site data acquired from coal fired power plant in Malaysia. Ninety three boiler operational variables were identified for the present investigation based on the plant operator experience. Various neural networks topology combinations were investigated. The results showed that the NN with two hidden layers performed better than one hidden layer using Levenberg-Maquardt training algorithm. Moreover, it was noticed that hyperbolic tangent function for input and output nodes performed better than other activation function types.
|Journal||MATEC Web of Conferences|
|Publication status||Published - 01 Jan 2014|
|Event||4th International Conference on Production, Energy and Reliability, ICPER 2014 - Kuala Lumpur, Malaysia|
Duration: 03 Jun 2014 → 05 Jun 2014
All Science Journal Classification (ASJC) codes
- Materials Science(all)