Fault diagnostic model for rotating machinery based on principal component analysis and neural network

In the current economic challenge, methods to accurately predict system failure has become a holy grail in maintenance with the goal to reduce the cost of unavailability due to unscheduled shutdown. This has led to the current research with the aim to achieve a more accurate fault diagnosis for rotating machinery using a neural network (NN) with principal component analysis (PCA) as a pre-processing step to fuse multiple sensor data. The multisensor data fusion has been proven to improve the fault detection ability for machinery compared to single source condition monitoring. In this paper, an NN-based methodology is presented, where PCA is applied as preprocessing step to detect the rotating machinery faults during operation. The effectiveness of the proposed model is illustrated by a case study on two shaft industrial gas turbine where the real-time performance monitoring data collected from the plant and used to train and test the proposed algorithm. The analysis results show that the PCA-based fusion process has significantly enhanced the performance of NNbased model when compared against NN algorithm without PCA. © 2006-2016 Asian Research Publishing Network (ARPN). All rights reserved.