Fault Diagnosis of Tennessee-Eastman Process Using Orthogonal Incremental Extreme Learning Machine Based on Driving Amount

Fault diagnosis is important to the industrial process. This paper proposes an orthogonal incremental extreme learning machine based on driving amount (DAOI-ELM) for recognizing the faults of the Tennessee-Eastman process (TEP). The basic idea of DAOI-ELM is to incorporate the Gram-Schmidt orthogonalization method and driving amount into an incremental extreme learning machine (I-ELM). The case study for the 2-D nonlinear function and regression problems from the UCI dataset results show that DAOI-ELM can obtain better generalization ability and a more compact structure of ELM than I-ELM, convex I-ELM (CI-ELM), orthogonal I-ELM (OI-ELM), and bidirectional ELM. The experimental training and testing data are derived from the simulations of TEP. The performance of DAOI-ELM is evaluated and compared with that of the back propagation neural network, support vector machine, I-ELM, CI-ELM, and OI-ELM. The simulation results show that DAOI-ELM diagnoses the TEP faults better than other methods.