Data-driven fault diagnosis and prognosis for process faults using principal component analysis and extreme learning machine

This paper presents a data-driven fault diagnosis and prognosis method for multi-dimensional process faults. Fault detection is carried out using a principal component analysis (PCA) model of the normal process operation data. Fault diagnosis is carried out using the fault reconstruction approach. A method for formulating the fault direction matrix for process faults is proposed. The first loading vector from the PCA model of the fault data is used to construct the fault direction matrix. The reconstructed fault magnitudes are then used to develop data-driven fault prognosis models. Both linear autoregressive models and extreme learning machine (ELM) models are developed for fault prognosis. However, linear autoregressive models fail to give acceptable long range prediction. ELM models can give accurate long range predictions of fault magnitudes and can be used in process fault prognosis. The proposed methods are demonstrated on a simulated continuous stirred tank reactor process.