Fault Detection and Identification with Kernel Principal Component Analysis and Long Short-Term Memory Artificial Neural Network Combined Method

• Type: Article in International Scientific Journal
• Date: 2023

Authors

Jafari, N (Author) Other; António Mendes Lopes (Author) FEUP

Journal

Title: Axioms

Vol. 12

Final page: 583

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 0 Citations

Other information

Authenticus ID: P-00Y-HGX

DOI: 10.3390/axioms12060583

Abstract (EN): A new fault detection and identification approach is proposed. The kernel principal component analysis (KPCA) is first applied to the data for reducing dimensionality, and the occurrence of faults is determined by means of two statistical indices, T-2 and Q. The K-means clustering algorithm is then adopted to analyze the data and perform clustering, according to the type of fault. Finally, the type of fault is determined using a long short-term memory (LSTM) neural network. The performance of the proposed technique is compared with the principal component analysis (PCA) method in early detecting malfunctions on a continuous stirred tank reactor (CSTR) system. Up to 10 sensor faults and other system degradation conditions are considered. The performance of the LSTM neural network is compared with three other machine learning techniques, namely the support vector machine (SVM), K-nearest neighbors (KNN) algorithm, and decision trees, in determining the type of fault. The results indicate the superior performance of the suggested methodology in both early fault detection and fault identification.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 15