In this article, a novel data-driven fault diagnosis method by combining deep canonical variate analysis and Fisher discriminant analysis (DCVA-FDA) is proposed for complex industrial processes. Inspired by the recently developed deep canonical correlation analysis, a new nonlinear canonical variate analysis (CVA) called DCVA is first developed by incorporating deep neural networks into CVA. Based on DCVA, a residual generator is designed for the fault diagnosis process. FDA is applied in the feature space spanned by residual vectors. Then, a Bayesian inference classifier is performed in the reduced dimensional space of FDA to label the class of process data. A continuous stirred-tank reactor and an industrial benchmark of the Tennessee Eastman process are carried out to test the performance of DCVA-FDA fault diagnosis. The experimental results demonstrate that the proposed DCVA-FDA fault diagnosis is able to significantly improve the fault diagnosis performance when compared to other methods also examined in this article.

Currently, the offshore wind turbine has become a hot research area in the wind energy industry. Among different offshore wind turbines, floating offshore wind turbine (FOWT) can harvest abundant wind energy in deepwater areas. However, the harsh working environment will dramatically increase the maintenance cost and downtime of FOWTs. Wind turbine fault diagnosis is being regarded as an indispensable system for maintenance issues. Owing to the complexity of FOWT, it imposes an enormous challenge for effective fault diagnosis. This paper develops a novel FOWT fault diagnosis method based on a stacked denoising autoencoder (SDAE). First, a sliding window technique is adopted for time-series data to preserve temporal information. Then, SDAE is employed to extract the features from high-dimensional data. Based on the extracted features from SDAE, a classifier using multilayer perceptrons (MLP) is developed to determine the health status of the FOWT. To verify the performance of the proposed method, a FOWT simulation benchmark based on the National Renewable Energy Laboratory (NREL) FAST simulator is employed. Results show the superior performance of the proposed method by comparison with other relevant methods.

Principal component analysis (PCA) and its modified methods have been widely applied in industrial process monitoring. In practice, industrial processes are with disparate characteristics, the process monitoring system should consider as many process characteristics as possible, such as dynamic and nonlinear characteristics. In this paper, a multi-feature extraction technique based on PCA is proposed for nonlinear dynamic process monitoring. The proposed method integrates dynamic inner PCA (DiPCA), PCA and kernel PCA (KPCA) methods through a serial structure to extract the dynamic, linear and nonlinear features among the process data. Along with the proposed method, the original data space is decomposed into several orthogonal subspaces, in which abnormal variations of different features can be monitored. For real-time process monitoring, a combined Hotelling's T² statistic based on the extracted multi-feature and a squared prediction error (SPE or Q) statistic are established. Case studies on a numerical example and the Tennessee Eastman process are carried out to demonstrate the superior process monitoring performance of the proposed method compared with other relevant methods.