Chiang, L. H., Russell, E. L., & Braatz, R. D. (2000). Fault detection and diagnosis in industrial systems: Springer Science & Business Media.
Downs, J. J., & Vogel, E. F. (1993). A plant-wide industrial process control problem. Computers chemical engineering, 17(3), 245-255. 
Gajjar, S., Kulahci, M., & Palazoglu, A. (2018). Real-time fault detection and diagnosis using sparse principal component analysis. Journal of Process Control, 67, 112-128. 
Kano, M., Tanaka, S., Hasebe, S., Hashimoto, I., & Ohno, H. (2003). Monitoring independent components for fault detection. AIChE Journal, 49(4), 969-976. 
Ku, W., Storer, R. H., & Georgakis, C. (1995). Disturbance detection and isolation by dynamic principal component analysis. Chemometrics intelligent laboratory systems, 30(1), 179-196. 
Lyman, P. R., Georgakis, C. J. C., & engineering, c. (1995). Plant-wide control of the Tennessee Eastman problem. 19(3), 321-331. 
Qin, S. J. (2003). Statistical process monitoring: basics and beyond. Journal of Chemometrics: A Journal of the Chemometrics Society, 17(8‐9), 480-502. 
Qin, S. J. (2012). Survey on data-driven industrial process monitoring and diagnosis. Annual reviews in control, 36(2), 220-234. 
Shang, C., Yang, F., Gao, X., Huang, X., Suykens, J. A., & Huang, D. (2015). Concurrent monitoring of operating condition deviations and process dynamics anomalies with slow feature analysis. AIChE Journal, 61(11), 3666-3682. 
Shardt, Y. A.W. (2015). Statistics for Chemical and Process Engineers: Springer.
Shardt, Y. A.W., Zhao, Y., Qi, F., Lee, K., Yu, X., Huang, B., & Shah, S. (2012). Determining the state of a process control system: Current trends and future challenges. The Canadian Journal of Chemical Engineering, 90(2), 217-245. 
Valle, S., Li, W., & Qin, S. J. (1999). Selection of the number of principal components: the variance of the reconstruction error criterion with a comparison to other methods. Industrial Engineering Chemistry Research, 38(11), 4389-4401. 
Wise, B. M., Ricker, N., Veltkamp, D., & Kowalski, B. R. (1990). A theoretical basis for the use of principal component models for monitoring multivariate processes. Process control quality, 1(1), 41-51. 
Wiskott, L., & Sejnowski, T. J. (2002). Slow feature analysis: Unsupervised learning of invariances. Neural computation, 14(4), 715-770.