Zohaib Hassan secured his Ph.D. under the supervision of Prof. Hu Lisheng at the Shanghai Jiao Tong University, China. His research lies at the intersection of statistics, geometry, machine learning, and computer science. His Ph.D. investigated manifold learning for industrial data feature extraction and geometry recovery with a particular emphasis on process monitoring and fault detection. Currently, he is exploring machine learning for sleep dynamics study at the Artificial Intelligence Research Institute of Zhejiang Lab, Hangzhou, Chinaunder the mentorship of Prof. Dongping Yang.
Some of his results have been published in high impact IEEE Transactions and Elsevier journals.
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PhD in Control Science and Engineering, 2022
Shanghai Jiao Tong University, China
Masters in Electrical Engineering, 2017
COMSATS Institute of Information Technology, Pakistan
Bachelors in Electrical Engineering, 2012
University of Engineering & Technology Taxila, Pakistan
Responsibilities include:
Principal component analysis (PCA) is widely adopted in local tangent space alignment (LTSA) to estimate local tangent spaces. These estimates are only accurate when uniformly distributed data lies in or close to linear subspaces. In practice, such conditions are rarely satisfied. Therefore, this approach fails to reveal manifold intrinsic features, resulting in degraded fault detection accuracy. Considering the drawbacks, weighted linear local tangent space alignment (WLLTSA), a manifold learning method is put forward. First, weighted PCA is adopted to provide local tangent space estimates. The parameter selection criterion for the weight matrix is established by taking the context of geometric preservation into account. Second, global low dimensional coordinates are formed by aligning local coordinates with global feature space. Finally, the fault detection model is developed, and KDE is utilized to approximate confidence bounds for T^2 and SPE statistics. WLLTSA is applied to fault detection and feature extraction.
Dimensionality reduction methods based on manifold learning are widely adopted for industrial process monitoring. However, in many situations, these methods fail to preserve manifold intrinsic features in low-dimensional space, resulting in reduced process monitoring efficacy. To overcome this problem, a modified locality preserving projection (MLPP) based on the Riemannian metric is put forward. First, the Riemannian metric, which embodies a manifold’s geometric information, is estimated from process data. Then, the low dimensional embedding coordinates obtained from LPP are supplemented with an estimate of the Riemannian metric. Finally, a process monitoring model is developed, and kernel density estimation is utilized to approximate confidence bounds for T^2 and SPE statistics. The proposed MLPP method is applied to the feature extraction of the Twin-Peaks dataset, fault detection of hot strip mill, steam turbine system and Tennessee Eastman processes. The effectiveness of the MLPP method is compared with both the manifold learning and deep learning approaches. In addition, the proposed method is evaluated under various noisy conditions. The average fault detection rate of 98.9%, 99.6% and 84.4% in hot strip mill, steam turbine system and Tennessee Eastman processes, respectively, are higher than the existing methods. Quantitative results indicate the superiority of the proposed MLPP method.