Monitoring multimode processes: a modified PCA algorithm with continual learning ability

• URL: http://arxiv.org/abs/2012.07044v4
• Date: Mon, 26 Apr 2021 13:45:40 GMT
• Title: Monitoring multimode processes: a modified PCA algorithm with continual learning ability
• Authors: Jingxin Zhang, Donghua Zhou, and Maoyin Chen
• Abstract summary: It could be an effective manner to make local monitoring model remember the features of previous modes. A modified PCA algorithm is built with continual learning ability for monitoring multimode processes. It is called PCA-EWC, where the significant features of previous modes are preserved when a PCA model is established for the current mode.
• Score: 2.5004754622137515
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: For multimode processes, one generally establishes local monitoring models corresponding to local modes. However, the significant features of previous modes may be catastrophically forgotten when a monitoring model for the current mode is built. It would result in an abrupt performance decrease. It could be an effective manner to make local monitoring model remember the features of previous modes. Choosing the principal component analysis (PCA) as a basic monitoring model, we try to resolve this problem. A modified PCA algorithm is built with continual learning ability for monitoring multimode processes, which adopts elastic weight consolidation (EWC) to overcome catastrophic forgetting of PCA for successive modes. It is called PCA-EWC, where the significant features of previous modes are preserved when a PCA model is established for the current mode. The optimal parameters are acquired by differences of convex functions. Moreover, the proposed PCA-EWC is extended to general multimode processes and the procedure is presented. The computational complexity and key parameters are discussed to further understand the relationship between PCA and the proposed algorithm. Potential limitations and relevant solutions are pointed to understand the algorithm further. Numerical case study and a practical industrial system in China are employed to illustrate the effectiveness of the proposed algorithm.

Related papers

• Online Variational Sequential Monte Carlo [49.97673761305336]
  We build upon the variational sequential Monte Carlo (VSMC) method, which provides computationally efficient and accurate model parameter estimation and Bayesian latent-state inference. Online VSMC is capable of performing efficiently, entirely on-the-fly, both parameter estimation and particle proposal adaptation.
  arXiv  Detail & Related papers  (2023-12-19T21:45:38Z)
• Sub-linear Regret in Adaptive Model Predictive Control [56.705978425244496]
  We present STT-MPC (Self-Tuning Tube-based Model Predictive Control), an online oracle that combines the certainty-equivalence principle and polytopic tubes. We analyze the regret of the algorithm, when compared to an algorithm initially aware of the system dynamics.
  arXiv  Detail & Related papers  (2023-10-07T15:07:10Z)
• Bayesian tomography using polynomial chaos expansion and deep generative networks [0.0]
  We present a strategy combining the excellent reconstruction performances of a variational autoencoder (VAE) with the accuracy of PCA-PCE surrogate modeling. Within the MCMC process, the parametrization of the VAE is leveraged for prior exploration and sample proposals.
  arXiv  Detail & Related papers  (2023-07-09T16:44:37Z)
• Provably Efficient UCB-type Algorithms For Learning Predictive State Representations [55.00359893021461]
  The sequential decision-making problem is statistically learnable if it admits a low-rank structure modeled by predictive state representations (PSRs) This paper proposes the first known UCB-type approach for PSRs, featuring a novel bonus term that upper bounds the total variation distance between the estimated and true models. In contrast to existing approaches for PSRs, our UCB-type algorithms enjoy computational tractability, last-iterate guaranteed near-optimal policy, and guaranteed model accuracy.
  arXiv  Detail & Related papers  (2023-07-01T18:35:21Z)
• Non-stationary Reinforcement Learning under General Function Approximation [60.430936031067006]
  We first propose a new complexity metric called dynamic Bellman Eluder (DBE) dimension for non-stationary MDPs. Based on the proposed complexity metric, we propose a novel confidence-set based model-free algorithm called SW-OPEA. We show that SW-OPEA is provably efficient as long as the variation budget is not significantly large.
  arXiv  Detail & Related papers  (2023-06-01T16:19:37Z)
• Predictable MDP Abstraction for Unsupervised Model-Based RL [93.91375268580806]
  We propose predictable MDP abstraction (PMA) Instead of training a predictive model on the original MDP, we train a model on a transformed MDP with a learned action space. We theoretically analyze PMA and empirically demonstrate that PMA leads to significant improvements over prior unsupervised model-based RL approaches.
  arXiv  Detail & Related papers  (2023-02-08T07:37:51Z)
• Continual learning-based probabilistic slow feature analysis for multimode dynamic process monitoring [2.9631016562930546]
  A novel multimode dynamic process monitoring approach is proposed by extending elastic weight consolidation (EWC) to probabilistic slow feature analysis (PSFA) EWC was originally introduced in the setting of machine learning of sequential multi-tasks with the aim of avoiding catastrophic forgetting issue. The effectiveness of the proposed method is demonstrated via a continuous stirred tank heater and a practical coal pulverizing system.
  arXiv  Detail & Related papers  (2022-02-23T03:57:59Z)
• Sequential Stochastic Optimization in Separable Learning Environments [0.0]
  We consider a class of sequential decision-making problems under uncertainty that can encompass various types of supervised learning concepts. These problems have a completely observed state process and a partially observed modulation process, where the state process is affected by the modulation process only through an observation process. We model this broad class of problems as a partially observed Markov decision process (POMDP)
  arXiv  Detail & Related papers  (2021-08-21T21:29:04Z)
• Self-learning sparse PCA for multimode process monitoring [2.8102838347038617]
  This paper proposes a novel sparse principal component analysis algorithm with self-learning ability for successive modes. Different from traditional multimode monitoring methods, the monitoring model is updated based on the current model and new data when a new mode arrives.
  arXiv  Detail & Related papers  (2021-08-07T13:50:16Z)
• Recurrent Model Predictive Control [19.047059454849897]
  We propose an off-line algorithm, called Recurrent Model Predictive Control (RMPC), to solve general nonlinear finite-horizon optimal control problems. Our algorithm employs a recurrent function to approximate the optimal policy, which maps the system states and reference values directly to the control inputs.
  arXiv  Detail & Related papers  (2021-02-23T15:01:36Z)
• Stein Variational Model Predictive Control [130.60527864489168]
  Decision making under uncertainty is critical to real-world, autonomous systems. Model Predictive Control (MPC) methods have demonstrated favorable performance in practice, but remain limited when dealing with complex distributions. We show that this framework leads to successful planning in challenging, non optimal control problems.
  arXiv  Detail & Related papers  (2020-11-15T22:36:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.