Monitoring nonstationary processes based on recursive cointegration analysis and elastic weight consolidation

• URL: http://arxiv.org/abs/2101.08579v1
• Date: Thu, 21 Jan 2021 12:49:18 GMT
• Title: Monitoring nonstationary processes based on recursive cointegration analysis and elastic weight consolidation
• Authors: Jingxin Zhang and Donghua Zhou and Maoyin Chen
• Abstract summary: Traditional approaches misidentify the normal dynamic deviations as faults and thus lead to high false alarms. In this paper, RCA and RPCA are proposed to distinguish the real faults from normal systems changes. elastic weight consolidation (EWC) is employed to settle the catastrophic forgetting' issue.
• Score: 2.8102838347038617
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper considers the problem of nonstationary process monitoring under frequently varying operating conditions. Traditional approaches generally misidentify the normal dynamic deviations as faults and thus lead to high false alarms. Besides, they generally consider single relatively steady operating condition and suffer from the catastrophic forgetting issue when learning successive operating conditions. In this paper, recursive cointegration analysis (RCA) is first proposed to distinguish the real faults from normal systems changes, where the model is updated once a new normal sample arrives and can adapt to slow change of cointegration relationship. Based on the long-term equilibrium information extracted by RCA, the remaining short-term dynamic information is monitored by recursive principal component analysis (RPCA). Thus a comprehensive monitoring framework is built. When the system enters a new operating condition, the RCA-RPCA model is rebuilt to deal with the new condition. Meanwhile, elastic weight consolidation (EWC) is employed to settle the `catastrophic forgetting' issue inherent in RPCA, where significant information of influential parameters is enhanced to avoid the abrupt performance degradation for similar modes. The effectiveness of the proposed method is illustrated by a practical industrial system.

Related papers

• Preventing Model Collapse in Deep Canonical Correlation Analysis by Noise Regularization [10.216526425377149]
  Multi-View Representation Learning (MVRL) aims to learn a unified representation of an object from multi-view data. Deep Canonical Correlation Analysis (DCCA) and its variants share simple formulations and demonstrate state-of-the-art performance. We observe the issue of model collapse, em i.e., the performance of DCCA-based methods will drop drastically when training proceeds. We develop NR-DCCA, which is equipped with a novel noise regularization approach to prevent model collapse.
  arXiv  Detail & Related papers  (2024-11-01T06:02:30Z)
• Revisiting Spurious Correlation in Domain Generalization [12.745076668687748]
  We build a structural causal model (SCM) to describe the causality within data generation process. We further conduct a thorough analysis of the mechanisms underlying spurious correlation. In this regard, we propose to control confounding bias in OOD generalization by introducing a propensity score weighted estimator.
  arXiv  Detail & Related papers  (2024-06-17T13:22:00Z)
• Pattern-Based Time-Series Risk Scoring for Anomaly Detection and Alert Filtering -- A Predictive Maintenance Case Study [3.508168174653255]
  We propose a fast and efficient approach to anomaly detection and alert filtering based on sequential pattern similarities. We show how this approach can be leveraged for a variety of purposes involving anomaly detection on a large scale real-world industrial system.
  arXiv  Detail & Related papers  (2024-05-24T20:27:45Z)
• Video Anomaly Detection via Spatio-Temporal Pseudo-Anomaly Generation : A Unified Approach [49.995833831087175]
  This work proposes a novel method for generating generic Video-temporal PAs by inpainting a masked out region of an image. In addition, we present a simple unified framework to detect real-world anomalies under the OCC setting. Our method performs on par with other existing state-of-the-art PAs generation and reconstruction based methods under the OCC setting.
  arXiv  Detail & Related papers  (2023-11-27T13:14:06Z)
• GEC: A Unified Framework for Interactive Decision Making in MDP, POMDP, and Beyond [101.5329678997916]
  We study sample efficient reinforcement learning (RL) under the general framework of interactive decision making. We propose a novel complexity measure, generalized eluder coefficient (GEC), which characterizes the fundamental tradeoff between exploration and exploitation. We show that RL problems with low GEC form a remarkably rich class, which subsumes low Bellman eluder dimension problems, bilinear class, low witness rank problems, PO-bilinear class, and generalized regular PSR.
  arXiv  Detail & Related papers  (2022-11-03T16:42:40Z)
• Causality-Based Multivariate Time Series Anomaly Detection [63.799474860969156]
  We formulate the anomaly detection problem from a causal perspective and view anomalies as instances that do not follow the regular causal mechanism to generate the multivariate data. We then propose a causality-based anomaly detection approach, which first learns the causal structure from data and then infers whether an instance is an anomaly relative to the local causal mechanism. We evaluate our approach with both simulated and public datasets as well as a case study on real-world AIOps applications.
  arXiv  Detail & Related papers  (2022-06-30T06:00:13Z)
• Learning convex regularizers satisfying the variational source condition for inverse problems [4.2917182054051]
  We propose adversarial convex regularization (ACR) to learn data-driven convex regularizers via adversarial training. We leverage the variational source condition (SC) during training to enforce that the ground-truth images minimize the variational loss corresponding to the learned convex regularizer. The resulting regularizer (ACR-SC) performs on par with the ACR, but unlike ACR, comes with a quantitative convergence rate estimate.
  arXiv  Detail & Related papers  (2021-10-24T20:05:59Z)
• Stateful Offline Contextual Policy Evaluation and Learning [88.9134799076718]
  We study off-policy evaluation and learning from sequential data. We formalize the relevant causal structure of problems such as dynamic personalized pricing. We show improved out-of-sample policy performance in this class of relevant problems.
  arXiv  Detail & Related papers  (2021-10-19T16:15:56Z)
• Towards Understanding Generalization via Decomposing Excess Risk Dynamics [13.4379473119565]
  We analyze the generalization dynamics to derive algorithm-dependent bounds, e.g., stability. Inspired by the observation that neural networks show a slow convergence rate when fitting noise, we propose decomposing the excess risk dynamics. Under the decomposition framework, the new bound accords better with the theoretical and empirical evidence compared to the stability-based bound and uniform convergence bound.
  arXiv  Detail & Related papers  (2021-06-11T03:42:45Z)
• Stochastically forced ensemble dynamic mode decomposition for forecasting and analysis of near-periodic systems [65.44033635330604]
  We introduce a novel load forecasting method in which observed dynamics are modeled as a forced linear system. We show that its use of intrinsic linear dynamics offers a number of desirable properties in terms of interpretability and parsimony. Results are presented for a test case using load data from an electrical grid.
  arXiv  Detail & Related papers  (2020-10-08T20:25:52Z)
• Multiplicative noise and heavy tails in stochastic optimization [62.993432503309485]
  empirical optimization is central to modern machine learning, but its role in its success is still unclear. We show that it commonly arises in parameters of discrete multiplicative noise due to variance. A detailed analysis is conducted in which we describe on key factors, including recent step size, and data, all exhibit similar results on state-of-the-art neural network models.
  arXiv  Detail & Related papers  (2020-06-11T09:58:01Z)

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.