A Novel Sparse Bayesian Learning and Its Application to Fault Diagnosis for Multistation Assembly Systems

• URL: http://arxiv.org/abs/2210.16176v1
• Date: Fri, 28 Oct 2022 14:47:51 GMT
• Title: A Novel Sparse Bayesian Learning and Its Application to Fault Diagnosis for Multistation Assembly Systems
• Authors: Jihoon Chung, Bo Shen, and Zhenyu (James) Kong
• Abstract summary: This paper addresses the problem of fault diagnosis in multistation assembly systems. Fault diagnosis is to identify process faults that cause the excessive dimensional variation of the product using dimensional measurements.
• Score: 5.225026952905702
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper addresses the problem of fault diagnosis in multistation assembly systems. Fault diagnosis is to identify process faults that cause the excessive dimensional variation of the product using dimensional measurements. For such problems, the challenge is solving an underdetermined system caused by a common phenomenon in practice; namely, the number of measurements is less than that of the process errors. To address this challenge, this paper attempts to solve the following two problems: (1) how to utilize the temporal correlation in the time series data of each process error and (2) how to apply prior knowledge regarding which process errors are more likely to be process faults. A novel sparse Bayesian learning method is proposed to achieve the above objectives. The method consists of three hierarchical layers. The first layer has parameterized prior distribution that exploits the temporal correlation of each process error. Furthermore, the second and third layers achieve the prior distribution representing the prior knowledge of process faults. Then, these prior distributions are updated with the likelihood function of the measurement samples from the process, resulting in the accurate posterior distribution of process faults from an underdetermined system. Since posterior distributions of process faults are intractable, this paper derives approximate posterior distributions via Variational Bayes inference. Numerical and simulation case studies using an actual autobody assembly process are performed to demonstrate the effectiveness of the proposed method.

Related papers

• Graph Spatiotemporal Process for Multivariate Time Series Anomaly Detection with Missing Values [67.76168547245237]
  We introduce a novel framework called GST-Pro, which utilizes a graphtemporal process and anomaly scorer to detect anomalies. Our experimental results show that the GST-Pro method can effectively detect anomalies in time series data and outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-01-11T10:10:16Z)
• Continual learning for surface defect segmentation by subnetwork creation and selection [55.2480439325792]
  We introduce a new continual (or lifelong) learning algorithm that performs segmentation tasks without undergoing catastrophic forgetting. The method is applied to two different surface defect segmentation problems that are learned incrementally. Our approach shows comparable results with joint training when all the training data (all defects) are seen simultaneously.
  arXiv  Detail & Related papers  (2023-12-08T15:28:50Z)
• Anomaly Detection via Learning-Based Sequential Controlled Sensing [25.282033825977827]
  We address the problem of detecting anomalies among a set of binary processes via learning-based controlled sensing. To identify the anomalies, the decision-making agent is allowed to observe a subset of the processes at each time instant. Our objective is to design a sequential selection policy that dynamically determines which processes to observe at each time.
  arXiv  Detail & Related papers  (2023-11-30T07:49:33Z)
• A Sparse Bayesian Learning for Diagnosis of Nonstationary and Spatially Correlated Faults with Application to Multistation Assembly Systems [3.4991031406102238]
  This article proposes a novel fault diagnosis method: clustering spatially correlated sparse Bayesian learning (CSSBL) The proposed method's efficacy is provided through numerical and real-world case studies utilizing an actual autobody assembly system. The generalizability of the proposed method allows the technique to be applied in fault diagnosis in other domains, including communication and healthcare systems.
  arXiv  Detail & Related papers  (2023-10-20T23:56:53Z)
• Interactive System-wise Anomaly Detection [66.3766756452743]
  Anomaly detection plays a fundamental role in various applications. It is challenging for existing methods to handle the scenarios where the instances are systems whose characteristics are not readily observed as data. We develop an end-to-end approach which includes an encoder-decoder module that learns system embeddings.
  arXiv  Detail & Related papers  (2023-04-21T02:20:24Z)
• Multi-scale Fusion Fault Diagnosis Method Based on Two-Dimensionaliztion Sequence in Complex Scenarios [0.0]
  Rolling bearings are critical components in rotating machinery, and their faults can cause severe damage. Early detection of abnormalities is crucial to prevent catastrophic accidents. Traditional and intelligent methods have been used to analyze time series data, but in real-life scenarios, sensor data is often noisy and cannot be accurately characterized in the time domain. This paper proposes an improved convolutional neural network method with a multi-scale feature fusion model and deep learning compression techniques for deployment in industrial scenarios.
  arXiv  Detail & Related papers  (2023-04-11T13:05:50Z)
• Power System Anomaly Detection and Classification Utilizing WLS-EKF State Estimation and Machine Learning [0.0]
  Power system state estimation is being faced with different types of anomalies. These might include bad data caused by gross measurement errors or communication system failures. Considering power grid as a cyber physical system, state estimation becomes vulnerable to false data injection attacks.
  arXiv  Detail & Related papers  (2022-09-26T12:24:59Z)
• 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)
• Robust Calibration with Multi-domain Temperature Scaling [86.07299013396059]
  We develop a systematic calibration model to handle distribution shifts by leveraging data from multiple domains. Our proposed method -- multi-domain temperature scaling -- uses the robustness in the domains to improve calibration under distribution shift.
  arXiv  Detail & Related papers  (2022-06-06T17:32:12Z)
• Factual Error Correction for Abstractive Summaries Using Entity Retrieval [57.01193722520597]
  We propose an efficient factual error correction system RFEC based on entities retrieval post-editing process. RFEC retrieves the evidence sentences from the original document by comparing the sentences with the target summary. Next, RFEC detects the entity-level errors in the summaries by considering the evidence sentences and substitutes the wrong entities with the accurate entities from the evidence sentences.
  arXiv  Detail & Related papers  (2022-04-18T11:35:02Z)
• Adversarial System Variant Approximation to Quantify Process Model Generalization [2.538209532048867]
  In process mining, process models are extracted from event logs and are commonly assessed using multiple quality dimensions. A novel deep learning-based methodology called Adversarial System Variant Approximation (AVATAR) is proposed to overcome this issue.
  arXiv  Detail & Related papers  (2020-03-26T22:06:18Z)

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.