Abnormal Condition Identification Modeling Method Based on Bayesian Network Parameters Transfer Learning for the Electro-Fused Magnesia Smelting Process

Ping, Yuan; Sun, Yufeng; Li, Hui; Wang, Fuli; Li, Hongru

doi:10.1109/access.2019.2947499

Cited by 15 publications

(13 citation statements)

References 34 publications

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“…Score-based, constraint-based or hybrid methods are used to learn the BN structure from the data. Sensor data was used in [23] to identify any abnormal condition in electro-fused magnesia smelting process. For diagnosing the root cause of defective wafers, BN structure was generated from sensor data in [10] using K2 algorithm.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

Fusion-Learning of Bayesian Network Models for Fault Diagnostics

Ademujimi

Prabhu

2021

Sensors

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Bayesian Network (BN) models are being successfully applied to improve fault diagnosis, which in turn can improve equipment uptime and customer service. Most of these BN models are essentially trained using quantitative data obtained from sensors. However, sensors may not be able to cover all faults and therefore such BN models would be incomplete. Furthermore, many systems have maintenance logs that can serve as qualitative data, potentially containing historic causation information in unstructured natural language replete with technical terms. The motivation of this paper is to leverage all of the data available to improve BN learning. Specifically, we propose a method for fusion-learning of BNs: for quantitative data obtained from sensors, metrology data and qualitative data from maintenance logs, corrective and preventive action reports, and then follow by fusing these two BNs. Furthermore, we propose a human-in-the-loop approach for expert knowledge elicitation of the BN structure aided by logged natural language data instead of relying exclusively on their anecdotal memory. The resulting fused BN model can be expected to provide improved diagnostics as it has a wider fault coverage than the individual BNs. We demonstrate the efficacy of our proposed method using real world data from uninterruptible power supply (UPS) fault diagnostics.

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Section: Background and Literature Reviewmentioning

confidence: 99%

Fusion-Learning of Bayesian Network Models for Fault Diagnostics

Ademujimi

Prabhu

2021

Sensors

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“…SDGs, FTs, BNs) of systems or subsystems can often remain the same under different operating conditions or processes. Based on this idea, some recent studies (Yuan et al, 2019;Li et al, 2019) attempted to adapt BNs from similar process.…”

Section: Rapid Model Developmentmentioning

confidence: 99%

Process fault diagnosis with model- and knowledge-based approaches: Advances and opportunities

et al. 2020

Control Engineering Practice

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“…When the data size for target modeling is small, transfer learning (TL) has brought impressive progress to the state-ofthe-art across a variety of machine learning tasks, including image classification, natural language processing, object recognition and so on [21]. To solve the abnormal condition identification modeling problem for the magnesia smelting process, the paper [22] shows a good survey for transfer learning methods using computational intelligence. TL is an AI technique, which can improve learning in the new task by transferring knowledge from the relevant learned task.…”

Section: Introductionmentioning

confidence: 99%

“…By integrating the expert knowledge, Ref. [22] and [28] presented a BN parameters transfer learning method regarding the varying balance between target and resource model. However, experimental comparisons of varying balance method and fixed (or Freeze) balance method are not investigated.…”

Section: Introductionmentioning

confidence: 99%

Bearing Fault Diagnosis Under Small Data Set Condition: A Bayesian Network Method With Transfer Learning for Parameter Estimation

et al. 2022

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Bearings are broadly applied in various types of industrial systems. Fault diagnosis, as a promising way for reliability of modern industrial internet of thing applications, has attracted increasing attention from both academia and industry fields. Being ideal modeling and inference tool in uncertainty situations, Bayesian network (BN) is becoming increasingly popular in many systems. However, in practical uncertain and complicated engineering surroundings, it's difficult or expensive to collect massive labeled fault data for the sake of fault diagnosis model learning. To address the issue of BN parameter learning under small data set conditions, this paper proposes a Varying Coefficient Transfer Learning (VCTL) algorithm based on aggregation and transfer learning, that considers both knowledge from the resource domain and the resource relevance contributions. The balancing weight function is designed to determine whether the learning task in the resource domain is activated. Relevance weight factors are proposed to measure the relevance of resource and target parameters quantitatively, by combing parameter information from resource domains with those obtained from the target domain, using maximum a posterior (MAP) or maximum likelihood estimation (MLE). Finally, the target parameters are aggregated with both the target initial parameters and the parameter knowledge from the resource domain. Based on VCTL, a bearing fault diagnosis approach is proposed and verified. The experimental results show that, under the condition of the small data set, learning accuracy of VCTL algorithm with varying coefficient aggregation is better than MLE algorithm, MAP algorithm or state-of-the-art parameter transfer method, local linear pooling transfer learning (LoLP) algorithm. Under the condition of sufficient data set, learning accuracy of VCTL algorithm approaches the classical MLE or MAP, and the correctness of the proposed algorithm is verified. Moreover, we illustrate the successful application to real-world bearing fault diagnosis case with VCTL, where we had access to expert-provided resource knowledge and real fault diagnosis data.

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Abnormal Condition Identification Modeling Method Based on Bayesian Network Parameters Transfer Learning for the Electro-Fused Magnesia Smelting Process

Cited by 15 publications

References 34 publications

Fusion-Learning of Bayesian Network Models for Fault Diagnostics

Fusion-Learning of Bayesian Network Models for Fault Diagnostics

Process fault diagnosis with model- and knowledge-based approaches: Advances and opportunities

Bearing Fault Diagnosis Under Small Data Set Condition: A Bayesian Network Method With Transfer Learning for Parameter Estimation

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