Monitoring and Diagnosis of Multistage Manufacturing Processes Using Hierarchical Bayesian Networks

Mondal, Partha Protim; Ferreira, Placid; Kapoor, Shiv Gopal; Bless, Patrick N.

doi:10.1016/j.promfg.2021.06.007

Cited by 9 publications

(5 citation statements)

References 33 publications

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“…BN consists of nodes, arcs, and a node probability table (NPT). Arcs represent causal relationships, and NPTs represent probability tables that summarize the probabilities of occurrence between causal nodes [18,19]. BN is very useful for solving problems such as insu cient information, a posteriori inference, and the change from qualitative to quantitative problems by learning new knowledge about the relationship between posterior and prior probabilities.…”

Section: Network Security Risk Quantificationmentioning

confidence: 99%

Application of Bayesian Algorithm in Risk Quantification for Network Security

Lei

2022

Computational Intelligence and Neuroscience

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Network security risk quantification involves both technical and management aspects. Risk quantification has great uncertainty and cannot be fully quantified. Therefore, the fully objective realization of network information security risk quantification is not yet mature. This paper analyzes and quantifies the network security risks caused by various threat sources through a network security risk quantification model based on the Bayesian algorithm. By combining expert knowledge, the conditional probability matrix under the inference rule of the Bayesian algorithm is clarified, and the subjective judgment information of experts on the damage degree of the target information system is synthesized into the prior information system of network security threat. The Bayesian algorithm is used to realize the observation node of objective assessment information and combining subjective security threat levels to achieve continuity and accumulation of security assessments. The error is about 3%, which has a very good effect on the quantification of network security risk.

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Section: Network Security Risk Quantificationmentioning

confidence: 99%

Application of Bayesian Algorithm in Risk Quantification for Network Security

Lei

2022

Computational Intelligence and Neuroscience

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“…Liu et al [8] employed a deep belief network to extract primary data features from manufacturing process raw data, thus creating an online process diagnosis model for highly effective quality recognition. In a separate investigation, Mondal et al [9] proposed a hierarchical Bayesian network framework for multivariate and multistage process fault diagnosis. Furthermore, Yu et al [3] developed a stacked denoising self-encoder technique for identifying patterns of defects in manufacturing processes.…”

Section: Introductionmentioning

confidence: 99%

“…Ma et al [15] examined quality anomaly detection, constructing a Gaussian mixture model, and refining the Mahalanobis distance. Meanwhile, Mondal et al [9] proposed a unified framework using a bi-level Bayesian network to determine the absolute mean deviation of the feature state through inferred state distributions produced by HBN. This framework also produces a control chart to identify process changes and diagnose their root causes.…”

Section: Introductionmentioning

confidence: 99%

Product Quality Anomaly Recognition and Diagnosis Based on DRSN-SVM-SHAP

Liu,

Wang,

Zhang

et al. 2024

Symmetry

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Conventional quality control methodologies are inadequate for fully elucidating the aberrant patterns of product quality. A multitude of factors influence product quality, yet the limited number of controlled quality characteristics is insufficient for accurately diagnosing quality abnormalities. Additionally, there are asymmetries in data collection, data pre-processing, and model interpretation. In this context, a quality anomaly recognition and diagnosis model for the complex product manufacturing process is constructed based on a deep residual network, support vector machine (SVM), and Shapley additive explanation (SHAP). Given the numerous complex product quality characteristic indexes and unpredictable accidental factors in the production process, it is necessary to mine the deep relationship between quality characteristic data and quality state. This mining is achieved by utilizing the strong feature extraction ability of the deep residual shrinkage network (DRSN) through self-learning. The symmetry of the data within the model has also been taken into account to ensure a more balanced and comprehensive analysis. The excellent binary classification ability of the support vector machine is combined with the DRSN to identify the quality anomaly state. The SHAP interpretable model is employed to diagnose the quality anomaly problem of a single product and to identify and diagnose quality anomalies in the manufacturing process of complex products. The effectiveness of the model is validated through case analysis. The accuracy of the DRSN-SVM quality anomaly recognition model reaches 99%, as demonstrated by example analysis, and the model exhibits faster convergence and significantly higher accuracy compared with the naive Bayesian model classification and support vector machine classification models.

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“…Furthermore, not all features are best for representing centrifugal pump conditions and they can affect the condition classification accuracy of the classifier. To address this concern, feature preprocessing for discriminant feature extraction is of primary importance [ 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 ]. Several feature dimensionality reduction and discriminancy evaluation techniques have been proposed [ 36 , 37 , 38 ].…”

Section: Introductionmentioning

confidence: 99%

Multistage Centrifugal Pump Fault Diagnosis Using Informative Ratio Principal Component Analysis

Ahmad

Nguyen

Ahmad

et al. 2021

Sensors

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This study proposes a fault diagnosis method (FD) for multistage centrifugal pumps (MCP) using informative ratio principal component analysis (Ir-PCA). To overcome the interference and background noise in the vibration signatures (VS) of the centrifugal pump, the fault diagnosis method selects the fault-specific frequency band (FSFB) in the first step. Statistical features in time, frequency, and wavelet domains were extracted from the fault-specific frequency band. In the second step, all of the extracted features were combined into a single feature vector called a multi-domain feature pool (MDFP). The multi-domain feature pool results in a larger dimension; furthermore, not all of the features are best for representing the centrifugal pump condition and can affect the condition classification accuracy of the classifier. To obtain discriminant features with low dimensions, this paper introduces a novel informative ratio principal component analysis in the third step. The technique first assesses the feature informativeness towards the fault by calculating the informative ratio between the feature within the class scatteredness and between-class distance. To obtain a discriminant set of features with reduced dimensions, principal component analysis was applied to the features with a high informative ratio. The combination of informative ratio-based feature assessment and principal component analysis forms the novel informative ratio principal component analysis. The new set of discriminant features obtained from the novel technique are then provided to the K-nearest neighbor (K-NN) condition classifier for multistage centrifugal pump condition classification. The proposed method outperformed existing state-of-the-art methods in terms of fault classification accuracy.

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Monitoring and Diagnosis of Multistage Manufacturing Processes Using Hierarchical Bayesian Networks

Cited by 9 publications

References 33 publications

Application of Bayesian Algorithm in Risk Quantification for Network Security

Application of Bayesian Algorithm in Risk Quantification for Network Security

Product Quality Anomaly Recognition and Diagnosis Based on DRSN-SVM-SHAP

Multistage Centrifugal Pump Fault Diagnosis Using Informative Ratio Principal Component Analysis

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