Fault diagnosis for underdetermined multistage assembly processes via an enhanced Bayesian hierarchical model

Yu, Dewen; Guo, Junkang; Zhao, Qiangqiang; Zhao, Dingtang; Hong, Jun

doi:10.1016/j.jmsy.2020.12.011

Cited by 6 publications

(3 citation statements)

References 48 publications

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“…e fault diagnosis problem is transformed into a search for a sparse solution to abnormal variance changes in process faults. Based on the non-negative property of a covariance matrix, a Bayesian hierarchical model was developed to allow sparse estimation of the variance in underdetermined multistage assembly processes [10]. Shukla et al [11] presented a novel method of sensor allocation for multistation assembly processes.…”

Section: Introductionmentioning

confidence: 99%

Sensor Optimization for Variation Diagnosis in Multistation Assembly Processes

He¹,

Li²,

Sun³

et al. 2022

Mathematical Problems in Engineering

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Appropriate sensor deployment is the key to the efficient diagnosis of product variation. Yet, optimizing sensor placement in complex manufacturing systems remains challenging. We propose a variation propagation analysis (VPA)-based sensor deployment strategy for variation diagnosis in multistation assembly processes. A state-space model is employed to analyze the influences of fixture faults and workpiece dimensional deviations on assembly variation. Based on matrix transformation, the assembly variation propagation characteristics are quantified and a VPN-based causal graph is constructed to represent the causality between assembly variation and sensor measurement. To ensure the diagnosability of over-tolerance of assembly variation (OAV) and the economics of the sensor system, an optimal sensor deployment scheme is presented. It uses the enhanced shuffled frog-leaping algorithm to minimize the OAV unobservability per unit cost and the sensor cost under the constraint of detectability. Finally, the effectiveness of the proposed approach is illustrated by a case study of sensor deployment for variation diagnosis in a multistation automobile differential assembly process.

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Section: Introductionmentioning

confidence: 99%

Sensor Optimization for Variation Diagnosis in Multistation Assembly Processes

He¹,

Li²,

Sun³

et al. 2022

Mathematical Problems in Engineering

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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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“…The identification of the fault and its type (mean shift or variance change) is accomplished by a control chart using the measured data and the inferred value from the HBN. Another HBN is proposed in [18] to deal with fault diagnosis in MMPs when the process is underdetermined. Under the assumption that less process faults are more likely to occur in MMPs, the problem of fault diagnosis is transformed into searching the sparse solution of abnormal variance changes for process faults.…”

Section: Introductionmentioning

confidence: 99%

A Sequential Inspection Procedure for Fault Detection in Multistage Manufacturing Processes

Moliner-Heredia

Bruscas-Bellido

Abellán-Nebot

et al. 2021

Sensors

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Fault diagnosis in multistage manufacturing processes (MMPs) is a challenging task where most of the research presented in the literature considers a predefined inspection scheme to identify the sources of variation and make the process diagnosable. In this paper, a sequential inspection procedure to detect the process fault based on a sequential testing algorithm and a minimum monitoring system is proposed. After the monitoring system detects that the process is out of statistical control, the features to be inspected (end of line or in process measurements) are defined sequentially according to the expected information gain of each potential inspection measurement. A case study is analyzed to prove the benefits of this approach with respect to a predefined inspection scheme and a randomized sequential inspection considering both the use and non-use of fault probabilities from historical maintenance data.

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Fault diagnosis for underdetermined multistage assembly processes via an enhanced Bayesian hierarchical model

Cited by 6 publications

References 48 publications

Sensor Optimization for Variation Diagnosis in Multistation Assembly Processes

Sensor Optimization for Variation Diagnosis in Multistation Assembly Processes

Multistage Centrifugal Pump Fault Diagnosis Using Informative Ratio Principal Component Analysis

A Sequential Inspection Procedure for Fault Detection in Multistage Manufacturing Processes

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