Time Sequential Phase Partition and Modeling Method for Fault Detection of Batch Processes

Ye, A. Xiaofeng; Wang, B. Peiliang; Yang, Chunjie

doi:10.1109/access.2017.2778095

Cited by 4 publications

(4 citation statements)

References 31 publications

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“…Inspired by EMD, the envelope estimation function can be constructed by using the cubic spline interpolation (CSI) envelopes of both the local minimum and maximum points to solve the phase difference problem, and the interpolation-based LMD method originating from EMD has become a widely used mode. CSI is a special case of spline interpolation; when compared to Lagrange and Newton polynomials, its interpolation polynomial is smoother and has less inaccuracy [28,29], while the continuous second derivative of the CSI envelopes makes it susceptible to overshoot and undershoot issues for powerful nonstationary signals.…”

Section: The Interpolation-based Local Mean Decompositionmentioning

confidence: 99%

A Double Interpolation and Mutation Interval Reconstruction LMD and Its Application in Fault Diagnosis of Reciprocating Compressor

Zhao

Liu³

et al. 2023

Applied Sciences

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The accuracy and stability of the envelope estimation function are enduring issues throughout the research process of LMD. This paper presents double interpolation and mutation interval reconstruction local mean decomposition (DIMIRLMD) to improve the stability of the demodulation process and the accuracy of PF components. DIMIRLMD first proposes a mutation interval reconstruction envelope algorithm using extreme symmetry points to suppress the demodulation mutation phenomenon, which disturbs the stability of the demodulation process, and then selects the optimal PF component from a double interpolation PF component library based on the index of orthogonality (IO) for a better hierarchical property. DIMIRLMD was employed to analyze the simulation signal and vibration signal of a reciprocating compressor in an oversized bearing clearance state, and the results illustrate its performances are more excellent than those of three other LMD methods. Furthermore, the envelope frequency spectrum obtained from the proposed LMD presents a clear double rotation fault frequency and lower noise disturbance.

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Section: The Interpolation-based Local Mean Decompositionmentioning

confidence: 99%

A Double Interpolation and Mutation Interval Reconstruction LMD and Its Application in Fault Diagnosis of Reciprocating Compressor

Zhao

Liu³

et al. 2023

Applied Sciences

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“…On the other hand, the multiple phases inherent to batch processes also add to the difficulties against feature extraction. To mitigate it, some composite frameworks have been proposed to cluster or classify the data to each phase beforehand, and then a secondary round of modeling is applied to each group of data to estimate the distribution [35,36]. However, these routes usually involve a hierarchy of complex procedures without a convenient framework for general batch processes.…”

Section: Introductionmentioning

confidence: 99%

Time-Specific Thresholds for Batch Process Monitoring: A Study Based on Two-Dimensional Conditional Variational Auto-Encoder

Zhu,

Liu,

Lou

et al. 2024

Processes

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This paper studies the use of varying threshold in the statistical process control (SPC) of batch processes. The motivation is driven by how when multiple phases are implicated in each repetition, the distributions of the features behind vary with phases or even the time; thus, it is inconsistent to uniformly bound them by an invariant threshold. In this paper, we paved a new path for learning and monitoring batch processes based on an efficient framework integrating a model termed conditional dynamic variational auto-encoder (CDVAE). Phase indicators are first used to split the data and are then separated, serving as an extra input for the model in order to alleviate the learning complexity. Dissimilar to the routine using features across all timescales, only features relevant to local timestamps are aggregated for threshold calculation, producing a varying threshold that is more specific for the process variations occurring among the timeline. Leveraged upon this idea, a fault detection panel is devised, and a deep reconstruction-based contribution diagram is illustrated for locating the faulty variables. Finally, the comparative results from two case studies highlight the superiority in both detection accuracy and diagnostic performance.

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“…However, these adaptive model methods are always time consuming when applied online, influencing the real time performance of the quality control system. A reasonable alternative method that is well suited to the process characteristic is to divide the batch process procedure into multiple phases and assign each phase a static model, which is referred to as phase‐based model methods or phase partition methods . Clustering‐based methods, the Gaussian mixture model (GMM), and the hidden Markov model (HMM) are widely used phase partition methods.…”

Section: Introductionmentioning

confidence: 99%

“…A reasonable alternative method that is well suited to the process characteristic is to divide the batch process procedure into multiple phases and assign each phase a static model, which is referred to as phasebased model methods or phase partition methods. [13][14][15][16] Clustering-based methods, the Gaussian mixture model (GMM), and the hidden Markov model (HMM) are widely used phase partition methods. For continuous processes, phase partition methods are also used to identify different modes.…”

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confidence: 99%

Online prediction of quality‐related variables for batch processes using a sequential phase partition method

Wang

Gao

et al. 2019

Can J Chem Eng

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Batch processes inherently have multiple operation phases; different phases exhibit different characteristics. Hence, it is reasonable to partition the process into phases and build sub‐phase models for online quality prediction. To this end, a sequential phase partition method based on the information increment is proposed. To address the multiphase behaviours in batch processes, this work utilizes a new information increment index to capture the dynamic characteristics of batch processes along a time direction and divides the process into sub‐phases. Next, phase‐based multiway partial least squares (MPLS) models are built to model within‐phase characteristics and predict the quality‐related variables online. Information increment is able to exploit the process evolution by focusing on the changing variable correlations derived from two adjacent extend time slice. It directly utilizes the available process measurements of successful history batch processes without data transformation or dimensionality reduction. The method is sequential and can overcome the limits of some phase partition methods that may divide the samples with discontinuous time sequence but similar characteristics into the same phase. In addition, the information increment is capable of reflecting the change of the process intuitively with high computation efficiency. Advantages of the proposed method are illustrated by two case studies, a penicillin simulation platform and an industrial application of Escherichia coli (E. coli) fermentation, respectively.

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Time Sequential Phase Partition and Modeling Method for Fault Detection of Batch Processes

Cited by 4 publications

References 31 publications

A Double Interpolation and Mutation Interval Reconstruction LMD and Its Application in Fault Diagnosis of Reciprocating Compressor

A Double Interpolation and Mutation Interval Reconstruction LMD and Its Application in Fault Diagnosis of Reciprocating Compressor

Time-Specific Thresholds for Batch Process Monitoring: A Study Based on Two-Dimensional Conditional Variational Auto-Encoder

Online prediction of quality‐related variables for batch processes using a sequential phase partition method

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