Wavelet-based multiscale statistical process monitoring: A literature review

Ganesan, Rajesh; Das, Tapas K.; Venkataraman, Vivekanand

doi:10.1080/07408170490473060

“…Performance monitoring charts with non-parametric control limits were then applied to identify the occurrence of non-conforming operation. Ganesan et al 177 presented a literature review of wavelet-based, multiscale statistical process monitoring. In their paper, over 150 published and unpublished papers are cited for this important subject, and some extensions of the current research are discussed.…”

Section: Neural Network and Non-linear Modelsmentioning

confidence: 99%

Multivariate statistical process control charts: an overview

Bersimis

¹

,

Psarakis

²

,

Panaretos

³

2006

Quality & Reliability Eng

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In this paper we discuss the basic procedures for the implementation of multivariate statistical process control via control charting. Furthermore, we review multivariate extensions for all kinds of univariate control charts, such as multivariate Shewharttype control charts, multivariate CUSUM control charts and multivariate EWMA control charts. In addition, we review unique procedures for the construction of multivariate control charts, based on multivariate statistical techniques such as principal components analysis (PCA) and partial least squares (PLS). Finally, we describe the most significant methods for the interpretation of an out-of-control signal.

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“…Multiscale representation has been shown to effectively deal with real process data as it allows efficient separation of important features from stochastic noise and provides wavelet coefficients that are approximately decorrelated and more Gaussian at multiple scales. Thus, it can help address most of the assumptions of the conventional univariate fault detection or control charts [24,25]. These advantages of multiscale representation will be utilized in this work to develop a multiscale Shewhart chart algorithm that will provide improved performance.…”

Section: Introductionmentioning

confidence: 99%

Improved Fault Detection and Process Safety Using Multiscale Shewhart Charts.

Sheriff¹,

Nounou²

2017

J Chem Eng Process Technol

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Process safety is a critical component in various process industries. Statistical process monitoring techniques were initially developed to maximize efficiency and productivity, but over the past few decades with catastrophic industrial disasters, process safety has become a top priority. Sensors play a crucial role in recording process measurements, and according to the number of monitored variables, process monitoring techniques can be classified into univariate or multivariate techniques. Most univariate process monitoring techniques rely on three fundamental assumptions: that process residuals contain a moderate level of noise, are independent, and are normally distributed. Practically, however, due to a variety of reasons such as modeling errors and malfunctioning sensors, these assumptions are violated, which can lead to catastrophic incidents. Fortunately, multiscale wavelet-based representation of data inherently possesses characteristics that are able to deal with these violations of assumptions. Therefore, in this work, multiscale representation is utilized to enhance the performance of the Shewhart chart (which is a well-known univariate fault detection method) to help improve its performance. The performance of the developed multiscale Shewhart chart was assessed and compared to the conventional chart through two examples, one using synthetic data, and the other using simulated distillation column data. The results of both examples clearly show that the developed multiscale Shewhart chart provides lower missed detection and false alarm rates, as well as lower ARL 1 values (i.e., quicker detection) for most cases where the fundamental assumptions of the Shewhart chart are violated. Additionally, the relative simplicity of the proposed algorithm encourages its implementation in practice to help improve process safety.

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“…Furthermore, it should be noted that the multiscale representation provides wavelet coefficients that are also closer to being stationary for non-stationary data [24,35]. In this section, it has been shown that multiscale representation possesses advantages that can help with these challenges and its advantages are described in the next section.…”

Section: Multiscale Wavelet-based Representation Of Datamentioning

confidence: 99%

Systematic Preparation of Artificial Cells (DNA Crown Cells)

Inooka¹

2017

J Chem Eng Process Technol

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Process safety is a critical component in various process industries. Statistical process monitoring techniques were initially developed to maximize efficiency and productivity, but over the past few decades with catastrophic industrial disasters, process safety has become a top priority. Sensors play a crucial role in recording process measurements, and according to the number of monitored variables, process monitoring techniques can be classified into univariate or multivariate techniques. Most univariate process monitoring techniques rely on three fundamental assumptions: that process residuals contain a moderate level of noise, are independent, and are normally distributed. Practically, however, due to a variety of reasons such as modeling errors and malfunctioning sensors, these assumptions are violated, which can lead to catastrophic incidents. Fortunately, multiscale wavelet-based representation of data inherently possesses characteristics that are able to deal with these violations of assumptions. Therefore, in this work, multiscale representation is utilized to enhance the performance of the Shewhart chart (which is a well-known univariate fault detection method) to help improve its performance. The performance of the developed multiscale Shewhart chart was assessed and compared to the conventional chart through two examples, one using synthetic data, and the other using simulated distillation column data. The results of both examples clearly show that the developed multiscale Shewhart chart provides lower missed detection and false alarm rates, as well as lower ARL 1 values (i.e., quicker detection) for most cases where the fundamental assumptions of the Shewhart chart are violated. Additionally, the relative simplicity of the proposed algorithm encourages its implementation in practice to help improve process safety.

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Wavelet-based multiscale statistical process monitoring: A literature review

Cited by 119 publications

References 127 publications

Multivariate statistical process control charts: an overview

Multivariate statistical process control charts: an overview

Improved Fault Detection and Process Safety Using Multiscale Shewhart Charts.

Systematic Preparation of Artificial Cells (DNA Crown Cells)

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