Statistical Process Monitoring Methods Research Articles

Feature-based statistical process monitoring for pressure swing adsorption processes

Pressure swing adsorption (PSA) is a widely used technology to separate a gas product from impurities in a variety of fields. Due to the complexity of PSA operations, process and instrument faults can occur at different parts and/or steps of the process. Thus, effective process monitoring is critical for ensuring efficient and safe operations of PSA systems. However, multi-bed PSA processes present several major challenges to process monitoring. First, a PSA process is operated in a periodic or cyclic fashion and never reaches a steady state; Second, the duration of different operation cycles is dynamically controlled in response to various disturbances, which results in a wide range of normal operation trajectories. Third, there is limited data for process monitoring, and bed pressure is usually the only measured variable for process monitoring. These key characteristics of the PSA operation make process monitoring, especially early fault detection, significantly more challenging than that for a continuous process operated at a steady state. To address these challenges, we propose a feature-based statistical process monitoring (SPM) framework for PSA processes, namely feature space monitoring (FSM). Through feature engineering and feature selection, we show that FSM can naturally handle the key challenges in PSA process monitoring and achieve early detection of subtle faults from a wide range of normal operating conditions. The performance of FSM is compared to the conventional SPM methods using both simulated and real faults from an industrial PSA process. The results demonstrate FSM’s superior performance in fault detection and fault diagnosis compared to the traditional SPM methods. In particular, the robust monitoring performance from FSM is achieved without any data preprocessing, trajectory alignment or synchronization required by the conventional SPM methods.

Nonparametric monitoring schemes in Phase II for ordinal profiles with application to customer satisfaction monitoring

The quality characteristic of some production and service processes is represented by an ordinal profile. The ordinal profile describes the functional relationship between the categorical response with three or more ordered attributes and some explanatory variables. Statistical process monitoring (SPM) for ordinal profiles has been receiving increasing attention since it is of vital importance to monitor the product and service quality timely. However, exiting SPM methods are often inadequate due to the fact that they are sensitive to the parametric model/distribution assumptions which are often invalid in practice. Therefore, two robust and effective monitoring schemes based on the nonparametric regression are proposed in this paper, and they are the generalized likelihood ratio scheme and the exponential weighted moving average scheme. We analyze and compare the performance of the proposed monitoring schemes for detecting changes in the functional relationship by thorough numerical simulations and a real example. Extensive results show that the two monitoring schemes are efficient in monitoring the ordinal profiles and robust to the latent regression models. Moreover, the proposed monitoring schemes perform relatively better than an existing novel method in general.

Statistical process monitoring in a specified period for the image data of fused deposition modeling parts with consistent layers

Statistical process monitoring (SPM) methods have been adopted and studied to detect variations in the fused deposition modeling (FDM) process in recent years. The FDM process that builds parts layer-by-layer is accomplished in a specified manufacturing period (number of layers) without interruption or suspension. Thus, traditional SPM methods, where the average run length is used for the calculation of the control limits and the measurement of the performance, are no longer applicable to the FDM process. In this paper, an SPM method is proposed based on the surface image data of FDM parts with consistent layers and a specified period. The probability of alarm in a specified period (PASP) and the cumulative PASP are introduced to determine the control limits and evaluate the monitoring performance. Regions of interest are determined in a fixed way to cover the sizes and locations of different defects. The statistics are calculated based on the generalized likelihood ratio. The control limit is determined based on the specified period and the nominal in-control PASP. A simulation study for different locations, sizes and magnitudes of the mean shift of defects is presented. In the case study, the proposed SPM method is applied to monitor the FDM process of a cuboid, which verifies the effectiveness of the proposed method.

Feature Based Fault Detection for Pressure Swing Adsorption Processes

Over the past few decades, there has been widespread development of pressure swing adsorption (PSA) systems, with their applications expanding from traditional bulk gas separation and drying, to CO2 sequestration, trace contaminant removal, and many others. With extensive industrial applications, there is a significant need for effective monitoring methods to detect and diagnose process abnormalities in realtime, as well as to facilitate predictive maintenance for avoiding major production disruptions ahead. Although periodic operations such as PSA have been used widely in chemical and petrochemical industries, the process monitoring of these operations has received limited attention compared to non-periodic continuous or batch processes. A potential reason is that the monitoring of periodic processes is significantly more challenging than that of processes operated at steady-state. In this work, we propose a data-driven feature space monitoring (FSM) approach for PSA processes. We show that the FSM based fault detection naturally addresses the challenges in monitoring periodic processes, such as unequal step and/or cycle time that requires trajectory alignment or synchronization for the traditional statistical process monitoring (SPM) methods. In addition, we demonstrate the superior fault detection performance of the proposed method compared to the conventional SPM methods using both simulated faults and real faults from an industrial PSA process.

Feature space monitoring for smart manufacturing via statistics pattern analysis

Statistical process monitoring (SPM) is an important component in the long-term reliable operation of any system and its importance can only become greater in the era of smart manufacturing (SM). Previously we proposed statistics pattern analysis (SPA) based on the idea of using various statistics to quantify process characteristics, and monitoring these statistics instead of process variables themselves to perform process monitoring. In this work we provide a comprehensive review on recent progresses made in SPA framework, which underpins a roadmap of SPM we outlined recently. Both sample-wise feature extraction and variable-wise feature extraction are discussed, with new applications in both fault detection and diagnosis, and soft sensor development. Specifically, we provide the first systematic examination on the SPA's capability in handling process characteristics including dynamics, nonlinearity and data non-Gaussianity; and compare its performance to representative state-of-the-art SPM methods to highlight the enhanced capability of feature-based monitoring. In addition, the performance of SPA is tested using the benchmark industrial simulator TEP for fault detection and diagnosis, plus a wet lab and an industrial case studies for soft sensor development. Finally, the advantages and potential limitations of SPA in addressing the new challenges presented by smart manufacturing big data are discussed.

Statistics pattern analysis: A new process monitoring framework and its application to semiconductor batch processes

AbstractIn the semiconductor industry, process monitoring has been recognized as a critical component of the manufacturing system. Multivariate statistical process monitoring (SPM) techniques, such as multiway principal component analysis and multiway partial least squares, have been extend to monitor semiconductor processes. These SPM methods require extensive, often off‐line data preprocessing such as data unfolding, trajectory mean shift, and trajectory alignment. This requirement is probably not an issue for the traditional chemical batch processes but it poses a significant challenge for semiconductor batch processes. This is because data preprocessing makes model building and maintenance extremely labor intensive due to the large number of models in a typical semiconductor fab. In addition, semiconductor process data often show more severe nonnormality compared to those of the traditional chemical process under closed‐loop control, which results in suboptimal performance in many applications. To address these challenges, several pattern classification based monitoring (PCM) methods have been developed recently, but some limitations remain and trajectory alignment is still required. In this article, we analyze the fundamental reasons for the limitations of the SPM and PCM methods when applied to monitor semiconductor processes. In addition, we propose a new statistics pattern analysis (SPA) framework to address the challenges associated with semiconductor processes. By monitoring batch statistics, the proposed SPA framework not only eliminates all data preprocessing steps but also provides superior fault detection performance. Finally, we use an industrial example to demonstrate the advantages of the proposed SPA framework, and examine the fundamental reasons for the improved performance from SPA. © 2010 American Institute of Chemical Engineers AIChE J, 2011

Statistical monitoring of multivariable dynamic processes with state‐space models

AbstractIndustrial continuous processes may have a large number of process variables and are usually operated for extended periods at fixed operating points under closed‐loop control, yielding process measurements that are autocorrelated, cross‐correlated, and collinear. A statistical process monitoring (SPM) method based on multivariate statistics and system theory is introduced to monitor the variability of such processes. The statistical model that describes the in‐control variability is based on a canonical‐variate (CV) state‐space model that is an equivalent representation of a vector autoregressive moving‐average time‐series model. The CV state variables obtained from the state‐space model are linear combinations of the past process measurements that explain the variability of the future measurements the most. Because of this distinctive feature, the CV state variables are regarded as the principal dynamic directions A T2 statistic based on the CV state variables is used for developing an SPM procedure. Simple examples based on simulated data and an experimental application based on a high‐temperature short‐time milk pasteurization process illustrate advantages of the proposed SPM method.