Statistical Process Control Framework Research Articles

Incorporating plan complexity into the statistical process control of volumetric modulated arc therapy pre-treatment verifications.

Statistical process control (SPC) is a powerful statistical tool for process monitoring that has been highly recommended in healthcare applications, including radiation therapy quality assurance (QA). The AAPM TG-218 report described the clinical implementation of SPC for Volumetric Modulated Arc Therapy (VMAT) pre-treatment verifications, pointing out the need to adjust tolerance limits based on plan complexity. However, the quantification of plan complexity and its integration into SPC remains an unresolvedchallenge. The primary aim of this study is to investigate the incorporation of plan complexity into the SPC framework for VMAT pre-treatment verifications. The study explores and evaluates various strategies for this incorporation, discussing their merits and limitations, and provides recommendations for clinicalapplication. A retrospective analysis was conducted on 309 VMAT plans from diverse anatomical sites using the PTW OCTAVIUS 4D device for QA measurements. Gamma Passing Rates (GPR) were obtained, and lower control limits were computed using both the conventional Shewhart method and three heuristic methods (scaled weighted variance, weighted standard deviations, and skewness correction) to accommodate non-normal data distributions. The 'Identify-Eliminate-Recalculate' method was employed for robust analysis. Eight complexity metrics were analyzed and two distinct strategies for incorporating plan complexity into SPC were assessed. The first strategy focused on establishing control limits for different treatment sites, while the second was based on the determination of control limits as a function of individual plan complexity. The study extensively examines the correlation between control limits and plan complexity and assesses the impact of complexity metrics on the controlprocess. The control limits established using SPC were strongly influenced by the complexity of treatment plans. In the first strategy, a clear correlation was found between control limits and average plan complexity for each site. The second approach derived control limits based on individual plan complexity metrics, enabling tailored tolerance limits. In both strategies, tolerance limits inversely correlated with plan complexity, resulting in all highly complex plans being classified as in control. In contrast, when plans were collectively analyzed without considering complexity, all the out-of-control plans were highlycomplex. Incorporating plan complexity into SPC for VMAT verifications requires meticulous and comprehensive analysis. To ensure overall process control, we advocate for stringent control and minimization of plan complexity during treatment planning, especially when control limits are adjusted based on plancomplexity.

Automated contouring and statistical process control for plan quality in a breast clinical trial

Background and purposeAutomatic review of breast plan quality for clinical trials is time-consuming and has some unique challenges due to the lack of target contours for some planning techniques. We propose using an auto-contouring model and statistical process control to independently assess planning consistency in retrospective data from a breast radiotherapy clinical trial. Materials and methodsA deep learning auto-contouring model was created and tested quantitatively and qualitatively on 104 post-lumpectomy patients’ computed tomography images (nnUNet; train/test: 80/20). The auto-contouring model was then applied to 127 patients enrolled in a clinical trial. Statistical process control was used to assess the consistency of the mean dose to auto-contours between plans and treatment modalities by setting control limits within three standard deviations of the data’s mean. Two physicians reviewed plans outside the limits for possible planning inconsistencies. ResultsMean Dice similarity coefficients comparing manual and auto-contours was above 0.7 for breast clinical target volume, supraclavicular and internal mammary nodes. Two radiation oncologists scored 95% of contours as clinically acceptable. The mean dose in the clinical trial plans was more variable for lymph node auto-contours than for breast, with a narrower distribution for volumetric modulated arc therapy than for 3D conformal treatment, requiring distinct control limits. Five plans (5%) were flagged and reviewed by physicians: one required editing, two had clinically acceptable variations in planning, and two had poor auto-contouring. ConclusionsAn automated contouring model in a statistical process control framework was appropriate for assessing planning consistency in a breast radiotherapy clinical trial.

A new multivariate control chart based on the isolation forest algorithm

Statistical process control (SPC) is an extensively utilized technique in quality management for service and manufacturing industries. Traditional SPC control charts rely on normality assumptions and analyze data in one dimension. Machine learning methods have been increasingly used to address limitations of traditional SPC techniques, such as one-class classification (OCC) procedures, using support vector machine (SVM), K-nearest neighbor (KNN), and other techniques. Nonetheless, there are drawbacks associated with these methods. For instance, SVM-based approaches may require significant computational resources, whereas KNN-based approaches may encounter difficulties in identifying minor variations. Here, these limitations are addressed using a new SPC framework that incorporates the isolation forest (iForest) algorithm, an unsupervised anomaly detection technique developed for machine learning applications. Our presented method examines how the number of trees impacts the charting scheme and offers several advantages: (1) it has a low constant time complexity that results in fast computing speeds; (2) it outperforms competitors in detecting small shifts in various distributions; and (3) while dealing with challenging distributions with heavy tails, it proves to be effective. We provide two examples that demonstrate the performance of our proposed control chart using Monte Carlo simulations.

Process Capability Studies in an Automated Flexible Assembly Process: A Case Study in an Automotive Industry

Statistical Process Control (SPC) methods can significantly increase organizational efficiency if appropriately used. The primary goal of process capability studies is to obtain critical information about processes to render them even more effective. This paper proposes a comprehensive framework for proper implementation of SPC studies, including the design of the sampling procedure and intervals as well as process capability indices. Some of the most essential process capability indices in the literature were reviewed to develop a methodology to utilize process capability indices within the SPC framework. The current study presents an efficiency-oriented criterion designed for measuring SPC implementation productivity. The framework is applied to the windshield installation process of an Iranian automobile assembly line. The process was sampled in various sessions. Results verify that the implemented SPC framework could successfully improve the process and that the proposed framework could significantly address bottleneck in the process and enhance the quality level of the process from satisfactory to excellent according to the reference values of process capability indices. Managerial insights are also drawn from results.

Adaptive radiotherapy based on statistical process control for oropharyngeal cancer.

PurposeThe purpose of this study is to quantify dosimetric changes throughout the delivery of oropharyngeal cancer treatment and to investigate the application of statistical process control (SPC) for the management of significant deviations during the course of radiotherapy.MethodsThirteen oropharyngeal cancer patients with daily cone beam computed tomography (CBCT) were retrospectively reviewed. Cone beam computed tomography images of every other fraction were imported to the Velocity software and registered to planning CT using the 6 DOF (degrees of freedom) couch shifts generated during patient setup. Using Velocity “Adaptive Monitoring” module, the setup‐corrected CBCT was matched to planning CT using a deformable registration. Volumes and dose metrics at each fraction were calculated and rated with plan values to evaluate interfractional dosimetric variations using a SPC framework. T‐tests between plan and fraction volumes were performed to find statistically insignificant fractions. Average upper and lower process capacity limits (UCL, LCL) of each dose metric were derived from these fractions using conventional SPC guidelines.ResultsGross tumor volume (GTV) and organ at risk (OAR) volumes in the first 13 fractions had no significant changes from the pretreatment planning CT. The GTV and the parotid glands subsequently decreased by 10% at the completion of treatment. There were 3–4% increases in parotid mean doses, but no significant differences in dose metrics of GTV and other OARs. The changes were organ and patient dependent. Control charts for various dose metrics were generated to assess the metrics at each fraction for individual patient.ConclusionsDaily CBCT could be used to monitor dosimetric variations of targets and OARs resulting from volume changes and tissue deformation in oropharyngeal cancer radiotherapy. Treatment review with the guidance of a SPC tool allows for an objective and consistent clinical decision to apply adaptive radiotherapy.

Implementation of statistical process control framework with machine learning on waveform profiles with no gold standard reference

Condensation water temperature profiles are collected from a curing process for high-pressure hose products. The shape of those profiles resembles sine waves with diminishing amplitudes. A gold standard wave profile does not exist. Instead some wave profiles with various frequency and amplitudes are deemed normal for the water release operation. To the best of our knowledge, the current practice and research on SPC do not provide a solution for monitoring wave profiles of this kind. We leveraged existing methods, tools, algorithms that can be found in open source or commercial software for quick response to this type of problem. The proposed SPC implementation framework first converts waveform profiles from the time domain to the frequency domain. Then a set of phase I IX control charts is constructed based on a Partition Around Medoids (PAM) clustering method. A Support Vector Machine (SVM) classifier is then used to label a new profile to its associated group for phase II monitoring so that the IX chart associated with a homogeneous group can provide better process monitoring. Overall 146 water temperature profiles were collected in phase I process, while 39 profiles were captured in phase II process. Out of those 39 profiles, 6 of which were recognized as abnormal waveform profiles by quality engineers and our judgements. The proposed framework with machine learning and SPC implementation in the frequency domain works well during phase I control charting with low false alarm rates. The proposed framework also outperforms the other profile analysis methods in phase II control charting in term of high detection rate of abnormal profiles.

A synergic multivariate statistical process control framework for monitoring, diagnosis, and adjustment of multiple response abrasive machining processes

A synergic multivariate statistical process control framework for monitoring, diagnosis, and adjustment of multiple response abrasive machining processes

A synergic multivariate statistical process control framework for monitoring, diagnosis, and adjustment of multiple response abrasive machining processes

In various abrasive machining processes, output quality is defined in terms of multiple critical responses and their deviations from target values. These multiple responses are often interacting and changing the process conditions for improving or controlling one response may deteriorate the quality of another. Thus, there is a need to simultaneously consider all responses and recommend a trade-off operating condition for process control and optimisation. Two important fields that consider simultaneous control and optimisation of multiple responses are the multivariate statistical process control (MSPC) and multiple response optimisation (MRO). Although various MSPC and MRO approaches have been proposed by researchers, there is limited prior research on the integration of MSPC and MRO approaches to ensure the stability and process capability. In this study, a synergic MSPC and MRO approach is proposed based on Mahalanobis-Taguchi system and nonlinear optimisation to ensure the stability and capability of the abrasive machining process.

TU-FG-201-09: Predicting Accelerator Dysfunction

Purpose: To develop an integrated statistical process control (SPC) framework using digital performance and component data accumulated within the accelerator system that can detect dysfunction prior to unscheduled downtime. Methods: Seven digital accelerators were monitored for twelve to 18 months. The accelerators were operated in a ‘run to failure mode’ with the individual institutions determining when service would be initiated. Institutions were required to submit detailed service reports. Trajectory and text log files resulting from a robust daily VMAT QA delivery were decoded and evaluated using Individual and Moving Range (I/MR) control charts. The SPC evaluation was presented in a customized dashboard interface that allows the user to review 525 monitored parameters (480 MLC parameters). Chart limits were calculated using a hybrid technique that includes the standard SPC 3σ limits and an empirical factor based on the parameter/system specification. The individual (I) grand mean values and control limit ranges of the I/MR charts of all accelerators were compared using statistical (ranked analysis of variance (ANOVA)) and graphical analyses to determine consistency of operating parameters. Results: When an alarm or warning was directly connected to field service, process control charts predicted dysfunction consistently on beam generation related parameters (BGP)– RF Driver Voltage, Gun Grid Voltage, and Forward Power (W); beam uniformity parameters – angle and position steering coil currents; and Gantry position accuracy parameter: cross correlation max-value. Control charts for individual MLC – cross correlation max-value/position detected 50% to 60% of MLCs serviced prior to dysfunction or failure. In general, non-random changes were detected 5 to 80 days prior to a service intervention. The ANOVA comparison of BGP determined that each accelerator parameter operated at a distinct value. Conclusion: The SPC framework shows promise. Long term monitoring coordinated with service will be required to definitively determine the effectiveness of the model. Varian Medical System, Inc. provided funding in support of the research presented.

Development and field application of a multivariate statistical process control framework for health-monitoring of transportation infrastructure

We present a two-part multivariate statistical process control framework to support health-monitoring of transportation infrastructure. The first part consists of estimation of regression and ARIMA–GARCH models to explain, predict, and control for common-cause variation in the data, i.e., changes that can be attributed to usual operating conditions, including traffic loads, environmental effects, and damage when present throughout the data. The second part of the framework consists of using multivariate control charts to simultaneously analyze the standardized innovations of the aforementioned models in order to detect possible special-cause or extraordinary events, such as unique/infrequent traffic, weather, or the onset of damage. The proposed approach revolves around construction of T2 control charts as a framework to jointly monitor the evolution and contemporaneous correlation of a set of measurements. The approach provides significant practical/computational advantages over individual analysis of multiple structural properties, and addresses technical problems stemming from ignoring the relationships among them.To illustrate the framework, we analyze strain and displacement data from the monitoring system on the Hurley Bridge (Wisconsin Structure B-26-7). Data were collected from 1 April 2010 to 28 June 2012. Analysis of 7 measurement sequences collected over the 27month planning horizon revealed 6 possible special-cause events. In terms of outlier interpretation, we use Mason–Young–Tracy Decomposition to establish the contribution of (subsets of) the measurements. Also, we link the most significant special-cause events, in terms of magnitude and duration, to unusual changes in weather and traffic. To conclude, we compare the proposed approach and benchmark the empirical results with Principal Component Analysis, perhaps, the most common alternative appearing in the literature.

Analysis of common-cause and special-cause variation in the deterioration of transportation infrastructure: A field application of statistical process control for structural health monitoring

We present a statistical process control framework to support structural health monitoring of transportation infrastructure. We contribute an integrated, generally-applicable (to various types of structural response data) statistical approach that links the literatures on statistical performance modeling and on structural health monitoring. The framework consists of two parts: The first, estimation of statistical models to explain, predict, and control for common-cause variation in the data, i.e., changes, including serial dependence, that can be attributed to usual operating conditions. The ensuing standardized innovation series are analyzed in the second part of the framework, which consists of using Shewhart and Memory Control Charts to detect special-cause or unusual events.We apply the framework to analyze strain and displacement data from the monitoring system on the Hurley Bridge (Wisconsin Structure B-26-7). Data were collected from April 1, 2010 to June 29, 2011. Our analysis reveals that, after controlling for seasonal effects, linear trends are significant components of the response measurements. Persistent displacement may be an indication of deterioration of the bridge supports. Trends in the strain data may indicate changes in the material properties, i.e., fatigue, sensor calibration, or traffic loading. The results also show that autocorrelation and conditional heteroscedasticity are significant sources of common-cause variation. Use of the control charts detected 43 possible special-cause events, with approximately 50% displaying persisting effects, and 25% lasting longer than one week. Analysis of traffic data shows that unusually heavy loading is a possible cause of the longest special-cause event, which lasted 11days.

Application of Statistical Process Control for Structural Health Monitoring of a Historic Building

AbstractThe authors apply a statistical process control framework to support structural health monitoring of the Grace Church building in Charleston, South Carolina. Specifically, they conduct a post-hoc analysis of displacement data acquired via remote monitoring of delamination between two wythes of brick in a clerestory wall. The framework consists of formulation and estimation of statistical models to explain the progression of the measurements under ordinary conditions and use of control charts to detect unusual events. One such event was excessive displacement in September 2011 that led the engineer of record to close the building to public access and order immediate repairs. The analysis also reveals a few unusual events that were not apparent from visual interpretation of the data, including a possible precursor to the aforementioned event.

Multivariate Process Monitoring and Diagnosis: A Case Study

In manufacturing industries, monitoring and diagnosis of multivariate process out-of-control condition become more challenging. Process monitoring refers to the identification of process status either it is running within a statistically in-control or out-of-control condition, whereas process diagnosis refers to the identification of the source variables of out-of-control process. In order to achieve these requirements, the application of an appropriate statistical process control framework is necessary for rapidly and accurately identifying the signs and source out-of-contol condition with minimum false alarm. In this research, a framework namely, an Integrated Multivariate Exponentially Weighted Moving Average with Artificial Neural Network was investigated in monitoring-diagnosis of multivariate process mean shifts in manufacturing audio video device component. Based on two-stages monitoring-diagnosis technique, the proposed framework has resulted in efficient performance.

Statistical process control for monitoring non-linear profiles using wavelet filtering and B-Spline approximation

A statistical process control framework is proposed to monitor non-linear profiles. The proposed methodology aims at identifying mean shifts or ‘shape changes’ in a profile. Discrete wavelet transformation (DWT) is applied to separate variation or noise from profile contours. B-splines are adopted to generate critical points to define the shape of a profile. The proposed method is innovative in that users can divide a profile into multiple segments to be monitored simultaneously. The high dimensionality problem that hinders the implementation of multivariate control charts can be solved by this framework. The distance difference statistic for each segment provides diagnostic information when the process of interest is out of control. These proposed statistics form a vector to be fed into any multivariate control chart such as the Hotelling T 2 control chart. A decomposition method can also be applied on the T 2 statistics when an out-of-control profile is detected. A simulation study applied to a forging process is conducted to demonstrate the property of the proposed method. The proposed method is capable of detecting profile shifts and identifying the exact location of problematic segments.

Wavelet texture analysis of on-line acquired images for paper formation assessment and monitoring

Paper formation (the distribution and intermixing of fibres in a paper sheet), plays a central role in paper products, and is usually evaluated off-line, with a significant delay relative to the high production rates achieved in modern paper machines. In this paper, we address an approach for evaluating and monitor paper formation using images acquired with an especially designed sensor, in-line, in-situ and in real time. The methodology essentially consists of applying wavelet texture analysis to raw images, in order to compute a wavelet signature for each image, based on which their discrimination, according to the formation quality level, can be made. A PCA analysis of such features confirms the different formation quality levels defined a priori after visual inspection, and, furthermore, suggests a new subclass for abnormal samples, related to the bulkiness of fibre flocks. A multivariate statistical process control framework, based on such PCA description (PCA-MSPC), is proposed to monitor formation quality, which provides quite good results when applied to the available images, as analyzed with the ROC curve for the method and confirmed with a Monte Carlo simulation study using subimages with 1/4 of the size of the original ones.

A statistical process control approach to business activity monitoring

Statistical Process Control (SPC) techniques have been successfully used in manufacturing industries to trigger and identify the root cause of variations so as to promote quality improvement. This paper develops a SPC framework to identify important changes deserved in business activity monitoring. To model and track thousands of diversified customer behaviors, the proposed SPC system consists of efficient and robust profiling methods to accommodate different behavior patterns including business changes, structural breakdowns, and unnecessary errors. Several customer profiling techniques are discussed and the activity monitoring performance based on the profiling algorithms is compared in a simulation example and a customer churn detection example in a telecommunications setting. The enhanced system will allow business managers and engineers to establish successful customer loyalty programs for churn prevention and fraud detection.

Kernel-based fault diagnosis on mineral processing plants

Safe operation, environmental issues, as well as economic considerations all form part of the wide range of driving forces for the development of better fault diagnostic systems on process plants. The continuous search for novel methods for fault detection and identification resulting from these incentives has recently drawn attention to support vector machines as a means towards improved fault diagnosis. These kernel-based methods are in theory capable of better generalization, particularly as far as large systems are concerned, since their performance is not dependent on the number of variables under consideration and recent studies underlined their promising role in diagnostic systems. However, integration of these methods into the classical multivariate statistical process control framework is complicated by difficulties in the identification of the original variables associated with detected faults. In this paper, a general strategy for process fault diagnosis is proposed. First, kernel methods are used to remove nonlinear structure from the data, if present, after which the residuals from the data are used to monitor the process. A novel element of the strategy is the use of one-class support vector machines to estimate nonparametric confidence limits for these residuals. Using these limits in conjunction with Gower and Hand biplots and standard statistics collectively constitute a powerful approach to monitoring process systems, as demonstrated by several case studies on mineral processing systems.

Fault reconstruction in linear dynamic systems using multivariate statistics

Treasure et al. (2004) recently proposed a new subspace-monitoring technique, based on the N4SID algorithm, within the multivariate statistical process control framework. This dynamic-monitoring method requires considerably fewer variables to be analysed when compared with dynamic principal component analysis (PCA). The contribution charts and variable reconstruction, traditionally employed for static PCA, are analysed in a dynamic context. The contribution charts and variable reconstruction may be affected by the ratio of the number of retained components to the total number of analysed variables. Particular problems arise if this ratio is large and a new reconstruction chart is introduced to overcome these. The utility of such a dynamic contribution chart and variable reconstruction is shown in a simulation and by application to industrial data from a distillation unit.

A Statistical Process Control Framework for the Characterization of Variation in Batch Profiles

An statistical process control framework for the characterization of the systematic variability in a historical database of batch profiles is proposed. The framework is geared toward facilitating an understanding of the sources of variability affecting the process. The overall variability in the profiles is categorized into two parts, systematic and unsystematic. The former is further divided, as along the time axis and the measurement axis. Scaling methods are applied to the profiles to obtain scale parameters that characterize the systematic time and measurement axis variability. The profile scaling is proposed so that each parameter has a very specific meaning in terms of the type of variability explained. Multivariate SPC charts on the scale parameters and also on the residuals remaining after scaling are developed for process monitoring. Profiles from two simulation examples, a simulated methyl methacralate polymerization reactor and a nylon-6,6 reactor, are used to demonstrate the application of the SPC framework. The examples demonstrate that a systematic study of the correlation structure of the scale parameters can reveal the signature of the primary disturbances affecting the process. Besides providing meaningful scale parameters, the framework also retains the power of projection methods for subtle special cause detection. The demonstration also highlights the importance of using the time variability information for final product quality predictions in batch data mining.

Analyzing and management of three levels of Behavioral Health Rehabilitation Services (Wrap Around) for children: Building order out of chaos.

Current Behavioral Health Rehabilitation Programs (BHRP's) for children are characterized by chaos and confusion. Poor dissemination of empirically supported technology is just the beginning of why such systems are both expensive and have failed to establish outcome data to support their effectiveness. The typical BHRP is driven to provide services to every child as opposed to serve every child's needs; leading to a system that often provides children with well educated and well paid babysitters as opposed to technologically armed clinicians that can solve problems and rehabilitate their clientele. In addition, current organizational contingencies select behaviors of low treatment integrity from workers because of lack of adequate focus, specification of critical performance behaviors, and reward for performance (Gilbert, 1978). With agencies often providing little management, training, and supervision and even less opportunity for growth in a field of expertise it is no surprise that the organizational imperative of most agencies is a warm body to fill every hour of approved service. Organizational behavior management (OBM) and integration of OMB within a systems framework have demonstrated effectiveness in the improvement of quality of social services (Reid, Parsons, & Green, 1989; Fleming & Reile, 1993). Recently, many have pointed to the similarities between organizational behavior management and more systems approaches to quality improvement (Hantula, 1995; Krapfl & Gasparotto, 1982; Mawhinney, 1986) The integration of a functional design within a statistical process control framework is just one way to provide such agencies with the needed organization to deliver services more effectively and more efficiently. Such services would undoubtedly have greater benefit to families. Characteristics of the Functionally Organized Agency A delineation of three important components or three defined levels of the organization characterize functionally organized service agencies. (1) The tool level is the technological level of the organization, key goals to research effectiveness of treatment, define and refine new assessment methods, update review and redefine best in Management at the tool level asks the question How will technological improvements aide the quality of treatment. (2) The individual level is the clinician level of the organization, organizational hiring practices, training practices, development of a personal investment and understanding of the mission, acceptance of the responsibility to provide effective Management at the individual level asks the question What are the effects of training and hiring practices and how can it be improved. (3) The systems level is the big picture including, goals that improve the delivery of services, policies that promote clinical expertise, policies strictly limiting the effects poor service delivery but rewarding innovation, dedication and clinical integrity. Management at the systems level asks the questions How will changing organizational contingencies foster higher quality. A functionally organized agency would not only be able to provide all of the services that a child needs, but would ultimately be more successful in the treatment of the child then an organization concerned solely with the simple supply of adequate staffing for children in Improving Quality at the Tool Level Data collection and interpretation methods must be deployed to give 1:1 staff direct feedback about the acquisition of goals and objectives and measures of effective Additionally, methods for clinical staff to deliver adequate client specific training based on accurate and up to date assessments and treatment plans. Pre-service training about specific clinical approaches as opposed to limited training about job responsibilities and organizational paperwork. Direct service should be trained that service is about change and improvement in quality of life and ultimately the rehabilitation of the client as opposed to the maintenance of the client in the community. …