Statistical Process Control Techniques Research Articles

Max-mixed EWMA control chart for joint monitoring of mean and variance: an application to yogurt packing process

The Max-Mixed EWMA Exponentially Weighted Moving Average (MM EWMA) control chart is a statistical process control technique used for joint monitoring of the mean and variance of a process. This control chart is designed to detect small and moderate shifts in the mean and variance of a process by comparing the maximum of two statistics, one based on the mean and the other on the variance. In this paper, we propose a new MM EWMA control chart. The proposed chart is compared with existing control charts using simulation studies, and the results show that the chart performs better in detecting small and moderate shifts in both the mean and variance. The proposed chart can be helpful in quality control applications, where joint monitoring of mean and variance is necessary to ensure a product’s or process’s quality. The real-life application of the proposed control chart on yogurt packing in a cup data set shows the outperformance of the MM EWMA control chart. Both simulations & real-life application results demonstrate the better performance of the proposed chart in detecting smaller shifts during the production process.

Portable spectroscopy, digital imaging colorimetry and multivariate statistical tools in contaminant identification: A case study of mint (Mentha) and basil (Ocimum basilicum)

The advent of portable Fourier-Transform Infrared (FTIR) and Raman spectrometers has revolutionized analysis capabilities, presenting the possibility of on-site contaminant identification without the need for specialized laboratory settings. Compared to laboratory instrumentation, portable spectroscopy is more prone to noise, and appropriate spectral processing procedures need to be established. This paper introduces a comprehensive methodology that integrates acquisition techniques, spectral analysis, and mathematical tools necessary for utilizing handheld spectrometers to diagnose plant contamination. It focuses on determining the efficacy of handheld FTIR, Raman spectroscopy, and digital imaging for detecting contaminants in two food plants, Basil (Ocimum basilicum) and Mint (Mentha). The study examines the impact of three pollutants: iron (II) sulphate (FeSO4), zinc (II) sulphate (ZnSO4), and copper (II) sulphate (CuSO4), on these plants, but also the necessary amount of measurements to spot the pollutants' effects. Measurements were conducted at the start, after 24 hours, and after 48 hours of exposure, on both fresh and dried plant leaves, as well as in solution. Spectral effects of each of the pollutants were identified with the use of multivariate statistical process control techniques. With the help of the developed methodologies, researchers can identify in-situ contaminant effects, exposure times and run diagnostics directly on the leaf both in alive and dried plants.

STATISTICAL TECHNIQUES IN PRECISION METROLOGY, APPLICATIONS AND BEST PRACTICES

Statistical techniques play a pivotal role in precision metrology, ensuring accurate measurements and reliable data analysis in various industries. This review delves into the applications and best practices of statistical techniques in precision metrology, highlighting their significance in enhancing measurement accuracy, reducing uncertainties, and optimizing manufacturing processes. In precision metrology, statistical methods are employed to analyze measurement data, assess measurement uncertainty, and make informed decisions regarding process control and quality assurance. One of the fundamental applications of statistical techniques is in experimental design, where methodologies such as Design of Experiments (DOE) aid in optimizing measurement processes by identifying influential factors and their interactions. Moreover, statistical process control (SPC) techniques like control charts are widely utilized in monitoring and maintaining measurement processes within specified limits, thereby ensuring consistency and reliability in metrology operations. These techniques enable early detection of deviations or anomalies, facilitating timely corrective actions to prevent the production of non-conforming products. Furthermore, the application of statistical analysis in uncertainty evaluation is crucial for quantifying the reliability of measurement results. Techniques such as Monte Carlo simulation and GUM (Guide to the Expression of Uncertainty in Measurement) provide systematic approaches for assessing and expressing measurement uncertainty, considering various sources of error and their contributions to the overall uncertainty budget. In addition to applications, adopting best practices in statistical techniques is essential for ensuring the effectiveness and efficiency of precision metrology processes. This includes proper data collection, adherence to statistical assumptions, selection of appropriate statistical models, and interpretation of results with caution. Overall, this review underscores the importance of statistical techniques in precision metrology, emphasizing their role in improving measurement accuracy, reducing variability, and enhancing overall quality in manufacturing processes. By implementing robust statistical methodologies and adhering to best practices, organizations can achieve higher levels of precision, consistency, and reliability in their metrology operations, ultimately leading to improved product quality and customer satisfaction.
 Keywords: Statistics, Precision Metrology, Best Practices, Application, Review.

STATISTICAL PROCESS CONTROL AS A TOOL FOR QUALITY IMPROVEMENT: A CASE STUDY IN DENIM PANT PRODUCTION

In this study, after defining the concepts of quality and quality control, the concept of statistical process control tools is examined. As a result of the data obtained from denim pants produced by a textile company operating in the ready-to-wear sector, defects were analyzed using statistical process control techniques. In this study, flowchart, control charts, pareto analysis, p-control diagram, cause-effect diagram, and grouping techniques were used. With the use of basic statistical process control tools, the most recurring defects were, and these defects were divided into subheadings for more detailed analysis. In this way, it was tried to prevent the repetition of defects by going down to the root causes of the detected defects. The causes of possible defects that may occur during production and solution suggestions were categorized. By going down to the basic causes of the detected defects and categorized them, defects were prevented by presenting and implementing solution suggestions. It is expected that the study with this different perspective will be an example of the sector and related fields as an alternative method. The aim of this study is to examine quality control practices in the garment industry, contribute to statistical control methods and problem-solving procedures, produce suggestions for problem-solving approaches, and disseminate improvement studies.

Implementation and Analysis of a Digital Twin for Propylene Glycol Production: Dynamic Simulation and Process Statistical Control

Purpose: Development of a dynamic simulation and analysis system for Propylene Glycol production through the hydrolysis of Propylene Oxide, utilizing market-available tools including a Digital Twin developed in the Unisim/Honeywell Simulation integrated to the PI System AVEVA/OSIsoft. This system aims to provide a realistic industrial environment for subsequent analysis and statical quality control. Theoretical framework: The manufacturing of Propylene Glycol involves complex reactive systems that require precise control and optimization. The integration of advanced simulation tools and statistical control charts, such as Shewhart Control Charts, is pivotal in monitoring and enhancing the quality of the process. This approach addresses the need for efficient process management in the chemical industry. Method/design/approach: The project employs a decoupled simulation structure, where the reactive system and the statistical control system operate simultaneously, exchanging information through the PI System supervisory software (AVEVA/OSIsoft) and a Python programming environment. The implementation of Shewhart Control Charts within the simulation provides real-time process quality monitoring. Results and conclusion: The outcomes demonstrate the feasibility of using a combination of software tools for daily industrial event simulation, as well as for training and acclimatizing engineers and operators. The analysis of statistical control charts allows for the detection of special cause variations, offering valuable insights for efficient process intervention, thereby enhancing the quality of both the process and the final product. Research implications: This study contributes to a deeper understanding of reactive systems in chemical engineering and the effective integration of simulation and statistical process control techniques. Originality/value: The development of a simulation framework that combines Digital Twin technology with statistical process control for the efficient and reliable production of Propylene Glycol.

Utilising routine clinical laboratory data to support quality improvement in health care: Application of a national acute kidney injury alert system as a proof of concept.

Acute kidney injury (AKI) is a global health issue known to cause avoidable harm and death. Improvement in its prevention and management is therefore considered an important goal for the health-care sector. The work here aimed to develop a tool which could be used to robustly and reliably measure, monitor, and compare the effectiveness of health-care interventions related to AKI across the Welsh NHS, a mechanism which did not exist previously. Using serum creatinine (SCr) as a biomarker for AKI and a validated national data-set collected from the all Wales Laboratory Information Management System, work involved applying Donabedian's framework to develop indicators with which to measure outcomes related to AKI, and exploring the potential of statistical process control (SPC) techniques for analysing data on these indicators. Rate of AKI incidence and 30-day AKI-associated mortality are proposed as valid, feasible indicators with which to measure the effectiveness of health-care interventions related to AKI. The control chart, funnel plot, and Pareto chart are proposed as appropriate, robust SPC techniques to analyse and visualise variation in AKI-related outcomes. This work demonstrates that routinely collected large SCr data offer a significant opportunity to monitor and therefore inform improvement in patient outcomes related to AKI. Moreover, while this work concerns utilisation of SCr data for improvement in AKI strategies, it is a proof of concept which could be replicated for other routinely collected clinical laboratory data, to improve the prevention and/or management of the conditions to which they relate.

TRENDING PERSPECTIVE IN EVALUATION OF INSPECTION CHARACTERISTICS OF PHARMACEUTICAL COMPOUND: COMPARATIVE STUDY OF CONTROL CHARTS

Background and objectives: The quality and efficacy of the pharmaceutical medicinal product are of paramount importance to the patients. Reproducibility and consistency of the properties of the dosage form start with the constituting ingredients either active or inactive. The present work provides an approach for the trending of one of the crucial inspection characteristics of a well-known excipient that is commonly used as a disintegrant of a water-insoluble synthetic cross-linked Polyvinyl Pyrrolidone (PVP). Establishing manufacturing criteria and quality limits should be assessed from the beginning level of the project using exploratory process-behavior plots. Materials and Methods: This study involved an assay of the initial 16 batches of the manufactured 1-ethenyl pyrrolidine-2-one expressed as a dried substance using a standard analysis method according to British Pharmacopoeia (BP). Exploratory Shewhart charts were plotted after preliminary distribution identification using Statistical Process Control (SPC) software. Based on the goodness of fit test results, the most appropriate assumed distribution was implemented in drawing the Individual/Moving Range (I/MR) control charts. Results: The goodness of fit study and probability plot showed that the most close-fitting to the data dispersion pattern was Johnson transformation to the normal spreading, smallest extreme value and Weibull distribution, respectively. While there were no alarms that could be detected from the process mean and the variability charts, further improvement is required from the supplier side to meet the benchmark minimum limit threshold, in addition, the process mean was not centered but shifted towards the Upper Control Limit (UCL). Conclusion: Implementation of SPC techniques is crucial in the modern competitive healthcare industry. Trending charts play a pivotal role in this perspective. They provide a quantitative estimation for the current situation and the actions needed for the future improvement of the monitored characteristic. Peer Review History: Received: 11 August 2023; Revised: 23 September; Accepted: 29 October, Available online: 15 November 2023 Academic Editor: Dr. Amany Mohamed Alboghdadly, Ibn Sina National College for Medical Studies in Jeddah, Saudi Arabia, amanyalboghdadly@gmail.com Received file: Reviewer's Comments: Average Peer review marks at initial stage: 6.5/10 Average Peer review marks at publication stage: 7.0/10 Reviewers: Dr. Taiwo O Elufioye, University of Ibadan, Nigeria, toonitaiwo@yahoo.com Dr. Bilge Ahsen KARA, Ankara Gazi Mustafa Kemal Hospital, Turkey, ahsndkyc@gmail.com

The partitioning ensemble control chart for on-line monitoring of high-dimensional image-based quality characteristics

The widespread implementation of computer technology has led to an increased use of Machine Vision Systems (MVS) for quality control in advanced manufacturing industries. The application of Statistical Process Control (SPC) techniques, especially control charts, is one of the most important and effective ways for fault detection in this field. Several statistical control charts have been extended to monitor images in the framework of MVS where the major aim is to improve the on-line detection ability, which is equivalent to avoid production of nonconforming products. In various industrial and non-industrial applications, control charts have been shown to be better able to detect anomalies in the underlying process(es) by integrating machine and ensemble learning techniques. However, in the field of image monitoring, approaches based on intelligent techniques have rarely been proposed. To bridge this gap and to improve the detection ability of control charts in monitoring image-based quality characteristics, this paper presents a novel control chart that combines machine learning, ensemble learning and image partitioning. In order to reach the maximum performance in Phase II SPC applications, specific designing and parameter tuning procedures are provided. The superiority of the proposed Partitioning Ensemble Control Chart (PECC) is verified by comparative analysis including conventional monitoring schemes by means of extensive simulations, in which different fault sizes and fault locations are applied to the images. Finally, a real MVS for quality control of O-rings is discussed to illustrate the practical application of the PECC.

Symmetry of gamma distribution data about the mean after processing with EWMA function

Statistical Process Control (SPC) plays a vital role in maintaining quality and reducing variability in manufacturing processes. Among SPC techniques, the Exponentially Weighted Moving Average (EWMA) stands out for its ability to detect small process shifts quickly, making it a valuable tool in ensuring product consistency and preventing quality issues. EWMA constructs control charts to monitor process mean shifts, tracks product/service quality by identifying variations, and monitors manufacturing process parameters for early detection of deviations and necessary adjustments. EWMA control chart has been proposed as an alternative to the Shewhart control chart. Sequential measurements are processed using the EWMA function before being placed on the control chart. One of the crucial concerns about the EWMA control chart is the asymmetry of the data around the mean. Although processing with the EWMA function reduces data skewness, the problem of asymmetric data may not be solved. The control chart is designed to leave in front of the upper control limit (UCL) α/2 of the data and behind the lower control limit (LCL) another α/2 of the data, and this does not occur in the case of symmetric data. α/2 represents the significance level for each tail in a two-tailed hypothesis test, indicating the probability of incorrectly rejecting the null hypothesis for each side of the distribution. Since many of the distributions in real life can be approximated by the Gamma distribution, the Gamma distribution was adopted in this study. The Monte Carlo simulation methodology was implemented to generate Gamma distributed data, process it with EWMA function and assess the skewness and kurtosis. The purpose of this paper is to evaluate the effect of EWMA parameters on the performance of the EWMA control chart. Moreover, it focuses on skewness and kurtosis reduction after data processing using the EWMA function. The findings help researchers and practitioners to select the best parameters. Further, the research investigates the effect of EWMA parameter on the shape of distribution.

An Outlier Detection Study of Ozone in Kolkata India by the Classical Statistics, Statistical Process Control and Functional Data Analysis

Air pollution is prevalent throughout the entire world due to the release of various gases such as NOx, PM, SO2, tropospheric ozone (O3), etc. Ground-stage ozone is the predominant issue in smog and is the product of the interplay between sunlight and emissions. The destructive impact on the health of the populace might also still occur in cities with noticeably clean air and where ozone levels hardly ever exceed safe limits. Therefore, the findings of small variations in air quality and the technique of regulating air contamination are thought-provoking. The study employs various techniques to effectively observe and assess strategies for detecting and eliminating outliers in ozone emissions from pollution episodes. This technique helps to describe the sources and exceedance values and enhance the value of monitoring the data. In this study, the data have some missing observations. The method of imputation, the classical statistical technique, the statistical process control (SPC) technique, functional data analysis (FDA), and functional process control help to fill in the data and detect outliers, trend deviations, and changes in ozone concentration at ground level. A comparison study is carried out using these three techniques: classical analysis, SPC, and FDA, and the results show how the statistical process control and functional data methods performed better than the classical technique for the detection of outliers and also in what way this methodology can enable an additional, comprehensive method of defining air pollution control measures and water pollution control measures.

APPLICATION OF STATISTICAL PROCESS CONTROL IN MECHANIZED CORN SEEDING

Several factors alter the quality of the mechanized sowing process of corn, and the use of the statistical process control (SPC) technique not only allows the identification of process limitation points but also ensures the correct functioning and conduction of the mechanized process, determining critical factors. that may decrease their performance. In view of the above, the present study aims to qualitatively evaluate, through this technique, the longitudinal distribution of corn seeds as a function of different stands and operational speeds during the sowing process. The experiment was carried out in a randomized block design (CBD) with three replications and in a split-plot design. Data were submitted to analysis of variance and when significant, analyzed using Tukey's test, with 5% significance. To carry out the statistical process control, mean and amplitude control charts were used to evaluate the effect of operational speed, seed distribution, deposition depth and plant germination. The results indicate that the use of statistical process control is an efficient tool to identify the behavior of the corn sowing process. Based on the results, speeds close to 4.5 km/h allowed an efficient sowing process, as it presented a higher percentage of normal spaces in the distribution of maize seeds.

New Anomaly Detection Method based on the Multivariate Generalized Pareto Distributions

The extreme events of floods, rainfalls, waves, and wind often cause serious damage. The observed data can be used to detect the sign of abnormal phenomena and can prevent the crucial damage. Anomaly detection techniques, the techniques of distinguishing between normal and abnormal cases from the data, have been one of the most used methods for detecting suspicious events from the collected data. Although many methods of the anomaly detection have appeared in the literature, almost all of them are based on multivariate statistical process control techniques and machine learning techniques. Recently, under the idea that information for suspicious and critical events is more likely to be involved in the largest or smallest values than values around the mean or median, the anomaly detection methods are based on the “Extreme Value Theory” (EVT), which is the statistical theory dealing with the largest or smallest data, attract the variety of areas. However, they can be applied to only univariate data since they have been based on the EVT for univariate data, and hence there has been little work for the multivariate anomaly detection based on the EVT. The multivariate anomaly detection method is important in applications because almost all data for anomaly detection are multivariate and can be misleading by applying univariate methods to multivariate data independently. In this paper, we propose the new anomaly detection method based on the multivariate EVT. The performance of the proposed methods is evaluated by the Monte Carlo simulation. To illustrate all the proposed methods of anomaly detection for multivariate data, we apply them to analyze the new real data of precipitation events in 2021. The numerical results show that the proposed method outperforms the existing methods.

Control Chart T2Qv for Statistical Control of Multivariate Processes with Qualitative Variables

The scientific literature is abundant regarding control charts in multivariate environments for numerical and mixed data; however, there are few publications for qualitative data. Qualitative variables provide valuable information on processes in various industrial, productive, technological, and health contexts. Social processes are no exception. There are multiple nominal and ordinal categorical variables used in economics, psychology, law, sociology, and education, whose analysis adds value to decision-making; therefore, their representation in control charts would be useful. When there are many variables, there is a risk of redundant or excessive information, so the application of multivariate methods for dimension reduction to retain a few latent variables, i.e., a recombination of the original and synthesizing of most of the information, is viable. In this context, the T2Qv control chart is presented as a multivariate statistical process control technique that performs an analysis of qualitative data through Multiple Correspondence Analysis (MCA), and the Hotelling T2 chart. The interpretation of out-of-control points is carried out by comparing MCA charts and analyzing the χ2 distance between the categories of the concatenated table and those that represent out-of-control points. Sensitivity analysis determined that the T2Qv control chart performs well when working with high dimensions. To test the methodology, an analysis was performed with simulated data and with a real case applied to the graduate follow-up process in the context of higher education. To facilitate the dissemination and application of the proposal, a reproducible computational package was developed in R, called T2Qv, and is available on the Comprehensive R Archive Network (CRAN).

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.

Robust Surveillance Schemes Based on Proportional Hazard Model for Monitoring Reliability Data

Product reliability is a crucial component of the industrial production process. Several statistical process control techniques have been successfully employed in industrial manufacturing processes to observe changes in reliability-related quality variables. These methods, however, are only applicable to single-stage processes. In reality, manufacturing processes consist of several stages, and the quality variable of the previous stages influences the quality of the present stage. This interdependence between the stages of a multistage process is an important characteristic that must be taken into account in process monitoring. In addition, sometimes datasets contain outliers and consequently, the analysis produces biased results. This study discusses the issue of monitoring reliability data with outliers. To this end, a proportional hazard model has been assumed to model the relationship between the significant quality variables of a two-stage dependent manufacturing process. Robust regression technique known as the M-estimation has been implemented to lessen the effect of outliers present in the dataset corresponding to reliability-related quality characteristics in the second stage of the process assuming Nadarajah and Haghighi distribution. The three monitoring approaches, namely, one lower-sided cumulative sum and two one-sided exponentially weighted moving average control charts have been designed to effectively monitor the two-stage dependent process. Using Monte Carlo simulations, the efficiency of the suggested monitoring schemes has been examined. Finally, two real-world examples of the proposed control approaches are provided in the study.

Integrating PCA and structural model decomposition to improve fault monitoring and diagnosis with varying operation points

Fast and efficient fault monitoring and diagnostics methods are essential for fault diagnosis and prognosis tasks in Health Monitoring Systems. These tasks are even more complicated when facing dynamic systems with multiple operation points. This article introduces a symbiotic solution for fault detection and isolation, based on the integration of two complementary techniques: Possible Conflicts (PCs), a model-based diagnosis technique from the Artificial Intelligence (AI) community, and Principal Component Analysis (PCA), a Multivariate Statistical Process Control (MSPC) technique. Our proposal improves the PCA-based fault detection in systems with multiple operation points and transient states and provides a straightforward fault isolation stage for PCA. At the same time, the proposal increases the robustness for fault detection using PCs through the application of PCA to the residual signals. PCA has the ability to filter out residual deviations caused by model uncertainties that can lead to a high number of false positives. The proposed method has been successfully tested in a real-world plant with accurate fault detection results. The plant has noisy sensors and a system model without the same accuracy at each operation point and transient states.

Calibrated closed-loop control to reduce the effect of geometry on mechanical behaviour in directed energy deposition

Directed energy deposition (DED) is an emerging technology with significant industrial potential in the repair of critical aerospace components, however its adoption has been limited by concerns about geometry-driven microstructural and mechanical property variation. These could be resolved by controlling the local temperature field, which would result in a consistent and predictable cooling profile. Closed-loop control approaches have been investigated previously, but with limited assessment of mechanical properties and only on small builds. In this work, we confirm that using fixed build parameters results in a statistically significant, geometry-driven variation in the bulk mechanical properties of DED-built 316 L steel. To address this issue, we have developed an industrially-suitable control algorithm using a low-cost coaxial camera, applying statistical process control techniques to identify representative melt pool images from the livestream. This has been tested on long builds, maintaining a control adjustment frequency of 1 Hz on build durations of >1 hour. Performance has been quantified through bulk mechanical testing, which confirmed that the control algorithm successfully eliminated the component-scale trends in melt pool size, and achieved a geometry-agnostic process with improved mechanical homogeneity. • Component geometry affects bulk mechanical properties in directed energy deposition • Geometry effects can be eliminated by closed-loop control of laser power • Statistical filtering was successful at selecting representative images for control • Control adjustments were maintained at 1 Hz over >1 hour build durations • Control yielded more predictable bulk mechanical properties with lower variation

The Application of Statistical Process Control Techniques for Quality Improvement of the Municipal Wastewater-Treated Process

The Application of Statistical Process Control Techniques for Quality Improvement of the Municipal Wastewater-Treated Process

Autogenerated Multilocal PLS Models without Pre-classification for Quality Monitoring of Nonlinear Processes with Unevenly Distributed Data

To achieve the desired product qualities for an operating process, the operational status must be monitored. Operating conditions are changed because of the variability in the quality of raw materials and equipment characteristics. The operating conditions are also changed to produce various product grades and to meet time-to-market demands. This makes the process data collected within a wide region to be distributed unevenly, so conventional multivariate statistical process control techniques that use a single global model are not suited to monitoring. Data is partitioned to construct each local model, but partitioning data without considering variable correlations can cause the information to be lost. This study uses a multilocal partial least-squares (ML-PLS) model to monitor a wide operation process. Without pre-selecting the data for each local model, ML-PLS automatically reinforces the data that pertains to each local model and weakens the data that does not pertain to other local models. The entire range of operation is clustered, and multiple PLS models are constructed using this clustered data. Statistical indices for quality-based process monitoring in the latent and observed variable spaces are derived using the learned ML-PLS models. The effectiveness and accuracy of the proposed method are demonstrated using a numerical example and a practical application for a chemical process.

Near real-time detection of blockages in the proximity of combined sewer overflows using evolutionary ANNs and statistical process control

Abstract Blockages are a major issue for wastewater utilities around the world, causing loss of service, environmental pollution, and significant clean-up costs. Increasing telemetry in combined sewer overflows (CSOs) provides the opportunity for near real-time data-driven modelling of wastewater networks. This paper presents a novel methodology, designed to detect blockages and other unusual events in the proximity of CSO chambers in near real-time. The methodology utilises an evolutionary artificial neural network (EANN) model for short-term CSO level predictions and statistical process control (SPC) techniques to analyse unusual level behaviour. The methodology was evaluated on historic blockage events from several CSOs in the UK and was demonstrated to detect blockage events quickly and reliably, with a low number of false alarms.