Conveners
Statistical Process Monitoring: 1
- Panagiotis Tsiamyrtzis (Politecnico di Milano)
Statistical Process Monitoring: 2
- Athanasios Rakitzis (University of Piraeus, Department of Statistics and Insurance Science)
Statistical Process Monitoring: 3
- Panagiotis Tsiamyrtzis (Politecnico di Milano)
Statistical Process Monitoring: 4
- Sven Knoth (Helmut Schmidt University Hamburg, Germany)
Statistical Process Monitoring: 5
- Sven Knoth (Helmut Schmidt University Hamburg, Germany)
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Athanasios Rakitzis (University of Piraeus, Department of Statistics and Insurance Science)15/09/2025, 13:30Statistical Process Monitoring
In this work we consider one-sided EWMA and CUSUM charts with one Shewhart-type control limit, and study their performance in the detection of shifts, of different magnitude, in the parameters of a two-parameter exponential distribution. Using Monte Carlo simulation, we calculate the run length distribution of the considered charts and evaluate their performance, focusing on the average run...
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KONSTANTINOS FOUNTOUKIDIS (UNIVERSITY OF PIRAEUS)15/09/2025, 13:50Statistical Process Monitoring
A single Shewhart chart based on a Max-type statistic has been suggested for monitoring a process using one control charts, based on a single plotting statistic, and detecting changes in its parameters. To improve its power, it is suggested to apply one or more supplementary rules based on run statistics, known as runs rules. Supplementary runs rules have been used since the 1950s to improve...
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Amitava Mukherjee (XLRI -Xavier School of Management)15/09/2025, 14:10Statistical Process Monitoring
Monitoring time between events, operational delays or responding to a customer call is essential for maintaining and thriving to enhance service quality. Several aspects of the processes, including location such as median time, variability and shape, are pivotal. This paper introduces a Phase-II distribution-free cumulative sum (CUSUM) procedure based on a combination of three orthogonal rank...
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Sven Knoth (Helmut Schmidt University Hamburg, Germany)15/09/2025, 14:35Statistical Process Monitoring
The exponentially weighted moving average (EWMA) control chart was proposed already in 1959 and it became one of the most popular devices in statistical process monitoring (SPM) in the last decade of the previous century. Besides its most popular version for monitoring the mean of a normal distribution, many other statistical parameters were deployed as target for setting up an EWMA chart....
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Christian Capezza (Department of Industrial Engineering, University of Naples Federico II)15/09/2025, 14:55Statistical Process Monitoring
Modern industrial systems generate high-dimensional data streams often used for statistical process monitoring (SPM), i.e., distinguishing between multiple in-control and out-of-control (OC) states. While supervised SPM methods benefit from labeled data in assessing the process state, label acquisition is often expensive and infeasible at large scale. This work proposes a novel stream-based...
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Ewa Szymanska (FrieslandCampina)15/09/2025, 15:15Statistical Process Monitoring
Nowadays, big data is generated real-time in the majority of industrial production processes. Happenstance data is characterized by high volume, variety, velocity and veracity (4v of big data).
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In this study production data from industrial purification process is analyzed to assess process performance and its relations with product quality. For this purpose, a comprehensive data... -
Kostas Triantafyllopoulos (University of Sheffield)16/09/2025, 09:00Statistical Process Monitoring
Statistical Process Control (SPC) and its numerous extensions/generalisations focus primarily on process monitoring. This permits identification of out-of-control signals, which might be isolated out-of-control observations or a more persistent process aberration, but says nothing about remedying or controlling them. While isolated out-of-control signals require isolated interventions, a more...
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Mr Moritz Bauchrowitz (Technical University of Denmark, Novo Nordisk A/S)16/09/2025, 09:20Statistical Process Monitoring
Many chemometrics methods like Principal Component Analysis (PCA) function under the assumption of time independent observations, which may not be valid in most industrial applications. This is particularly true when PCA is employed for multivariate statistical process control. To handle time dependent data, Dynamic PCA (DPCA) has been proposed, which incorporates expanding the feature matrix...
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Theodoros Perdikis (University of Piraeus)16/09/2025, 09:40
Distribution-free (also known as nonparametric) control charts have been shown to be useful for on-line monitoring of lot production within a finite horizon production (FHP) process. Despite the partial process knowledge at the beginning of production in a FHP process, a distribution-free control chart can be started without any restrictive assumption about the underlying distribution of the...
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38. Suitability of Parametric and Nonparametric Statistical Methods for Turboprop Engine DiagnosticsZuzana Hübnerová (Brno University of Technology)16/09/2025, 10:05Statistical Process Monitoring
Turboprop engines undergo regular inspections, yet continuous analysis of in-flight sensor data provides an opportunity for earlier detection of wear and degradation—well before scheduled maintenance. The choice of statistical method plays a crucial role in ensuring diagnostic accuracy and interpretability. In this study, we compare the performance of traditional parametric...
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Fabian Mies (Delft University of Technology)16/09/2025, 10:25Statistical Process Monitoring
The power curve of a wind turbine describes the generated power as a function of wind speed, and typically exhibits an increasing, S-shaped profile. We suggest to utilize this functional relation to monitor the wind energy systems for faults, sub-optimal controls, or unreported curtailment. The problem is formulated as a regression changepoint model with isotonic shape constraints on the model...
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Mr Emanuele Rossi (Università degli studi di Napoli Federico II)16/09/2025, 10:45Statistical Process Monitoring
Electric batteries are often connected in parallel to ensure a wider power supply range to external electrical loads. Their condition is routinely monitored through the current measured when the batteries supply power. When the condition is adequate, the current is balanced throughout the system, with each battery contributing equally to the electrical load.
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To ensure that monitoring focuses... -
Panagiotis Tsiamyrtzis (Politecnico di Milano)17/09/2025, 09:00Statistical Process Monitoring
The Shiryaev’s change point methodology is a powerful Bayesian tool in detecting persistent parameter shifts. It has certain optimality properties when we have pre/post-change known parameter setups. In this work we will introduce a self-starting version of the Shiryaev’s framework that could be employed in performing online change point detection in short production runs. Our proposal will...
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Praise Obanya (North-West University)17/09/2025, 09:20Statistical Process Monitoring
Statistical process monitoring (SPM) is used widely to detect changes or faults in industrial processes as quickly as possible. Most of the approaches applied in industry are based on assuming that the data follows some parametric distribution (e.g., normality). However, in industry this assumption is not always feasible and limits the application and usefulness of SPM for fault detection. In...
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Eva Soto-Antonio (Universidade da Coruña)17/09/2025, 09:40Statistical Process Monitoring
Recent advances in the Internet of Things (IoT) and sensor technologies have provided powerful tools for the continuous, real-time monitoring of highly complex systems characterized by a wide range of features. This is particularly relevant for HVAC systems in buildings, where the objective is to maintain appropriate levels of hygrothermal comfort while minimizing energy consumption. As such,...
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