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This work proposes a non-destructive quality control for a pitting process of cherries. A system composed of a video camera and a light source records pictures of backlit cherries. The images processing in MATLAB environment provides the dynamic histograms of the pictures, which are analysed to state the presence of the pit. A feedforward artificial neural network was implemented and trained with the histograms obtained. The network developed allows a fast detection of stone fractions not visible by human inspection and the reduction of the accidental reject of properly manufactured products. [ABSTRACT FROM AUTHOR]

With the advance and necessary implementation of technology nowadays, new challenges come along for educational centers. Undoubtedly, the most important ones in terms of taking ad-vantage of technologies for the optimization of processes, involve the identification of the equipment and the tools that are adapted to their own requirements or needs; exploring the possibilities for the management and streamlining of processes, and making a rational and planned use of them. For this reason, the aim of this paper is to review a case of application of information and communication technologies (ICT) in the administrative area of a higher education institution, by means of a management system oriented to the structuring of academic pro-grams. The methodological strategy that was used for this research was of one of descriptive and exploratory nature, so that the areas of greatest problem for teachers could be identified and alternative solutions could be generated. Thus, it was concluded that the automation of teacher planning processes represents an area of opportunity for improvement in administrative management, to the extent that it decreases delivery times and improves teacher performance, and keeps not only the staff updated in the use of ICTs, but also the processes of the in-situation. [ABSTRACT FROM AUTHOR]

This paper presents selected possibilities for mathematical models in predictive maintenance of equipment. This model includes automatic classification of machines by labor intensity, determination of labor intensity standards, and drawing up monthly and yearly maintenance plans for manufacturing lines and technical equipment in an engineering company. This model reduces human error, clarifies accounting and operational records of machines, evaluates the actual maintenance labor intensity, eliminates routine administrative work, enables the use of cloud storages, and includes automatic reporting of problems in the case of on-board diagnostic systems. It is based on differentiated machine care, can be an effective tool for the overall optimization of maintenance processes, and is a part of the digitization of these processes in engineering companies. [ABSTRACT FROM AUTHOR]

Several studies were conducted to demonstrate the application of Process Mining (PM) techniques to Ethereum-compatible application event data. However, the availability of event data is constrained by the application's process awareness, which is under-reported in the literature. Based on domain analysis, which identified several challenges to mining the business process from blockchain applications, a framework was designed, instantiated, and tested in this study. The framework supports identification of appropriate cases for PM and automates the generation of event logs from blockchain data. It consists of two modules, the Process Awareness Recognizer (PAR) and the Event Log Generator (ELG). PAR is a rule-based classifier to assess the process awareness of a given application. ELG is an automated batch processing model consisting of three methods: (1) Extractor: to retrieve event data from blockchains; (2) Decoder: to transform the extracted data to a human-readable format; and (3) Formatter: to produce event log files in a format compatible with PM tools. It was validated by implementing a proof-of-concept application with an input set of 201 real-world applications. The results prove the framework's feasibility and applicability. [Display omitted] [ABSTRACT FROM AUTHOR]

The world of logistics is changing and entering a new era. The advance of digitalization and technologization enables new business models, increased process efficiencies, novel planning approaches, and much more but, on the downside, there is also the risk of being lost in the maelstrom of developments. Within these developments, the automation of logistics processes and ultimately the design of autonomous logistics systems is one of the most defining trends that has far-reaching consequences for the planning and execution of future logistics processes. This Special Issue aims to contribute to the discussion and to get to the bottom of the question of how the path towards automated and autonomous logistics systems should be designed. This editorial lays a foundation by presenting application areas of automation and discussing the theoretical path towards autonomous logistics systems. The articles that follow provide highly practical insights into current research results on the automation and autonomization of informational and physical logistics processes.

The article contains an introduction to BPM and presents the examples of BPM deployment in high schools. The main part of the article contains the key preparation and realization stages of BPM course for students of logistics at Maria Curie-Skłodowska University (MCSU) in Poland. The main conclusions from the BPM market analysis and course preparation suggest that BPM competences are strongly desirable and BPN can be a powerful tool for improving organizational efficiency. Availability of many standards and tools makes it difficult to develop BPM course tailored to market needs and study programmes. Moreover ideal teaching software should cover the wide range of models and most popular standards as well as should reflect the whole BPM lifecycle. To meet the requirements we enhanced ADONIS software with tools for process automation like Bonita Suite.

The article describes the urgency of this problem and modern ways of its solving. Modern disinfection and lighting systems are not developed enough. The authors proposed to automate the system of disinfection, dust removal and lighting in the farm building. We developed algorithm on the basis of which the universal program in the programming language C ++ was written. Assembled the microprocessor set ARDUINO. The program is included in the microprocessor ATMEGA, which is part of ARDUINO. As a basis for the disinfection system, a two-component ARUF device was used, which includes two decontaminating components: an ultraviolet lamp and an aerosol dispenser. Each of the algorithms (lighting and disinfection) consists of 3 parts: data collection and analysis, control, data output to the display. The sequence of 3 parts is 1 cycle, then it repeated. In the first part on the basis of indications of sensors assignment to variables of some values was carried out. In the second part, based on the values of the variables, the decontamination and lighting devices are controlled. In the third part due to changing the screen number, variable, it is possible to switch the display and view the status of the installation and external parameters. The program, written on the basis of algorithms, is universal and suitable for almost any poultry and livestock buildings. All parameters (the time of the beginning and completion of the ARUV operation within 24 hours, the daily time of the illumination of a certain cycle, the maximum and minimum humidity, the duration of each cycle) used for control and management are variables. Operator can set the items using the display in the menu.

In the process of organizing food production, there is a constant need to control the quality and safety of products, control technological and organizational and managerial processes. The solution of this task is the internal audit of the integrated management system that operates at the enterprise, which allows identifying and timely eliminating inconsistencies. When carrying out internal checks, a large amount of data is generated about the operation of the enterprise, including positive and negative observations, information on identified observations, analysis of causes of inconsistencies, corrective and preventive actions. With the current level of information technology development, organizations can optimize the process of data collecting, accounting and analyzing. The proposed software product is designed to ensure the passage of audit within the organization, using a systematic approach to evaluate and improve the efficiency of processes through the use of computer technology and statistical methods. To develop the program the requirements of the standards for management systems and audit have been analyzed; classification of inconsistencies and statistical data on the elimination of comments at the enterprise has been applied. Based on the obtained data a program has been developed in the Microsoft Visual Studio 2010 for data processing and generating automated reports for the selected period. The result of the research is the development of recommendations "Audit of an integrated management system" for optimizing the process by the example ofconfectionery production. Forms for accounting inconsistencies, containing data on inconsistencies, its classification in terms of significance, in terms of ensuring the safety of manufactured products, planned corrective actions, as well as the form of notification of deadlines for remedial measures have been developed. The "Analysis of audit data" sub-process has been automated. To approbate the software product a list of inconsistencies based on the results of external and internal audits for 2016 has been designed and ranked.

Simulation experiments performed while solving multidisciplinary engineering and scientific problems require joint usage of multiple software tools. Further, when following a preset plan of experiment or searching for optimum solu- tions, the same sequence of calculations is run multiple times with various simulation parameters, input data, or conditions while overall workflow does not change. Automation of simulations like these requires implementing of a workflow where tool execution and data exchange is usually controlled by a special type of software, an integration environment or plat- form. The result is an integration workflow (a platform-dependent implementation of some computing workflow) which, in the context of automation, is a composition of weakly coupled (in terms of communication intensity) typical subtasks. These compositions can then be decomposed back into a few workflow patterns (types of subtasks interaction). The pat- terns, in their turn, can be interpreted as higher level subtasks.This paper considers execution control and data exchange rules that should be imposed by the integration envi- ronment in the case of an error encountered by some integrated software tool. An error is defined as any abnormal behavior of a tool that invalidates its result data thus disrupting the data flow within the integration workflow. The main requirementto the error handling mechanism implemented by the integration environment is to prevent abnormal termination of theentire workflow in case of missing intermediate results data. Error handling rules are formulated on the basic pattern level and on the level of a composite task that can combine several basic patterns as next level subtasks. The cases where workflow behavior may be different, depending on user's purposes, when an error takes place, and possible error handling op- tions that can be specified by the user are also noted in the work.

The current experience-dominated structural design process separates the structural design from CAE analysis in the development of a new wind turbine gearbox,which leads to a quite long-term development cycle and the redundancy of structural materials.Consequently,a new structural design process is presented based on topological optimization theory and combined with CAE.Through a secondary development for Hyperworks with C# and TCL / TK language,a new process automatic system characterized by lightweight structure for the structural design of wind turbine gearboxes is developed.The operability of the system is testified by the instance of designing a planet carrier.The case indicates that the process automatic system can lead designers to finish the lightweight-oriented structural design for wind turbine gearbox parts,and the pre and post processing efficiency can increase by 90% than the current process,and the development cycle of new products of wind turbine gearbox is shorten enormously.

Cell and gene therapies (CGTs) are intended to address many different diseases, including widespread diseases with millions of patients. The success of CGTs thus depends on the practicability of scaling cell manufacturing to population. It is obvious that process integration and automation are key for the reproducibility, quality, cost-effectiveness, and scalability of cell manufacturing. Still, different manufacturing concepts can be considered depending on the characteristics of cell therapies such as the degree of ex vivo manipulation of cells, the intended treatment scheme for the underlying medical indication, the prevalence of the indication, and the cell dose per final drug product. Here, we explain the characteristics of cellular products and their implications from the perspective of a manufacturer. We outline and exemplify with a list of devices' different strategies and scaling options for CGT manufacturing considering technical and regulatory aspects in the early and late clinical development of cellular products. Finally, we address the need for appropriate in-process and quality controls and the regulatory requirements and options for improvements of a cellular product at different manufacturing stages., (Copyright © 2024 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.)

One of the main benefits of Building Information Modelling is the capability of improving the decision-making process thanks performing what-if tests on digital twins of the building to be realized. Pairing BIM models to Building Energy Models allows designers to determine in advance the energy consumption of the building, improving sustainability of the construction. The challenge is to consider as many elements involved in the energy balance as possible and shuffling their parameters within a certain range. In this work, the automatic creation of a relevant set of design options to be analyzed for searching the optimum has been carried out. Firstly, the usual workflow that would be applied manually has been automatically followed by running scripts and codes, depending just on the initial setup given by the user. Although the procedure is very resource consuming, the main advancement relies in the reduction of the manual intervention and the possibility of creating large datasets of design options, avoiding gross errors. Secondly, Artificial Neural Networks and Transfer Learning techniques are applied to speed up the process of dataset creation. With such approach, the same dataset has been created, with about 30% of initial data and without significant loss of accuracy.

There are many initiatives and technologies working towards implementing factories of the future. One consensus is that the classical hierarchical automation system design needs to be flattened while supporting the functionality of both Operation Technology (OT) and Information Technology (IT) within the same network infrastructure. To achieve the goal of IT/OT convergence in process automation, an evolutionary transition is preferred. Challenges are foreseen during the transition, mainly caused by the traditional automation architecture, and the main challenge is to identify the gap between the current and future network architectures. To address the challenges, in this paper, we describe one desired future scenario for process automation and carry out traffic measurements from a pulp and paper mill. The measured traffic is further analyzed, which reveals representative traffic characteristics in the process automation. Finally, the key challenges and future directions towards a system architecture for factories of the future are presented.

A data-driven approach in production logistics is adopted as a response to challenges such as low visibility and system rigidity. One important step for such a transition is to identify the enabling technologies from a value-creating perspective. The existing corpus of literature has discussed the benefits and applications of smart technologies in overall manufacturing or logistics. However, there is limited discussion specifically on a production logistics level, from a systematic perspective. This paper addresses two issues in this respect by conducting a systematic literature review and analyzing 142 articles. First, it covers the gap in literature concerning mapping the application of these smart technologies to specific production logistic activities. Ten groups of technologies were identified and production logistics activities divided into three major categories. A quantitative share assessment of the technologies in production logistics activities was carried out. Second, the ultimate goal of implementing these technologies is to create business value. This is addressed in this research by presenting the “production logistics data lifecycle” and the importance of having a balanced holistic perspective in technology development. The result of this paper is beneficial to build a ground to transit towards a data-driven state by knowing the applications and use cases described in the literature for the identified technologies.

The aim was to develop an electronic data capture (EDC) system to capture patient-reported outcome (PRO) measures successfully by automating processes identified as barriers to implementation. Clinical success, research impact, and patient acceptance of this system were evaluated during a pilot and a follow-up period 2 years later. During the pilot, there were 44,831 eligible visits. Capture rate was 99.0% (44,374 visits) and completion rate was 99.4% (44,108 visits). Capture rate was 99.4% and completion rate was 95.2% during the follow-up period. Zero help desk tickets were put in for the EDC system during either time period. Patients accepted the EDC system both during the pilot (1.4% refusal rate) and follow-up period (1.2%). An automated Structured Query Language server feed provided data used to produce numerous abstracts and manuscripts. Automation was crucial to overcoming implementation barriers and delivering PRO scores to the electronic health record in real time with minimal impact on clinical workflow. Automation also has supported PRO research. [ABSTRACT FROM AUTHOR]

Eliminating loop interaction is a critical issue in the control of Multi Input Multi Output (MIMO) systems. In this study, an experimental setup, Festo Didactic MPS-PA, is used as MIMO system. Inverted decoupling PID method is applied to the system to eliminate the loop interactions of MIMO system. For this purpose, firstly, the system is modelled. Then, decoupler blocks are designed to eliminate the loop interactions. Finally, the modelled system is simulated, and experiments are performed on the real system. The effect of inverted decoupling PID control on the system performance and the effect of classical PID control on the system performance are compared. [ABSTRACT FROM AUTHOR]

Industrial waterjet stripping/cleaning is a prime example of a dull, dirty, dangerous manufacturing process that is ripe for automation, yet it remains a manual task in most instances due to complex workpiece geometry and/or low-volume, high-mix production. Recently developed automated tool trajectory planning algorithms and collaborative path planning frameworks offer a potential solution but are of limited use without corresponding process models and simulation tools to evaluate toolpath quality. Existing process models do not consider the spray impingement angle or the cumulative effect of successive tool passes—both of which are inevitable when spraying geometries that possess concave and/or discontinuous features. This research proposes a novel process model that includes impingement angle and accounts for the cumulative, ablative nature of the process. It also develops a simulation algorithm that applies this model to complex geometries while considering shading effects caused by protrusions and overhangs. Model parameters are determined via a design of experiments approach and nonlinear regression, and verification experiments on complex test parts show good agreement between predicted and measured results. Paired with the aforementioned trajectory planning tools, this research represents a complete robotic process planning solution for waterjet stripping/cleaning of complex parts in high-mix, low-volume manufacturing. [ABSTRACT FROM AUTHOR]

During the last three decades, technological innovations in cargo handling equipment have made it possible to automate operational processes in container terminals. Despite the increasing trend in terminal automation, little work has been done to develop theoretical guidelines for evaluating the benefits of this industrial practice. We assess terminal automation by focusing on whether strategic content and process structure are aligned. In this study, we explore the reasons that these results are mixed in the context of service automation. Have market competitiveness and operational performance been enhanced by automation in seaports? We focus on two key strategic elements and their proper alignment to produce the best performance for a port. The first element is the overall business strategy and strategic content adopted by the port. In this study, we look at Porter's (Competitive strategy, Free Press, New York, 1980) generic strategic classification of low cost, differentiation, or focus strategies. The second element is the process structure of the port, which may have been impacted by technological innovation. Using the framework of contingency theory, we explore the interface of strategic content and process structure and how this interface impacts the service process automation. A multiple case study is conducted on a sample of 20 container terminals, selected from the list of 2014 Journal of Commerce's Top Productive Terminals. We come up with three important findings. First, a port's strategic market position determines the choice of overall business strategy. If a port is strategically positioned as an international gate, then it should adopt an overall cost-leadership strategy, whereas a transshipment terminal should adopt an overall differentiation strategy. Second, we find that the process structure adopted is associated with the level of automation, and a differentiation strategy is dependent on the level of flexibility, speed, and reliability. Higher market uncertainty requires higher flexibility, while lower market uncertainty requires greater speed and reliability. Third, the level of process automation depends on throughput volume and stability. Closer relationships with maritime supply–chain partners help increase throughput volume and reduce throughput uncertainty. [ABSTRACT FROM AUTHOR]

Dough fermentation plays an essential role in the bread production process, and its success is critical to producing high-quality products. In Germany, the number of stores per bakery chain has increased within the last years as well as the trend to finish the bakery products local at the stores. There is an unsatisfied demand for skilled workers, which leads to an increasing number of untrained and inexperienced employees at the stores. This paper proposes a method for the automatic monitoring of the fermentation process based on optical techniques. By using a combination of machine learning and superellipsoid model fitting, we have developed an instance segmentation and parameter estimation method for dough objects that are positioned inside a fermentation chamber. In our method we measure the given topography at discrete points in time using a movable laser sensor system that is located at the back of the fermentation chamber. By applying the superellipsoid model fitting method, we estimated the volume of each object and achieved results with a deviation of approximately 10% on average. Thereby, the volume gradient is monitored continuously and represents the progress of the fermentation state. Exploratory tests show the reliability and the potential of our method, which is particularly suitable for local stores but also for high volume production in bakery plants. [ABSTRACT FROM AUTHOR]

As banks strive to find ways to increase profits against headwinds that include continued low interest rates and intensive regulation, they are compelled to look within all areas of the organisation for efficiencies that result in cost savings. Fierce competition from traditional competitors and the emergence of financial technology (Fin-Tech) in traditional banking areas are further pushing organisations to reduce costs, eliminate credit risk loses and optimise capital utilisation. For too long, commercial and corporate lending ('banking book') businesses have been hampered by manual processes, fragmented systems and multiple data sources. As a result, banking book lending is exposed to increased operational and credit risk and inefficient business decision making — impeding their ability to scale efficiently and therefore eroding profits. Loan collateral mitigates credit risk while also providing the opportunity to mitigate pressure on banking book performance through efficient use. To achieve these goals, however, automated and holistic collateral management operations are mandatory: to, specifically, reduce counterparty and operational risk; eliminate manual processes; improve capital efficiency and provide the accurate datasets required for capital reporting and management. Transformation to an automated and holistic banking book collateral management operation also enables the deployment of digital technologies including machine learning (artificial intelligence) and distributed ledger technology, adding further to the potential to improve risk management, operational efficiency and scalability across the banking book. [ABSTRACT FROM AUTHOR]

Single-diaphragm forming is a cost saving alternative to labor-intensive ply-by-ply layup. This paper reports on forming of flat uncured multi-angle prepreg stacks into C shape. The main focus is on the forming-process profile , which has attracted little attention in previous studies on the topic. Hexcel's M21E/IMA prepreg is examined within the study to analyze the particular effect of the prepreg's interleaf layers on the forming process and vice versa. Specimens with different multi-angle stackings were formed at 40, 60 and 80 °C, on male tools with 4, 6 and 8 mm radii. It is shown that the composed infrastructure setup allows for a precise control of the recipient pressure profile. The forming status is monitored based on a resistance-measurement-based approach, whose application suggests a two-phase forming process characteristic. Recipient pressure levels of 60 and 510 mbar below ambient pressure were identified as practical for gentle forming. It could be shown that interleaf layers of M21E/IMA specimens are not harmed considerably by the forming procedure. Overall, the proposed forming process led to prepreg preforms of adequate quality, suitable for series production. [ABSTRACT FROM AUTHOR]

Copyright of HMD: Praxis der Wirtschaftsinformatik is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

• The development and concept of Quality-by-Control (QbC) was discussed and defined. • Perspectives on QbC in pharmaceutical continuous manufacturing were provided. • Proof-of-concept towards QbC was demonstrated in a continuous manufacturing process. The Quality-by-Design (QbD) guidance issued by the US Food and Drug Administration (FDA) has catalyzed the modernization of pharmaceutical manufacturing practices including the adoption of continuous manufacturing. Active process control was highlighted recently as a means to improve the QbD implementation. This advance has since been evolving into the concept of Quality-by-Control (QbC). In this study, the concept of QbC is discussed, including a definition of QbC, a review of the recent developments towards the QbC, and a perspective on the challenges of QbC implementation in continuous manufacturing. The QbC concept is demonstrated using a rotary tablet press, integrated into a pilot scale continuous direct compaction process. The results conclusively showed that active process control, based on product and process knowledge and advanced model-based techniques, including data reconciliation, model predictive control (MPC), and risk analysis, is indispensable to comprehensive QbC implementation, and ensures robustness and efficiency. [ABSTRACT FROM AUTHOR]

Abstract Artificial intelligence (AI) is a powerful tool that is commonly applied in engineering multi-disciplines owing to its functionality to resolve real-world problems where deterministic solutions are arduous to achieve. Revolution in water treatment and desalination process automation has been emerging recently. Several challenges are present in the water sector related to data structuring and smart water services through which AI would have great potential once those issues are addressed. The distinctive tools of AI, mainly; artificial neural networks (ANNs), as a regression model, and genetic algorithm (GA), as one of the global optimization techniques, have been immensely applied in desalination and water treatment for multi-purpose applications. Modelling desalination and water treatment processes and optimizing the operating condition are few among the many applications. In the current review, paramount applications of AI tools in desalination and water treatment have been thoroughly reviewed. In addition, benchmarking ANNs with the conventional modelling approaches were highlighted, along with the shortcomings and challenges expected to associate with these common tools in some complex nature practical application. It was concluded that the use of AI tools will undoubtedly pave the way in the water sector towards better operation, process automation, and water resources management in an increasingly volatile environment. Highlights • Machine learning and process automation prospects in desalination. • Limitations of classical approaches in predicting membrane characteristics and performance. • Modelling of ions and pollutant removal by Artificial neural network and genetic algorithms. • Statistical algorithms for optimizing desalination plants. • Cost and efficiency prediction by artificial intelligence for desalination application. [ABSTRACT FROM AUTHOR]

To remain competitive in a connected world, offline retailers have responded with integrating digital in-store technologies into their physical servicescapes. Often, the introduction of multichannel connecting services like click & collect or order from or return to store are first steps. Shopper-facing advanced technologies can be key to creating a different physical shopping experience for consumers and delivering benefits to retailers such as improved traffic, conversion and baskets or streamlined operational cost. In general, consumers consider retailing technologies as useful. However, shoppers assess the fairness of the exchange about procedures, outcome and treatment and the value of the technology they receive compared to what the retailer gets. Also, satisfaction, trust and privacy concerns are relevant for customers. Retail managers need to ensure the functionality and safety of their application and take consumer concerns seriously. Also, they need to address privacy concerns and build trust, if they want proximity marketing to deliver on its promise of increasing basket size or attracting new shoppers. [ABSTRACT FROM AUTHOR]

Anomalies are data points or a cluster of data points that lie away from the neighboring points or clusters and are inconsistent with the overall pattern of the data. Anomaly detection techniques help distinguish the anomalous observations from the regular ones, and thus provide the basis for developing a standard performance guideline for process control. The process of identifying anomalies becomes complicated in the absence of labeled training data as in supervised learning. Moreover, Euclidean distance between two points is less likely able to reflect the intrinsic structural distance imposed by the underlying manifold structure. In this paper, the authors propose a minimum spanning tree (MST)-based anomaly detection method. The merit of the method is that an MST provides a new distance measure, capable of capturing the relative connectedness of data points/clusters in a complicated manifold, and could be a better (dis)similarity metric, than the simple Euclidean distance, to identify anomalies in unsupervised learning settings. The proposed method is compared with 13 popular anomaly detection methods on 20 benchmark data sets, demonstrating a considerable improvement in its ability of identifying anomalies. Furthermore, the MST-based anomaly detection is applied to the data set from a hydropower turbine and demonstrates remarkable detection competence. Note to Practitioners—This paper is motivated by the problem of unsupervised anomaly detection in a hydropower generation plant, which operates with turbine systems that are instrumented with dozens of sensors. Each turbine has subcomponents or functional areas such as several bearing systems, a generator, and so on. Sensors collect various types of data in real time such as temperature of oil inside the bearing systems, temperature of the bearings, ambient temperature, vibrations in each functional areas, a variety of harmonics in functional areas, temperature of the coil in the generator, and many more. In total, each turbine collects more than 200 attributes from its sensors. The sensor data are then stored in a control system and kept as time stamped historical data points. When a service/maintenance engineer suspects that there is a malfunction in a turbine, she/he extracts a data set from the control system that contains the collected sensor data for that turbine for the selected period of time (few weeks to few months), and then stores this data in a relational databases or simply in a comma separate value (csv) file for further analysis. The objective is to efficiently identify and isolate anomalies in the turbines. Toward this goal, we propose a new solution for tackling this challenging problem, which is an unsupervised method based on the concept of MST. The proposed method can be used as a competitive tool to aid the practitioners in their search of anomalies for making their systems better. [ABSTRACT FROM AUTHOR]

Copyright of Automatisierungstechnik is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract The Process Analytical Technology (PAT) initiative published by the Food and Drug administration (FDA) describes PAT systems as important tools to optimize pharmaceutical processes in order to ensure the final product quality. Real-time particle size measurements provide a promising approach for process automation of fluid bed granulation processes. This work focuses on the introduction and evaluation of a new time-based buffer approach for in-line spatial filtering technique (SFT) in order to provide a minimum time delay of the real-time measurement. Comparing the new buffer system to the conventionally used number-based approach, a reduced influence of the particle rate and a constant response time was found. A performance qualification of the Parsum probe IPP 80 using this buffer approach according to the ICH guideline Q2 was performed to investigate whether common variations of process parameters influence the real-time particle size measurement. Focussing on the main requirements for process control, hereby a good precision and specificity as well as an adequate robustness was found. Summarized, this study identified the time-based buffer system as more suitable buffer system for process automation. Graphical abstract Unlabelled Image [ABSTRACT FROM AUTHOR]