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Automatic anode rod inspection in aluminum smelters using deep-learning techniques: a case study
(2020)
Automatic fault detection using machine learning has become an exciting and promising area of research. This because it accurate and timely way to manage and classify with minimal human effort. In the computer vision community, deep-learning methods have become the most suitable approaches for this task. Anodes are large carbon blocks that are used to conduct electricity during the aluminum reduction process. The most basic function of anode rod inspection is to prevent a situation where the anode rod will not fit into the stub-holes of a new anode. It would be the case for a rod containing either severe toe-in, missing stubs, or a retained thimble on one or more stubs. In this work, to improve the accuracy of shape defect inspection for an anode rod, we use the Fast Region-based Convolutional Network method (Fast R-CNN), model. To train the detection model, we collect an image dataset composed of multi-class of anode rod defects with annotated labels. Our model is trained using a small number of samples, an essential requirement in the industry where the number of available defective samples is limited. It can simultaneously detect multi-class of defects of the anode rod in nearly real-time.
Today, many companies are adapting their strategy, business models, products, services as well as business processes and information systems in order to expand their digitalization level through intelligent systems and services. The paper raises an important question: What are cognitive co-creation mechanisms for extending digital services and architectures to readjust the usage value of smart services? Typically, extensions of digital services and products and their architectures are manual design tasks that are complex and require specialized, rare experts. The current publication explores the basic idea of extending specific digital artifacts, such as intelligent service architectures, through mechanisms of cognitive co-creation to enable a rapid evolutionary path and better integration of humans and intelligent systems. We explore the development of intelligent service architectures through a combined, iterative, and permanent task of co-creation between humans and intelligent systems as part of a new concept of cognitively adapted smart services. In this paper, we present components of a new platform for the joint co-creation of cognitive services for an ecosystem of intelligent services that enables the adaptation of digital services and architectures.
AI technologies such as deep learning provide promising advances in many areas. Using these technologies, enterprises and organizations implement new business models and capabilities. In the beginning, AI-technologies have been deployed in an experimental environment. AI-based applications have been created in an ad-hoc manner and without methodological guidance or engineering approach. Due to the increasing importance of AI-technologies, however, a more structured approach is necessary that enable the methodological engineering of AI-based applications. Therefore, we develop in this paper first steps towards methodological engineering of AI-based applications. First, we identify some important differences between the technological foundations of AI- technologies, in particular deep learning, and traditional information technologies. Then we create a framework that enables to engineer AI-applications using four steps: identification of an AI-application type, sub-type identification, lifecycle phase, and definition of details. The introduced framework considers that AI-applications use an inductive approach to infer knowledge from huge collections and streams of data. It not only enables the rapid development of AI-application but also the efficient sharing of knowledge on AI-applications.
Digital technologies are main strategic drivers for digitalization and offer ubiquitous data availability, unlimited connectivity, and massive processing power for a fundamentally changing business. This leads to the development and application of intelligent digital systems. The current state of research and practice of architecting digital systems and services lacks a solid methodological foundation that fully accommodates all requirements linked to efficient and effective development of digital systems in organizations. Research presented in this paper addresses the question, how management of complexity in digital systems and architectures can be supported from a methodological perspective. In this context, the current focus is on a better understanding of the causes of increased complexity and requirements to methodological support. For this purpose, we take an enterprise architecture perspective, i.e. how the introduction of digital systems affects the complexity of EA. Two industrial case studies and a systematic literature analysis result in the proposal of an extended Digital Enterprise Architecture Cube as framework for future methodical support.
Artificial Intelligence-based Assistants AIAs are spreading quickly both in homes and offices. They already have left their original habitats of "intelligent speakers" providing easy access to music collections. The initiated a multitude of new devices and are already populating devices such as TV sets. Characteristic for the intelligent digital assistants is the formation of platforms around their core functionality. Thus, AIS capabilities of the assistants are used to offer new services and create new interfaces for business processes. There are positive network effects between the assistants and the services as well as within the services. Therefore, many companies see the need to get involved in the field of digital assistants but lack a framework to align their initiatives with their corporate strategies. In order to lay the foundation for a comprehensive method, we are therefore investigating intelligent digital assistants. Based on this analysis, we are developing a framework of strategic opportunities and challenges.
Intelligent systems and services are the strategic targets of many current digitalization efforts and part of massive digital transformations based on digital technologies with artificial intelligence. Digital platform architectures and ecosystems provide an essential base for intelligent digital systems. The paper raises an important question: Which development paths are induced by current innovations in the field of artificial intelligence and digitalization for enterprise architectures? Digitalization disrupts existing enterprises, technologies, and economies and promotes the architecture of cognitive and open intelligent environments. This has a strong impact on new opportunities for value creation and the development of intelligent digital systems and services. Digital technologies such as artificial intelligence, the Internet of Things, service computing, cloud computing, blockchains, big data with analysis, mobile systems, and social business network systems are essential drivers of digitalization. We investigate the development of intelligent digital systems supported by a suitable digital enterprise architecture. We present methodological advances and an evolutionary path for architectures with an integral service and value perspective to enable intelligent systems and services that effectively combine digital strategies and digital architectures with artificial intelligence.
Artificial Intelligence enables innovative applications, and applications based on Artificial Intelligence are increasingly important for all aspects of the Digital Economy. However, the question of how AI resources such as tools and data can be linked to provide an AI-capability and create business value is still open. Therefore, this paper identifies the value-creating mechanisms of connectionist artificial intelligence using a capability-oriented view and points out the connections to different kinds of business value. The analysis supports an agenda that identifies areas that need further research to understand the mechanism of value creation in connectionist artificial intelligence.
Predictive maintenance information systems: the underlying conditions and technological aspects
(2020)
Predictive maintenance has the potential to improve the reliability of production and service provisioning. However, there is little knowledge about the proper implementation of predictive maintenance in research and practice. Therefore, we conducted a multi-case study and investigated underlying conditions and technological aspects for implementing a predictive maintenance system and where it leads to. We found that predictive maintenance initiatives are triggered by severe impacts of failures on revenue and profit. Furthermore, successful predictive maintenance initiatives require that pre-conditions are fulfilled: Data must be available and accessible. Very important is also the support by the management. We identified four factors important for the implementation of predictive maintenance. The integration of data is highly facilitated by Cloud-based mechanisms. The detection of events is enabled by advanced analytics. The execution of predictive maintenance operations is supported by data-driven process automation and visualization.
Scenario-based analysis is a comprehensive technique to evaluate software quality and can provide more detailed insights than e.g. maintainability metrics. Since such methods typically require significant manual effort, we designed a lightweight scenario-based evolvability evaluation method. To increase efficiency and to limit assumptions, the method exclusively targets service- and microservice-based systems. Additionally, we implemented web-based tool support for each step. Method and tool were also evaluated with a survey (N=40) that focused on change effort estimation techniques and hands-on interviews (N=7) that focused on usability. Based on the evaluation results, we improved method and tool support further. To increase reuse and transparency, the web-based application as well as all survey and interview artifacts are publicly available on GitHub. In its current state, the tool-supported method is ready for first industry case studies.
While many maintainability metrics have been explicitly designed for service-based systems, tool-supported approaches to automatically collect these metrics are lacking. Especially in the context of microservices, decentralization and technological heterogeneity may pose challenges for static analysis. We therefore propose the modular and extensible RAMA approach (RESTful API Metric Analyzer) to calculate such metrics from machine-readable interface descriptions of RESTful services. We also provide prototypical tool support, the RAMA CLI, which currently parses the formats OpenAPI, RAML, and WADL and calculates 10 structural service-based metrics proposed in scientific literature. To make RAMA measurement results more actionable, we additionally designed a repeatable benchmark for quartile-based threshold ranges (green, yellow, orange, red). In an exemplary run, we derived thresholds for all RAMA CLI metrics from the interface descriptions of 1,737 publicly available RESTful APIs. Researchers and practitioners can use RAMA to evaluate the maintainability of RESTful services or to support the empirical evaluation of new service interface metrics.