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The increase of productivity and decrease of production loss is an important goal for modern industry to stay economically competitive. For that, efficient fault management and quick amendment of faults in production lines are needed. The prioritization of faults accelerates the fault amendment process but depends on preceding fault detection and classification. Data-driven methods can support fault management. The increasing usage of sensors to monitor machine health status in production lines leads to large amounts of data and high complexity. Machine Learning methods exploit this data to support fault management. This paper reviews literature that presents methods for several steps of fault management and provides an overview of requirements for fault handling and methods for fault detection, fault classification, and fault prioritization, as well as their prerequisites. The paper shows that fault prioritization lacks research about available learning methods and underlines that expert opinions are needed.
Cyber-physical systems become more complex, therewith production lines become more complex in the smart factory. Every employed system produces high amounts of data with unknown dependencies and relationships, making incident reasoning difficult. Context-aware fault diagnosis can unveil such relationships on different levels. A fault diagnosis application becomes context-aware when the current production situation is used in the reasoning process. We have already published TAOISM, a visual analytics model defining the context-aware fault diagnosis process for the Industry 4.0 domain. In this article, we propose the Flourish dashboard for context-aware fault diagnosis. The eponymous visualization Flourish is a first implementation of a context-displaying visualization for context-aware fault diagnosis in an Industry 4.0 setting. We conducted a questionnaire and interview-based bilingual evaluation with two user groups based on contextual faults recorded in a production-equal smart factory. Both groups provided qualitative feedback after using the Flourish dashboard. We positively evaluate the Flourish dashboard as an essential part of the context-aware fault diagnosis and discuss our findings, open gaps, and future research directions.
Smart factories are complex; with the increased complexity of employed cyber-physical systems, the complexity evolves further. Cyber-physical systems produce high amounts of data that are hard to capture and challenging to analyze. Real-time recording of all data is not possible due to limited network capabilities. Limited network capabilities are the reason for a chain of faults introduced via active surveillance during fault diagnosis. These introduced faults may slow down production or lead to an outage of the production line. Here, we present a novel approach to automatically select production-relevant shop floor parameters to decrease the number of surveyed variables and, at the same time, maintain quality in fault diagnosis without overloading the network. We were able to achieve higher throughput, mitigate communication losses and prevent the disruption of factory instructions. Our approach uses an autoencoder ensemble via minority voting to differentiate between normal—always on—variables and production variables that may yield a higher entropy. Our approach has been tested in a production-equal smart factory and was cross-validated by a domain expert.
Machine intelligence, a.k.a. artificial intelligence (AI) is one of the most prominent and relevant technologies today. It is in everyday use in the form of AI applications and has a strong impact on society. This article presents selected results of the 2020 Dagstuhl workshop on applied machine intelligence. Selected AI applications in various domains, namely culture, education, and industrial manufacturing are presented. Current trends, best practices, and recommendations regarding AI methodology and technology are explained. The focus is on ontologies (knowledge-based AI) and machine learning.
The manufacturing industry is undergoing a transformation marked by the emergence of Industry 4.0 and Industry 5.0 paradigms, which are characterized by the integration and automation of machinery. Thereby, the machinery evolves into Cyber-Physical Systems (CPSs). These CPSs consist of software and hardware modules implementing complex manufacturing processes. The ongoing integration of machinery, and external technologies, e.g., the Industrial Internet of Things (IIoT), led to an evolving Smart Manufacturing (SM) environment. At the same time, legacy machinery, the brownfield machinery, exists side-by-side with modern CPSs. The brownfield machinery might be integrated by retrofitting in the modern manufacturing process. Therefore, the evolution of the SM domain thriven by the Industry 4.0 and Industry 5.0 paradigms leads to a more complex SM environment. Moreover, the integration and ongoing adaption of technologies and processes introduce novel relationships and dependencies between employed machinery and systems. Fault Diagnosis (FD) in such a complex SM environment becomes more time-consuming and laborious. A side effect of the ongoing evolution is the advancing capabilities of the machinery and the ability to produce data. Therewith, not only complex data has to be analyzed during any FD but also vast quantities. The search for the origin of the fault is challenging. Additionally, technical challenges in the SM environment hinder a thorough FD. For instance, the available bandwidth for data transmission is unequal to the capabilities of the machinery to produce vast data quantities. Therefore, the application challenge exists to focus on specific areas of the SM environment while choosing a reasonable granularity in data surveillance to cover the fault traces without losing too much information. Thereby, any FD depends heavily on the domain knowledge of the professionals entrusted with the FD task. On top, there is also economic pressure, which raises the tension on the employed professionals as an unexpected downtime, and the loss in production quantity equals the economic loss.
The thesis introduces context-aware FD to mitigate the risen complexity of the SM environment and support the professionals in their work. By supporting the professionals, the time for FD can be reduced, which results in faster fault amendment and reduced cost-intensive production downtimes. The Context-Aware Diagnosis in Smart Manufacturing (TAOISM) Visual Analytics (VA) model backs the context-aware FD. The TAOISM VA model is the theoretical foundation for the context-aware FD and defines the data layer, the models layer, the visualization layer, and the knowledge layer for SM. Hereby, the VA model enables the definition of context, context models, and context hierarchies for their integration in the respective layers. The main idea behind the context-aware FD is to use the narrowing character of the context definition to slice vast amounts of data into manageable context-separated data groups. Thereby, the context model works as a virtual boundary across machinery and systems, which encloses the physical domain (hardware) and the immaterial domain (software) equally. Further, the thesis focuses on contextual faults, which arise from context model violations, and proposes approaches for collecting contextual data. Also, the automated building of context models and the extraction and transformation of contextual data is part of the thesis. Employing the context models impacts each layer of the proposed TAOISM VA model. For each layer, various approaches show the impact of the context models and their employment in three different application scenarios for FD in SM. The performed research is tested and verified in the scenarios of Robotics Application Development (RAD), Maintenance of Industrial Inspection Machines (MIIM) and Abnormal Event Management in Production Lines (AEMPL). Along with employing context models, data augmentation with context models is proposed. Along with other benefits, the presented data augmentation technique has the ability to balance undersampled datasets, which would enable a reduction of data recordings for any context-aware FD in the future. Thereby, the data augmentation technique is to answer the existing inaccuracies in an SM environment, which also impacts the quality of any employed Artificial Intelligence (AI). Another approach targets the unsupervised selection of production-relevant variables to focus FD-related data recordings and surveillance on areas of the SM environment active during production automatically without any domain knowledge involved. The hypothesis, which was proven right, was that faults, especially contextual faults, occur more often on active software and hardware modules. Another challenge from the vast amount of data is that labeling data for AI becomes uneconomical, even for small fault cases in AI. As a result, evaluating any AI model in SM becomes challenging, as standard measures, e.g., accuracy, precision, recall, and F1-score, cannot be applied. In this case, the thesis proposes novel AI performance metrics that decouple comparability and correctness to enable the evaluation of AI models in an SM environment. All the contributions have led to the development of two distinct Proof of Concepts (PoCs). The PoCs are the reference implementation of the context-aware FD and reflect a knowledge-based FD Expert System (ES) and an unsupervised data-driven FD system. The latter was part of the thorough evaluation of the context-aware FD by two groups of domain experts and junior professionals. The successful qualitative evaluation not only hints towards a working context-aware FD but also unveils future research directions and a future vision for SM. Additional domain expert interviews expose the views on the relevancy of a context-aware FD in SM for the future. In general, the evaluation hints towards a context-aware FD, which has versatile applicability, usability, and suitability in SM-related FD.