A Data-Driven Holistic Approach to Fault Prognostics in a Cyclic Manufacturing Process

Kozjek, Dominik; Vrabič, Rok; Kralj, David; Butala, Peter

doi:10.1016/j.procir.2017.03.109

Cited by 19 publications

(8 citation statements)

References 9 publications

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“…2 Title: A data-driven holistic approach to fault prognostics in a cyclic manufacturing process (Kozjek et al 2017a) Description: A data-driven holistic approach, which includes data generation, acquisition, storage, processing, and prognostics, is shown in the case of a typical cyclic manufacturing process, i.e., plastic injection moulding (PIM). The approach is able to tackle the high dimensionality and the large size of the data to create and evaluate prediction models for prognostics of the unplanned machine stops.…”

Section: Methodsmentioning

confidence: 99%

“…This section demonstrates the use of a conceptual framework on the selected studies of introducing data analytics in manufacturing systems. Five existing case studies of developing data-analytics solutions in manufacturing systems, i.e., (Kozjek et al 2017a(Kozjek et al , 2017b(Kozjek et al , 2018a(Kozjek et al , 2018bVrabič, Kozjek, and Butala 2017), are selected. Data-analytics solutions developed within these projects are either innovative ways of reducing the incompleteness of information and discovering new knowledge through additional use of data or they enable the more efficient reduction of information incompleteness than the conventional approaches.…”

Section: Demonstrating the Use Of A Conceptual Framework On Selected Studiesmentioning

confidence: 99%

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Advancing manufacturing systems with big-data analytics: A conceptual framework

Kozjek

Vrabič

Rihtaršič

et al. 2020

International Journal of Computer Integrated Manufacturing

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With the intensive development and implementation of information and communication technologies in manufacturing, large amounts of heterogeneous data are now being generated, gathered and stored. Handling large amounts of complex dataoften referred to as big datarepresents a challenge as there are many new approaches, methods, techniques, and tools for data analytics that open up new possibilities for exploiting data by converting them into useful information and/or knowledge.However, the application of advanced data analytics in manufacturing lags behind in terms of penetration and diversity in comparison with other domains such as marketing, healthcare and business, meaning that the available data often remain unexploited. This paper proposes a new conceptual framework for systematically introducing big-data analytics into manufacturing systems. To this end, the paper defines a new stepwise procedure that identifies what knowledge and skills, and which reference models, software and hardware tools, are needed for the development, implementation and operation of data-analytics solutions in manufacturing systems. The feasibility of the proposed conceptual framework is demonstrated in a case study from an engineer-to-order company and by mapping the framework to several previous data-analytics projects.

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Section: Methodsmentioning

confidence: 99%

Section: Demonstrating the Use Of A Conceptual Framework On Selected Studiesmentioning

confidence: 99%

Advancing manufacturing systems with big-data analytics: A conceptual framework

Kozjek

Vrabič

Rihtaršič

et al. 2020

International Journal of Computer Integrated Manufacturing

Self Cite

View full text Add to dashboard Cite

show abstract

“…In the manufacturing industry, most relevant works involving computation pipelines have focused on constructing an autonomous framework to tune a specific computation pipeline or only a limited number of method options. Examples of such works include the application of computation pipelines for preventive maintenance operation (O'Donovan et al 2015), fault prognostics (Kozjek et al 2017), and production planning (Wang et al 2018b). While the aforementioned works are applicable to a specific contextualized computation task, they cannot be adequate in different tasks Fig.…”

Section: Concept Of Computation Pipelinesmentioning

confidence: 99%

Challenges of modeling and analysis in cybermanufacturing: a review from a machine learning and computation perspective

et al. 2021

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In Industry 4.0, smart manufacturing is facing its next stage, cybermanufacturing, founded upon advanced communication, computation, and control infrastructure. Cybermanufacturing will unleash the potential of multi-modal manufacturing data, and provide a new perspective called computation service, as a part of service-oriented architecture (SOA), where on-demand computation requests throughout manufacturing operations are seamlessly satisfied by data analytics and machine learning. However, the complexity of information technology infrastructure leads to fundamental challenges in modeling and analysis under cybermanufacturing, ranging from information-poor datasets to a lack of reproducibility of analytical studies. Nevertheless, existing reviews have focused on the overall architecture of cybermanufacturing/SOA or its technical components (e.g., communication protocol), rather than the potential bottleneck of computation service with respect to modeling and analysis. In this paper, we review the fundamental challenges with respect to modeling and analysis in cybermanufacturing. Then, we introduce the existing efforts in computation pipeline recommendation, which aims at identifying an optimal sequence of method options for data analytics/machine learning without time-consuming trial-and-error. We envision computation pipeline recommendation as a promising research field to address the fundamental challenges in cybermanufacturing. We also expect that computation pipeline recommendation can be a driving force to flexible and resilient manufacturing operations in the post-COVID-19 industry.

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“…The identification of correlation between process parameters of injection molding and final part quality, classified on visual inspection of the specimens and their weight, is shown in [59]. Another study in [60] identified common process parameters such as cavity pressure and switching point to holding pressure, which are then used to forecast unplanned machine downtime. Further research in [61] shows that varying process parameter settings, like switching point, can be reflected in cavity pressure profiles.…”

Section: Sensor Integration In Moldsmentioning

confidence: 99%

Tooling systems with integrated sensors enabling data based process optimization

Bleicher¹,

Ramsauer²,

Leonhartsberger³

et al. 2021

Journal of Machine Engineering

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Sensor integration into machining equipment has become an important factor for gaining deep process insights mainly driven by increasingly smaller and cheaper sensors and transmitters. Due to advances in microelectronics and communication technology, a broader field of applications in production processes and machine tools can be addressed using sensing devices and their implementation potentials. Ensuring a sensitive but robust data stream from close to the actual process allows not only reliable monitoring but also process and quality control based on sensor information. This paper provides an overview of the utilization of sensor data for the purpose of condition monitoring, model fitting and real-time control coping with stochastic effects. Examples of sensor integration in fields of injection molding, roll forming and heavy-duty milling comprise the state of the art of sensor implementation, data evaluation and possible feedback loops in the respective application scenarios.

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A Data-Driven Holistic Approach to Fault Prognostics in a Cyclic Manufacturing Process

Cited by 19 publications

References 9 publications

Advancing manufacturing systems with big-data analytics: A conceptual framework

Advancing manufacturing systems with big-data analytics: A conceptual framework

Challenges of modeling and analysis in cybermanufacturing: a review from a machine learning and computation perspective

Tooling systems with integrated sensors enabling data based process optimization

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