Streaming analytics processing in manufacturing performance monitoring and prediction

Wu, Yi-Hsin; Wang, Sheng‐De; Chen, Li-Jung; Yu, Cheng-Juei

doi:10.1109/bigdata.2017.8258312

Cited by 8 publications

(3 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the product domain, the costs of 3D printed products can be predicted with the proposed big-data based solution and three machine learning algorithms [55]. In the production domain, machine learning is used for the prediction of product performance degradation [63], cycle times for production planning [62], energy consumption and KPI values on MES system [25], and production efficiency [162]. A number of papers focus on the prediction of product quality with different machine learning algorithms such as Bayesian Network [88] and statistic analytics [87].…”

Section: Driver 3: Predictionmentioning

confidence: 99%

Manufacturing big data ecosystem: A systematic literature review

Cui

Kara

Chan

2020

Robotics and Computer-Integrated Manufacturing

255

View full text Add to dashboard Cite

Advanced manufacturing is one of the core national strategies in the US (AMP), Germany (Industry 4.0) and China (Made-in China 2025). The emergence of the concept of Cyber Physical System (CPS) and big data imperatively enable manufacturing to become smarter and more competitive among nations. Many researchers have proposed new solutions with big data enabling tools for manufacturing applications in three directions: product, production and business.Big data has been a fast-changing research area with many new opportunities for applications in manufacturing. This paper presents a systematic literature review of the state-of-the-art of big data in manufacturing. Six key drivers of big data applications in manufacturing have been identified. The key drivers are system integration, data, prediction, sustainability, resource sharing and hardware.Based on the requirements of manufacturing, nine essential components of big data ecosystem are captured. They are data ingestion, storage, computing, analytics, visualization, management, workflow, infrastructure and security.Several research domains are identified that are driven by available capabilities of big data ecosystem. Five future directions of big data applications in manufacturing are presented from modelling and simulation to realtime big data analytics and cybersecurity.

show abstract

Section: Driver 3: Predictionmentioning

confidence: 99%

Manufacturing big data ecosystem: A systematic literature review

Cui

Kara

Chan

2020

Robotics and Computer-Integrated Manufacturing

255

View full text Add to dashboard Cite

show abstract

“…Other cited publications point out that sensors do not only serve the elicitation of machine data, but also of production environment data [9], [10]. This becomes relevant in case environmental conditions influence production efficiency.…”

Section: Data Sourcementioning

confidence: 99%

Requirements Identification for Real-Time Anomaly Detection in Industrie 4.0 Machine Groups: A Structured Literature Review

Stahmann

Rieger

2021

Proceedings of the Annual Hawaii International Conference on System Sciences

View full text Add to dashboard Cite

Industrie 4.0 environments generate an unprecedented amount of production data. This is due to the rising number of sensors and interconnected devices capable of emitting data in millisecond frequencies. Streaming analytics offers promising methodologies that can support handling and analysis of data volume and variety. Transparency and control over real-time data can increase production efficiency in tightly connected machine environments. Data transparency may avoid time-consuming assessment of machines to detect anomalous machine behavior causing production inefficiencies or failures. This paper aims to identify requirements to implement streaming analytics for the detection of anomalies in Industrie 4.0 production machine groups through a structured literature review.

show abstract

“…Literature review also shows that KPIs are generated mainly based on specific type of industries and only few KPI sets exists based on manufacturing shop floor operations [41]- [45]. Industrial norms for selecting, composing, defining and identifying a required set of KPIs for manufacturing shop floor operations is lacking.…”

Section: Literature Reviewmentioning

confidence: 99%

Literature Survey about Elements of Manufacturing Shop Floor Operation Key Performance Indicators

Khan

Ahmad

2019

2019 5th International Conference on Control, Automation and Robotics (ICCAR)

View full text Add to dashboard Cite

In the era of globalisation, manufacturing industries are compelled to continuously monitor their manufacturing operations to maintain competitiveness. As a result, manufacturers have integrated several measurement models to inspect their manufacturing operations. These models comprise of a set of Key Performance Indicators (KPIs), which are capable to enumerate the effectiveness, competence, efficiency and proficiency of manufacturing operations. This paper presents a review of manufacturing shop floor operation KPIs that has been studied in the recent literature. Based on the reviewed literature author proposes various KPI elements such as: description, category, scope, formula, unit of measure, range, trend, mode of display, viewers and manufacturing approach. These elements can help manufacturers to better describe, classify, analyse and measure the appropriate KPIs for their shop floor operations. Thus, enabling manufacturers to accomplish and uphold great quality, increased productivity and throughput.

show abstract

Streaming analytics processing in manufacturing performance monitoring and prediction

Cited by 8 publications

References 2 publications

Manufacturing big data ecosystem: A systematic literature review

Manufacturing big data ecosystem: A systematic literature review

Requirements Identification for Real-Time Anomaly Detection in Industrie 4.0 Machine Groups: A Structured Literature Review

Literature Survey about Elements of Manufacturing Shop Floor Operation Key Performance Indicators

Contact Info

Product

Resources

About