A Manufacturing Big Data Solution for Active Preventive Maintenance

By Jiafu Wan, Shenglong Tang, Di Li, Shiyong Wang, Chengliang Liu, Haider Abbas, Athanasios V. Vasilakos. IEEE Transactions on Industrial Informatics, 2017

365 citations recorded by the source corpus.

Subfields: Industrial and Manufacturing Engineering, Management Information Systems

View Paper: https://www.semanticscholar.org/paper/8566f36d7917d4bd5705e1f7dff39197e2d22817

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Industrial and Manufacturing Engineering

1. Jiewu Leng
2. Qiang Liu
3. Ding Zhang
4. Xin Chen
5. Hao Zhang
6. Marco Perona
7. Andrea Bacchetti
8. Ting Zheng

In Management Information Systems

1. Min Chen
2. Marco Perona
3. Andrea Bacchetti
4. Ting Zheng
5. Marco Ardolino

Citations in the corpus

• Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0 (2020, background): “These advantages also have a tremendous and strong bond with the procedures of maintenance [1,8–11].”
• The applications of Industry 4.0 technologies in manufacturing context: a systematic literature review (2020, methodology): “These data are transmitted through IoT in order to develop smart solutions for condition-based maintenance (Fumagalli et al. 2019; Wan et al. 2017).”
• A Digital Twin-Based Approach for Designing and Multi-Objective Optimization of Hollow Glass Production Line (2017, methodology): “0 vision [6], [7] and the development of big data analytics [8], [9], faster algorithms, increased computation power, and amount of available data enable the simulation with ability of real-time control and optimization of products and production lines, which is referred to as a Digital Twin [10], u”
• The Role of Big Data Analytics in Industrial Internet of Things (2019, neutral): “Researchers have integrated Hadoop and Storm technologies for big data processing and used neural network-based methods for prediction [78].”
• Data-Driven Methods for Predictive Maintenance of Industrial Equipment: A Survey (2019, background): “The second method refers to the failures that can be prevented, achieved by regular replacement of components, but leading to additional operating costs and an increased unexploited lifetime [24].”
• Smart Factory of Industry 4.0: Key Technologies, Application Case, and Challenges (2018, background): “[88] proposed a manufacturing big data solution for active preventive maintenance in manufacturing environment, which combines a real-time active maintenance mechanism with an off-line prediction method.”
• Digital Twin and Big Data Towards Smart Manufacturing and Industry 4.0: 360 Degree Comparison (2018, neutral): “, for active preventive MRO [27].”
• Deep Learning for Smart Industry: Efficient Manufacture Inspection System With Fog Computing (2018, background): “0, or in other words, the smart industry, is a word representing the current trend of manufacturing revolution [1].”
• Disease Prediction by Machine Learning Over Big Data From Healthcare Communities (2017, methodology): “For unstructured data, for example, using convolutional neural network (CNN) to extract text characteristics automatically has already attracted wide attention and also achieved very good results [20], [21] .”