A Global Manufacturing Big Data Ecosystem for Fault Detection in Predictive Maintenance

Yu, Wenjin; Dillon, Tharam S.; Mostafa, Fahed; Rahayu, Wenny; Liu, Yuehua

doi:10.1109/tii.2019.2915846

Cited by 153 publications

(77 citation statements)

References 26 publications

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“…Currently, there exist few works presenting unsupervised learning solutions for predictive maintenance. In Reference [71], a complex system is given, performing IoT data collection, storage, processing and visualization, prior to predictive modelling. This system provides structured and unstructured data ingestion from multiple sources, as well as data management via data lakes, based on file and data base management systems, such as Hadoop and Apache Hive, among others.…”

Section: Unsupervised Learning-based Solutionsmentioning

confidence: 99%

Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects

Angelopoulos

Michailidis

Νομικός

et al. 2019

Sensors

216

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The recent advancements in the fields of artificial intelligence (AI) and machine learning (ML) have affected several research fields, leading to improvements that could not have been possible with conventional optimization techniques. Among the sectors where AI/ML enables a plethora of opportunities, industrial manufacturing can expect significant gains from the increased process automation. At the same time, the introduction of the Industrial Internet of Things (IIoT), providing improved wireless connectivity for real-time manufacturing data collection and processing, has resulted in the culmination of the fourth industrial revolution, also known as Industry 4.0. In this survey, we focus on the vital processes of fault detection, prediction and prevention in Industry 4.0 and present recent developments in ML-based solutions. We start by examining various proposed cloud/fog/edge architectures, highlighting their importance for acquiring manufacturing data in order to train the ML algorithms. In addition, as faults might also occur from sources beyond machine degradation, the potential of ML in safeguarding cyber-security is thoroughly discussed. Moreover, a major concern in the Industry 4.0 ecosystem is the role of human operators and workers. Towards this end, a detailed overview of ML-based human–machine interaction techniques is provided, allowing humans to be in-the-loop of the manufacturing processes in a symbiotic manner with minimal errors. Finally, open issues in these relevant fields are given, stimulating further research.

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Section: Unsupervised Learning-based Solutionsmentioning

confidence: 99%

Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects

Angelopoulos

Michailidis

Νομικός

et al. 2019

Sensors

216

View full text Add to dashboard Cite

show abstract

“…The proposed platform design in accordance with RAMI 4.0' layers, which consists basically of creating a noncentralized network for manufacturing systems, with the implementation of communication among the equipment and devices that compose the system, a data lake for data storage [53], a machine learning framework, and decentralized data processing. The tasks must be built in the instance phase of the RAMI 4.0 life cycle-axis, more specifically at the production stage.…”

Section: Platform Design and Trendsmentioning

confidence: 99%

Cooperative Robotics and Machine Learning for Smart Manufacturing: Platform Design and Trends Within the Context of Industrial Internet of Things

Lins

Givigi

2021

IEEE Access

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Internet of Things (IoT) in industrial settings now leads to the development of a new generation of systems designed to improve the operational efficiency of the new paradigm of smart manufacturing plants. Thereby, the current article introduces in detail the definitions, concepts, standards, and other important aspects related to smart manufacturing, cooperative robotics, and Machine Learning techniques (ML). The paper highlights the opportunities presented by the new paradigm and the challenges faced in effectively implementing it in the industrial context. Especially, the focus is on the challenges associated with the architectures, communications technology, and protocols that enable the integration and deployment of machine learning algorithms to improve the execution of tasks daily performed by the collaboration of human operators, machines, and robots. Finally, it also provides a systematic review of state-of-the-art research efforts for the fields aforementioned, and a designed platform for integrating collaborative robotics and machine learning established on six layers and four hierarchies of the RAMI 4.0 (Reference Architectural Model Industry 4.0) is presented.

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“…Imani et al [25] adopted RF based on Spark to rapidly diagnose wind turbine gearbox faults. Yu et al [26] built a fault diagnosis platform of industrial equipment using MapR-DB, Hive, MapReduce, Spark, principal component analysis, and other technologies. Most of the existing researches on fault diagnosis based on big data technology apply the parallel machine learning algorithms based on MapReduce or Spark to fault diagnosis, which improve the performance of fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

A Novel Bearing Fault Diagnosis Method Using Spark-Based Parallel ACO-K-Means Clustering Algorithm

Wan

Zhang

et al. 2021

IEEE Access

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K-Means clustering algorithm is a typical unsupervised learning method, and it has been widely used in the field of fault diagnosis. However, the traditional serial K-Means clustering algorithm is difficult to efficiently and accurately perform clustering analysis on the massive running-state monitoring data of rolling bearing. Therefore, a novel fault diagnosis method of rolling bearing using Spark-based parallel ant colony optimization (ACO)-K-Means clustering algorithm is proposed. Firstly, a Spark-based three-layer wavelet packet decomposition approach is developed to efficiently preprocess the running-state monitoring data to obtain eigenvectors, which are stored in Hadoop Distributed File System (HDFS) and served as the input of ACO-K-Means clustering algorithm. Secondly, ACO-K-Means clustering algorithm suitable for rolling bearing fault diagnosis is proposed to improve the diagnosis accuracy. ACO algorithm is adopted to obtain the global optimal initial clustering centers of K-Means from all eigenvectors, and the K-Means clustering algorithm based on weighted Euclidean distance is used to perform clustering analysis on all eigenvectors to obtain a rolling bearing fault diagnosis model. Thirdly, the efficient parallelization of ACO-K-Means clustering algorithm is implemented on a Spark platform, which can make full use of the computing resources of a cluster to efficiently process large-scale rolling bearing datasets in parallel. Extensive experiments are conducted to verify the effectiveness of the proposed fault diagnosis method. Experimental results show that the proposed method can not only achieve good fault diagnosis accuracy but also provide high model training efficiency and fault diagnosis efficiency in a big data environment.

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A Global Manufacturing Big Data Ecosystem for Fault Detection in Predictive Maintenance

Cited by 153 publications

References 26 publications

Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects

Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects

Cooperative Robotics and Machine Learning for Smart Manufacturing: Platform Design and Trends Within the Context of Industrial Internet of Things

A Novel Bearing Fault Diagnosis Method Using Spark-Based Parallel ACO-K-Means Clustering Algorithm

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