Experimental Setup for Online Fault Diagnosis of Induction Machines via Promising IoT and Machine Learning: Towards Industry 4.0 Empowerment

Tran, Minh-Quang; Elsisi, Mahmoud; Mahmoud, Karar; Liu, Mengkun; Lehtonen, Matti; Darwish, Mohamed M. F.

doi:10.1109/access.2021.3105297

Cited by 86 publications

(35 citation statements)

References 45 publications

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“…In the manufacturing sector, the latest wave of internet technology such as cloud, IoT, big data, robotics, and cyber–physical systems has allowed the manufacturing industry to generate a vast array of business data that will bring new challenges, particularly cybersecurity [ 79 , 119 , 120 , 121 ]. These challenges are discussed in Table 5 [ 49 , 122 , 123 ].…”

Section: Research Challenges Opportunities Scope Of Future Work and Implication For Practitionersmentioning

confidence: 99%

Performance Measurement System and Quality Management in Data-Driven Industry 4.0: A Review

Tambare¹,

Meshram²,

Lee

et al. 2021

Sensors

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The birth of mass production started in the early 1900s. The manufacturing industries were transformed from mechanization to digitalization with the help of Information and Communication Technology (ICT). Now, the advancement of ICT and the Internet of Things has enabled smart manufacturing or Industry 4.0. Industry 4.0 refers to the various technologies that are transforming the way we work in manufacturing industries such as Internet of Things, cloud, big data, AI, robotics, blockchain, autonomous vehicles, enterprise software, etc. Additionally, the Industry 4.0 concept refers to new production patterns involving new technologies, manufacturing factors, and workforce organization. It changes the production process and creates a highly efficient production system that reduces production costs and improves product quality. The concept of Industry 4.0 is relatively new; there is high uncertainty, lack of knowledge and limited publication about the performance measurement and quality management with respect to Industry 4.0. Conversely, manufacturing companies are still struggling to understand the variety of Industry 4.0 technologies. Industrial standards are used to measure performance and manage the quality of the product and services. In order to fill this gap, our study focuses on how the manufacturing industries use different industrial standards to measure performance and manage the quality of the product and services. This paper reviews the current methods, industrial standards, key performance indicators (KPIs) used for performance measurement systems in data-driven Industry 4.0, and the case studies to understand how smart manufacturing companies are taking advantage of Industry 4.0. Furthermore, this article discusses the digitalization of quality called Quality 4.0, research challenges and opportunities in data-driven Industry 4.0 are discussed.

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Section: Research Challenges Opportunities Scope Of Future Work and Implication For Practitionersmentioning

confidence: 99%

Performance Measurement System and Quality Management in Data-Driven Industry 4.0: A Review

Tambare¹,

Meshram²,

Lee

et al. 2021

Sensors

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“…The data driven diagnosis method is widely used in the field of PEMFC system fault diagnosis because of its fast speed and comprehensive diagnosis [6]. In recent years, with the advent of the era of big data, deep learning technology continues to break through, and a growing number of researchers use data-driven methods for fuel cell fault diagnosis [7,8]. In order to solve the adverse effects and the interference of unreliable sensors on the diagnosis performance, refs.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis for PEMFC Water Management Subsystem Based on Learning Vector Quantization Neural Network and Kernel Principal Component Analysis

Jiang

Gan

et al. 2021

WEVJ

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To solve the problem of water management subsystem fault diagnosis in a proton exchange membrane fuel cell (PEMFC) system, a novel approach based on learning vector quantization neural network (LVQNN) and kernel principal component analysis (KPCA) is proposed. In the proposed approach, the KPCA method is used for processing strongly coupled fault data with a high dimension to reduce the data dimension and to extract new low-dimensional fault feature data. The LVQNN method is used to carry out fault recognition using the fault feature data. The effectiveness of the proposed fault detection method is validated using the experimental data of the PEMFC power system. Results show that the proposed method can quickly and accurately diagnose the three health states: normal state, water flooding failure and membrane dry failure, and the recognition accuracy can reach 96.93%. Therefore, the method proposed in this paper is suitable for processing the fault data with a high dimension and abundant quantities, and provides a reference for the application of water management subsystem fault diagnosis of PEMFC.

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“…Dashboard applications, providing the output of a machine learning detector in a predictive maintenance setting are still in its initial stage. Tran et al describes such an application to show and derive faults within an IoT use case [6]. While their focus is mainly on the detection of possible cyber attacks on the IoT system, the underlying system only evaluates the detection rate of the deployed machine learning model.…”

Section: Related Workmentioning

confidence: 99%

Event-Driven Dashboarding and Feedback for Improved Event Detection in Predictive Maintenance Applications

et al. 2021

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Manufacturers can plan predictive maintenance by remotely monitoring their assets. However, to extract the necessary insights from monitoring data, they often lack sufficiently large datasets that are labeled by human experts. We suggest combining knowledge-driven and unsupervised data-driven approaches to tackle this issue. Additionally, we present a dynamic dashboard that automatically visualizes detected events using semantic reasoning, assisting experts in the revision and correction of event labels. Captured label corrections are immediately fed back to the adaptive event detectors, improving their performance. To the best of our knowledge, we are the first to demonstrate the synergy of knowledge-driven detectors, data-driven detectors and automatic dashboards capturing feedback. This synergy allows a transition from detecting only unlabeled events, such as anomalies, at the start to detecting labeled events, such as faults, with meaningful descriptions. We demonstrate all work using a ventilation unit monitoring use case. This approach enables manufacturers to collect labeled data for refining event classification techniques with reduced human labeling effort.

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Experimental Setup for Online Fault Diagnosis of Induction Machines via Promising IoT and Machine Learning: Towards Industry 4.0 Empowerment

Cited by 86 publications

References 45 publications

Performance Measurement System and Quality Management in Data-Driven Industry 4.0: A Review

Performance Measurement System and Quality Management in Data-Driven Industry 4.0: A Review

Fault Diagnosis for PEMFC Water Management Subsystem Based on Learning Vector Quantization Neural Network and Kernel Principal Component Analysis

Event-Driven Dashboarding and Feedback for Improved Event Detection in Predictive Maintenance Applications

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