Towards Distributed IoT/Cloud based Fault Detection and Maintenance in Industrial Automation

Xenakis, Apostolos; Karageorgos, Anthony; Lallas, Efthimios; Chis, Adriana E.; González–Vélez, Horacio

doi:10.1016/j.procs.2019.04.091

Cited by 35 publications

(18 citation statements)

References 8 publications

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“…Similar to study conducted by Rohit Dhall et al [6] for an IoT-based predictive connected car maintenance approach; the total maintenance cost reduction resulted to 30%. In addition, other benefits were achieved such as multiple return of investment, elimination of breakdown, reduction in downtime and increase in production which is comparable to previous studies [7], [10], [18]- [21].…”

Section: Other Benefitssupporting

confidence: 78%

“…Facilities Management (FM) Industry in Saudi Arabia has not yet adapted to predictive maintenance associated with IoT Technology despite of various benefits that may be achieved such as real-time asset condition monitoring, energy efficient monitoring and control without human intervention, to analyze and process machine faults in real-time, and minimization of total operational costs [7]. However, various factors are to be considered before commencing the implementation like IoT Sensor, Data, Centralized Data Processing Platform, Cloud Servers, Network, Software, Mobile Application and Information Visualization [7]- [9]. Information Visualization specifically Predictive Analytics and Prescriptive Analytics are the key features of these concepts enabling for the maintenance group to ease the decision making in providing solution to possible asset failure and to avoid its repetition [11]- [13].…”

Section: Introductionmentioning

confidence: 99%

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Automation of Predictive Maintenance Using Internet of Things (IoT) Technology at University-Based O&M Project

Fernandez¹,

Prasetyo²,

Persada³

et al. 2021

IJIET

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Predictive Maintenance can be defined as a type of advanced maintenance that detects the onset of system degradation allowing causal stressors to be eliminated or controlled prior to any significant deterioration in component physical state. Thru Internet of Things (IoT) Technology, automation, and implementation of Predictive Maintenance are possible. The purpose of this study is to propose the implementation of Predictive Maintenance using IpT Technology at University-based Operation & Maintenance Project that aims to transform the current Key Performance Indicator (KPI) of PM to CM Ratio from 80:20 to 90:10. Six Sigma DMAIC Methodology and Data-Driven Predictive Maintenance Planning Framework were utilized as the methodology of this research. Research’s results show that KPI, 90:10 (PM to CM Ratio) is achievable and maintenance cost can significantly reduce from 25% to 30%. Other valuable benefits are return of investment (10X), elimination of breakdown (70 - 75%), reduction in downtime (35% - 45%) and increase of production (20% - 25%). The proposed concept can be utilized in other industries to achieve high customer satisfaction percentages, sustainable operations, fault prediction, and online monitoring using PC or mobile applications.

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Section: Other Benefitssupporting

confidence: 78%

Section: Introductionmentioning

confidence: 99%

Automation of Predictive Maintenance Using Internet of Things (IoT) Technology at University-Based O&M Project

Fernandez¹,

Prasetyo²,

Persada³

et al. 2021

IJIET

View full text Add to dashboard Cite

show abstract

“…A system for machine tools based on fog computing, claimed to improve autonomy and collaboration using data available, is presented in [20]. In [21] an approach is proposed where data acquisition sensors are distributed across machines, and feature extraction and health condition classification is on fog nodes. Maintenance service architectures for CPS making use of data have been presented in [22]- [24].…”

Section: Related Workmentioning

confidence: 99%

Dynamic Edge and Cloud Service Integration for Industrial IoT and Production Monitoring Applications of Industrial Cyber-Physical Systems

Hästbacka

Halme

Barna

et al. 2022

IEEE Trans. Ind. Inf.

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Industrial cyber-physical systems rely increasingly on data from IoT devices and other systems as continuously emerging use cases implement new intelligent features. Edge computing can be seen as an extension of the cloud in close physical proximity, in which some of the typical cloud computing loads are beneficial to run. This paper studies data analytics application development for integration of industrial IoT data and composition of application services executed on edge and cloud. A solution is designed to support heterogeneous hardware and run-time platforms, and focuses on the service layer that enables flexible orchestration of data flows and dynamic service compositions. The unified model and system architecture implemented, using the open Arrowhead Framework model, is verified through two representative industrial use cases.

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“…Deep learning analysis is performed to detect faults in the manufacturing process. As another example, Xenakis et al [47] proposed a fault detection method for an IoT environment. The method is introduced for industrial automation.…”

Section: Related Workmentioning

confidence: 99%

Formal Verification of a Hybrid Machine Learning-Based Fault Prediction Model in Internet of Things Applications

et al. 2020

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By increasing the complexity of the Internet of Things (IoT) applications, fault prediction become an important challenge in interactions between human, and smart devices. Fault prediction is one of the key factors to achieve better arranging the IoT applications. Most of the current research studies evaluated the fault prediction methods using simulation environments. However, formal verification of the correctness of a fault prediction method has not been reported yet. This paper presents a behavioral modeling and formal verification of a hybrid machine learning-based fault prediction model with Multi-Layer Perceptron (MLP) and Particle Swarm Optimization (PSO) algorithms. In particular, the PSO is used for feature selection. Then, the fault prediction is considered as a behavior to be verified formally. The fault prediction behavior is divided into two types of behaviors: dimension reduction behavior and prediction behavior. For each of the behaviors, one formal model is designed. The behavioral models designed are mapped into the Labeled Transition System (LTS). The Process Analysis Toolkit (PAT) model checker is employed to evaluate the behavioral models. The accuracy of the fault prediction method is done by some existing specifications such as deadlock-free and reachability properties in terms of linear temporal logic formulas. Also, the verification of the fault prediction behaviors is used to detect the defect metrics of information-centric IoT applications. Experimental results showed that our proposed verification method has minimum verification time and memory usage for evaluating critical specification rules than other research studies. INDEX TERMS Internet of Things applications, fault prediction, formal verification, process analysis toolkit, multi-layer perceptron, particle swarm optimization.

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Towards Distributed IoT/Cloud based Fault Detection and Maintenance in Industrial Automation

Cited by 35 publications

References 8 publications

Automation of Predictive Maintenance Using Internet of Things (IoT) Technology at University-Based O&M Project

Automation of Predictive Maintenance Using Internet of Things (IoT) Technology at University-Based O&M Project

Dynamic Edge and Cloud Service Integration for Industrial IoT and Production Monitoring Applications of Industrial Cyber-Physical Systems

Formal Verification of a Hybrid Machine Learning-Based Fault Prediction Model in Internet of Things Applications

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