Congestion avoidance algorithm using ARIMA(2,1,1) model-based RTT estimation and RSS in heterogeneous wired-wireless networks

Jeyasekar, A; Raja, S. V. Kasmir; Uthra, R. Annie

doi:10.1016/j.jnca.2017.05.008

Cited by 8 publications

(1 citation statement)

References 39 publications

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“…By predicting the remaining useful life (Qin et al ., 2017) in advance, catastrophes can be eliminated to maximize machine uptime. The collected data of signature parameters related to breakdown are processed using Machine Learning (ML) models like Multi-Layer Perceptron (MLP), Autoregression Integrated Moving Average (ARIMA) model and Support Vector Regression (SVR) model (Santos Júnior et al ., 2019; Jeyasekar et al ., 2017).…”

Section: Introductionmentioning

confidence: 99%

LSTM based artificial intelligence predictive maintenance technique for availability rate and OEE improvement in a TPM implementing plant through Industry 4.0 transformation

Mohan

Roselyn

Uthra

2023

JQME

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PurposeThe artificial intelligence (AI) based total productive maintenance (TPM) condition based maintenance (CBM) approach through Industry 4.0 transformation can well predict the breakdown in advance to eliminate breakdown.Design/methodology/approachMeeting the customer requirement as per the delivery schedule with the existing resources are always a big challenge in industries. Any catastrophic breakdown in the equipment leads to increase in production loss, damage to machines, repair cost, time and affects delivery. If these breakdowns are predicted in advance, the breakdown can be addressed before its occurrence and the demand supply chain can be met. TPM is one of the essential operational excellence tool used in industries to utilize the existing resources of a plant in a optimal way. The conventional time based maintenance (TBM) and CBM approach of TPM in Industry 3.0 is time consuming and not accurate enough to achieve zero down time.FindingsThe proposed AI and IIoT based TPM is achieved in a digitalized data oriented platform to monitor and control the health status of the machine which may reduce the catastrophic breakdown by 95% and also improves the quality rate and machine performance rate. Based on the identified key signature parameters related to major breakdown are measured using the sensors, digitalised by programmable logic controller (PLC) and monitored by supervisory control and data acquisition (SCADA) and predicted in server or cloud.Originality/valueLong short term memory based deep learning network was developed as a regression forecasting model to predict the remaining useful life RUL of the part or assembly and based on the predictions, corrective action has been implemented before the occurrence of breakdown. The reliability and consistency of the proposed approach are validated and horizontally deployed in similar machines to achieve zero downtime.

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Section: Introductionmentioning

confidence: 99%

LSTM based artificial intelligence predictive maintenance technique for availability rate and OEE improvement in a TPM implementing plant through Industry 4.0 transformation

Mohan

Roselyn

Uthra

2023

JQME

View full text Add to dashboard Cite

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LSTM Based Predictive Maintenance Approach for Zero Breakdown in Foundry Line Through Industry 4.0

Mohan

Roselyn

Uthra

2023

Lecture Notes in Mechanical Engineering

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QoS monitoring in real-time streaming overlays based on lock-free data structures

Tommasi

Luca

Melle

2021

Multimed Tools Appl

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Peer-to-peer streaming is a well-known technology for the large-scale distribution of real-time audio/video contents. Delay requirements are very strict in interactive real-time scenarios (such as synchronous distance learning), where playback lag should be of the order of seconds. Playback continuity is another key aspect in these cases: in presence of peer churning and network congestion, a peer-to-peer overlay should quickly rearrange connections among receiving nodes to avoid freezing phenomena that may compromise audio/video understanding. For this reason, we designed a QoS monitoring algorithm that quickly detects broken or congested links: each receiving node is able to independently decide whether it should switch to a secondary sending node, called “fallback node”. The architecture takes advantage of a multithreaded design based on lock-free data structures, which improve the performance by avoiding synchronization among threads. We will show the good responsiveness of the proposed approach on machines with different computational capabilities: measured times prove both departures of nodes and QoS degradations are promptly detected and clients can quickly restore a stream reception. According to PSNR and SSIM, two well-known full-reference video quality metrics, QoE remains acceptable on receiving nodes of our resilient overlay also in presence of swap procedures.

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Congestion avoidance algorithm using ARIMA(2,1,1) model-based RTT estimation and RSS in heterogeneous wired-wireless networks

Cited by 8 publications

References 39 publications

LSTM based artificial intelligence predictive maintenance technique for availability rate and OEE improvement in a TPM implementing plant through Industry 4.0 transformation

LSTM based artificial intelligence predictive maintenance technique for availability rate and OEE improvement in a TPM implementing plant through Industry 4.0 transformation

LSTM Based Predictive Maintenance Approach for Zero Breakdown in Foundry Line Through Industry 4.0

QoS monitoring in real-time streaming overlays based on lock-free data structures

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