Intelligent machine learning based total productive maintenance approach for achieving zero downtime in industrial machinery

Mohan, T. Roosefert; Roselyn, J. Preetha; Uthra, R. Annie; Devaraj, D.; Umachandran, Krishnan

doi:10.1016/j.cie.2021.107267

Cited by 53 publications

(17 citation statements)

References 18 publications

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“…The current issue and full text archive of this journal is available on Emerald Insight at: https://www.emerald.com/insight/1355-2511.htm Total productive maintenance (TPM) is one of the maintenance approaches used in industries to reduce downtime (Mungani and Visser, 2013). Corrective maintenance (CM), time based maintenance (TBM), breakdown maintenance (BM), condition based maintenance (CBM), inspection and repair (IR) and maintenance prevention (MP) are the conventional maintenance techniques which are used to achieve zero downtime (Roosefert Mohan et al, 2021). Predictive maintenance can be achieved effectively using the Internet of things (IoT) (Ran et al, 2019).…”

Section: Industry 40 Transformation 763mentioning

confidence: 99%

“…In QM pillar activity, by maintaining the healthy status of these Q-components which is related to quality, the quality rate is increased. Though KK pillar and JH pillar activities like kaizens, putting tags, Hard To Access, Source of Contamination, Visual Controls, One Point Lessons (OPL) and CLIT as discussed in Roosefert Mohan et al (2021) have adhered 100%, it is observed that it is difficult to achieve zero downtime and improve OEE to an optimized level by implementing the conventional TPM. Also, to run a machine with zero breakdowns above 20 years of life and improve the OEE to an optimized level in industries are very essential and hence the PM pillar needs to be addressed along with AI and IoT which enhances the system performance by eliminating the human intervention, manual decision making and error in data capturing.…”

Section: Industry 40 Transformation 763mentioning

confidence: 99%

“…With the rapid growth in automobile consumption, the demand in the automobile manufacturing industry is growing rapidly (Gackowiec, 2019). Through Industry 3.0 to Industry 4.0 transformation, the breakdown of a machine is eliminated which leads to stoppage or reduction in speed of the machine or production, delay in delivery schedule, decrease in production, increase in operational cost and affect the product quality (Roosefert Mohan et al ., 2021). Any unplanned breakdown or functional deterioration of a machine may lead to stoppage or reduction in speed of machine or production, delay in delivery schedule, decrease in production, increase in operational cost, affect the product quality and also reduce earnings before interest, tax, depreciation and amortization EBITDA of an industry.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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: Industry 40 Transformation 763mentioning

confidence: 99%

Section: Industry 40 Transformation 763mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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“…Life cycle and equipment deterioration are analyzed. Agent system determines components' useful lives and provides condition-based monitoring (Mohan et al ., 2021). All concerns are explained by the variables.…”

Section: Literature Reviewmentioning

confidence: 99%

Analyzing the role of Multi-Agent Technology in minimizing breakdown probabilities in Manufacturing Industries

Singh,

Sharma,

Sharma

2023

JQME

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Purpose Every company or manufacturing system is vulnerable to breakdowns. This research aims to analyze the role of Multi-Agent Technology (MAT) in minimizing breakdown probabilities in Manufacturing Industries.Design/methodology/approach This study formulated a framework of six factors and twenty-eight variables (explored in the literature). A hybrid approach of Multi-Criteria Decision-Making Technique (MCDM) was employed in the framework to prioritize, rank and establish interrelationships between factors and variables grouped under them.Findings The research findings reveal that the “Manufacturing Process” is the most essential factor, while “Integration Manufacturing with Maintenance” is highly impactful on the other factors to eliminate the flaws that may cause system breakdown. The findings of this study also provide a ranking order for variables to increase the performance of factors that will assist manufacturers in reducing maintenance efforts and enhancing process efficiency.Practical implications The ranking order developed in this study may assist manufacturers in reducing maintenance efforts and enhancing process efficiency. From the manufacturer’s perspective, this research presented MAT as a key aspect in dealing with the complexity of manufacturing operations in manufacturing organizations. This research may assist industrial management with insights into how they can lower the probability of breakdown, which will decrease expenditures, boost productivity and enhance overall efficiency.Originality/value This study is an original contribution to advancing MAT’s theory and empirical applications in manufacturing organizations to decrease breakdown probability.

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Educational Programs’ Development in the Field of Software Systems for Designing and Control Cyber-Physical Systems Using Information Modeling Technologies

Dukhanov

Chistyakova

2022

Studies in Systems, Decision and Control

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Intelligent machine learning based total productive maintenance approach for achieving zero downtime in industrial machinery

Cited by 53 publications

References 18 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

Analyzing the role of Multi-Agent Technology in minimizing breakdown probabilities in Manufacturing Industries

Educational Programs’ Development in the Field of Software Systems for Designing and Control Cyber-Physical Systems Using Information Modeling Technologies

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