2019Inżynieria MineralnaOpen access

The Use of Predictive Maintenance in the Production Processes

Aneta Napieraj

Open full text 1 citations

Abstract

Failures are a problem for every company that causes the plant to stop working and thus incur losses. It is therefore obvious that companies want to eliminate unplanned downtime in the production process. In the wake of the still increasing demands in terms of productivity and safety requirements, cost reduction, the industry is forced to seek the optimum between economic requirements and an acceptable level of risk in terms of security. Modern factories equipped with computerized processes and extensive diagnostic tools often do not use all the information that is collected from the hardware level. It happens that some of the relationships between events are often overlooked or neglected. The article presents an approach to increasing machine reliability through predictive data analysis. The assumptions of the predictive and preventive maintenance methods are presented. The threats and possibilities offered by this methodology implemented in the production process are presented.

About this research paper

What this paper is about

Failures are a problem for every company that causes the plant to stop working and thus incur losses. It is therefore obvious that companies want to eliminate unplanned downtime in the production process. In the wake of the still increasing demands in terms of productivity and safety requirements, cost reduction, the industry is forced to seek the optimum between economic requirements and an acceptable level of risk in terms of security. Modern factories equipped with computerized processes and extensive diagnostic tools often do not use all the information that is collected from the hardware level. It happens that some of the relationships between events are often overlooked or neglected. The article presents an approach to increasing machine reliability through predictive data analysis. The assumptions of the predictive and preventive maintenance methods are presented. The threats and possibilities offered by this methodology implemented in the production process are presented.

Why it matters

OpenAlex reports 1 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

Failures are a problem for every company that causes the plant to stop working and thus incur losses. It is therefore obvious that companies want to eliminate unplanned downtime in the production process. In the wake of the still increasing demands in terms of productivity and safety requirements, cost reduction, the industry is forced to seek the optimum between economic requirements and an acceptable level of risk in terms of security. Modern factories equipped with computerized processes and extensive diagnostic tools often do not use all the information that is collected from the hardware level. It happens that some of the relationships between events are often overlooked or neglected. The article presents an approach to increasing machine reliability through predictive data analysis. The assumptions of the predictive and preventive maintenance methods are presented. The threats and possibilities offered by this methodology implemented in the production process are presented.

Key concepts: Downtime, Production (economics), Risk analysis (engineering), Productivity, Reliability (semiconductor), Process (computing), Predictive maintenance, Preventive maintenance

Related papers

Back to paper searchBrowse research topicsOriginal source
The Use of Predictive Maintenance in the Production Processes — Research Paper | ScholarLens