Preventive Maintenance System in a Company from the Printing Industry

Szczepaniak, Marta; Trojanowska, Justyna

doi:10.1007/978-3-030-22365-6_35

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…These holistic views of AM emphasize the importance of PAM rather than just asset maintenance [1]. However, maintenance plays a key role in PAM and is also a principal actor within Industry 4.0 [15,46] and a contributor to operational performance [47,48].…”

Section: Background Literature and Hypothesis Developmentmentioning

confidence: 99%

An Analysis of Physical Asset Management Core Practices and Their Influence on Operational Performance

2020

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The commitment of organisations to physical assets management (PAM) has recently received considerable attention in theory and practice. Indeed, PAM plays a key role in asset-intensive organizations and is also considered as a principal actor within Industry 4.0. Therefore, this paper aims to examine the PAM core practices and the performance implications of integrating these practices into business, in particular by assessing their impact on operational performance. Survey data were collected from managers in 138 international and local organisations. The data was analysed using Partial Least Squares Path Modeling (PLS-PM). The study validates the second-order construct consisting of PAM latent variables, namely Strategy and Planning, Risk management, Lifecycle Delivery, Asset Information, and Asset Review. The results have shown that PAM core practices directly influence operational performance. This paper is a response to recent calls for empirical studies on the organisational approaches that can increase the success of organisations.

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Section: Background Literature and Hypothesis Developmentmentioning

confidence: 99%

An Analysis of Physical Asset Management Core Practices and Their Influence on Operational Performance

2020

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“…The concept of Predictive Maintenance [1][2][3][4] foresees the carrying out of maintenance activities before the equipment failure. The primary goal of predictive maintenance is to reduce the frequency of equipment failures by preventing the failure before it actually occurs [5]. This strategy helps to minimize breakdown costs and downtime (loss of production) and increase product quality, well known thanks to [6] and recently reiterated by [4].…”

Section: Introductionmentioning

confidence: 99%

Text Mining for Industrial Machine Predictive Maintenance with Multiple Data Sources

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2021

Advances in Dynamical Systems Theory, Models, Algorithms and Applications

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This paper presents an innovative methodology, from which an efficient system prototype is derived, for the algorithmic prediction of malfunctions of a generic industrial machine tool. It integrates physical devices and machinery with Text Mining technologies and allows the identification of anomalous behaviors, even of minimal entity, rarely perceived by other strategies in a machine tool. The system works without waiting for the end of the shift or the planned stop of the machine. Operationally, the system analyzes the log messages emitted by multiple data sources associated with a machine tool (such as different types of sensors and log files produced by part programs running on CNC or PLC) and deduces whether they can be inferred from them future machine malfunctions. In a preliminary offline phase, the system associates an alert level with each message and stores it in a data structure. At runtime, three algorithms guide the system: pre-processing, matching and analysis: Preprocessing, performed only once, builds the data structure; Matching, in which the system issues the alert level associated with the message; Analysis, which identifies possible future criticalities. It can also analyze an entire historical series of stored messages The algorithms have a linear execution time and are independent of the size of the data structure, which does not need to be sorted and therefore can be updated without any computational effort.

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