Predictive Maintenance in Forging Industry

Prasad, G. K. Arun; Panse, Chetan

doi:10.1109/iciptm54933.2022.9754058

Cited by 2 publications

(1 citation statement)

References 7 publications

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“…New Machine Learning (ML) methods are thus under increasing development because of their relevant role in Big Data Analytics (BDA), particularly in achieving improved production and equipment productivity (Chan et al ., 2005). In this context, since Overall Equipment Effectiveness (OEE) is considered a standard for measuring the productivity of equipment as a function of its efficiency (Arun Prasad and Panse, 2022), studying the equipment through ML based on the evaluation of OEE can be beneficial for manufacturing process improvement.…”

Section: Introductionmentioning

confidence: 99%

A rule-based machine learning methodology for the proactive improvement of OEE: a real case study

Lucantoni,

Antomarioni,

Ciarapica

et al. 2023

IJQRM

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PurposeThe Overall Equipment Effectiveness (OEE) is considered a standard for measuring equipment productivity in terms of efficiency. Still, Artificial Intelligence solutions are rarely used for analyzing OEE results and identifying corrective actions. Therefore, the approach proposed in this paper aims to provide a new rule-based Machine Learning (ML) framework for OEE enhancement and the selection of improvement actions.Design/methodology/approachAssociation Rules (ARs) are used as a rule-based ML method for extracting knowledge from huge data. First, the dominant loss class is identified and traditional methodologies are used with ARs for anomaly classification and prioritization. Once selected priority anomalies, a detailed analysis is conducted to investigate their influence on the OEE loss factors using ARs and Network Analysis (NA). Then, a Deming Cycle is used as a roadmap for applying the proposed methodology, testing and implementing proactive actions by monitoring the OEE variation.FindingsThe method proposed in this work has also been tested in an automotive company for framework validation and impact measuring. In particular, results highlighted that the rule-based ML methodology for OEE improvement addressed seven anomalies within a year through appropriate proactive actions: on average, each action has ensured an OEE gain of 5.4%.Originality/valueThe originality is related to the dual application of association rules in two different ways for extracting knowledge from the overall OEE. In particular, the co-occurrences of priority anomalies and their impact on asset Availability, Performance and Quality are investigated.

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Section: Introductionmentioning

confidence: 99%