Machine Learning Framework for Predictive Maintenance in Milling

Traini, Emiliano; Bruno, Giulia; D’Antonio, Gianluca; Lombardi, Franco

doi:10.1016/j.ifacol.2019.11.172

Cited by 76 publications

(38 citation statements)

References 5 publications

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“…Spendla et al [52], Dong et al [19], Fang et al [20], and Kaiser et al [71] present the predictive maintenance of machines using sensors degradation data for calculating the time to failure of various machines. Traini et al [49], Zhang et al [50], and He et al [53] worked on predictive maintenance analytics by considering recent past data to eliminate prospective failures and also to improve the mission dependability of production systems.…”

Section: Discussion and Future Research Agendamentioning

confidence: 99%

“…Literature review on predictive maintenance related to flexible unit systems. [49] A general framework has been developed and that has been applied to manufacturing tools by using predictive maintenance. [50] Conducts a study of the predictive maintenance on industrial equipment.…”

Section: Upgradationmentioning

confidence: 99%

“…Dong et al [19] have attempted to work on a flexible structure of a versatile manufacturing system to satisfy different needs and item varieties and to build up a PHM structure for assembling with different online sensors and flexible structures utilizing different sensors-based degradation data for registering and predicting each machine's time to failure. For example, Traini et al [49] discussed the execution of predictive maintenance of milling cutting tool information, and the collection as validation of a structure, and [56] and [57] presented a model-driven approach using embedded artificial intelligence strategies by the development and implementation of a quality monitoring framework, and also presented a sensory system for high precision monitoring, applicable to all machining and milling operations on conductive materials. Kevin et al [58] proposed a sensory updated degradation based predictive maintenance strategy.…”

Section: Predictive Maintenancementioning

confidence: 99%

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Investigation of Degradation and Upgradation Models for Flexible Unit Systems: A Systematic Literature Review

et al. 2021

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Research on flexible unit systems (FUS) with the context of descriptive, predictive, and prescriptive analysis have remarkably progressed in recent times, being now reinforced in the current Industry 4.0 era with the increased focus on integration of distributed and digitalized systems. In the existing literature, most of the work focused on the individual contributions of the above mentioned three analyses. Moreover, the current literature is unclear with respect to the integration of degradation and upgradation models for FUS. In this paper, a systematic literature review on degradation, residual life distribution, workload adjustment strategy, upgradation, and predictive maintenance as major performance measures to investigate the performance of the FUS has been considered. In order to identify the key issues and research gaps in the existing literature, the 59 most relevant papers from 2009 to 2020 have been sorted and analyzed. Finally, we identify promising research opportunities that could expand the scope and depth of FUS.

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Section: Discussion and Future Research Agendamentioning

confidence: 99%

Section: Upgradationmentioning

confidence: 99%

Section: Predictive Maintenancementioning

confidence: 99%

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Investigation of Degradation and Upgradation Models for Flexible Unit Systems: A Systematic Literature Review

et al. 2021

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“…While most publications aim to test the predictive power of data-driven models, very few enumerate all the steps taken required to implement PHM methods in manufacturing. Traini et al [ 63 ] developed a framework to address predictive maintenance in milling based on a generalized methodology. Yaguo et al reviewed the stages in CBM, from data acquisition to RUL estimation, for different PHM datasets.…”

Section: Research Gap and Proposalmentioning

confidence: 99%

Smart Prognostics and Health Management (SPHM) in Smart Manufacturing: An Interoperable Framework

Sundaram

Zeid

2021

Sensors

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Advances in the manufacturing industry have led to modern approaches such as Industry 4.0, Cyber-Physical Systems, Smart Manufacturing (SM) and Digital Twins. The traditional manufacturing architecture that consisted of hierarchical layers has evolved into a hierarchy-free network in which all the areas of a manufacturing enterprise are interconnected. The field devices on the shop floor generate large amounts of data that can be useful for maintenance planning. Prognostics and Health Management (PHM) approaches use this data and help us in fault detection and Remaining Useful Life (RUL) estimation. Although there is a significant amount of research primarily focused on tool wear prediction and Condition-Based Monitoring (CBM), there is not much importance given to the multiple facets of PHM. This paper conducts a review of PHM approaches, the current research trends and proposes a three-phased interoperable framework to implement Smart Prognostics and Health Management (SPHM). The uniqueness of SPHM lies in its framework, which makes it applicable to any manufacturing operation across the industry. The framework consists of three phases: Phase 1 consists of the shopfloor setup and data acquisition steps, Phase 2 describes steps to prepare and analyze the data and Phase 3 consists of modeling, predictions and deployment. The first two phases of SPHM are addressed in detail and an overview is provided for the third phase, which is a part of ongoing research. As a use-case, the first two phases of the SPHM framework are applied to data from a milling machine operation.

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“…Their framework copes only with (1), but they identified that one of the biggest challenges consists in describing the shop floor equipment and corresponding condition indicators in a uniform manner, hence (2). Traini et al [20] presented a basic framework for PdM of a generic manufacturing tool but it is based on Machine Learning (ML) techniques only, and does not consider the flexibility required for a smart factory. In [21] a framework for a flexible maintenance platform is proposed enabling modularization of related functions, but it also relies on AI techniques for the failure predictions, and the integration of devices and data is addressed partially.…”

Section: Related Studiesmentioning

confidence: 99%

A Model for Predictive Maintenance Based on Asset Administration Shell

Cavalieri

Salafia

2020

Sensors

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Maintenance is one of the most important aspects in industrial and production environments. Predictive maintenance is an approach that aims to schedule maintenance tasks based on historical data in order to avoid machine failures and reduce the costs due to unnecessary maintenance actions. Approaches for the implementation of a maintenance solution often differ depending on the kind of data to be analyzed and on the techniques and models adopted for the failure forecasts and for maintenance decision-making. Nowadays, Industry 4.0 introduces a flexible and adaptable manufacturing concept to satisfy a market requiring an increasing demand for customization. The adoption of vendor-specific solutions for predictive maintenance and the heterogeneity of technologies adopted in the brownfield for the condition monitoring of machinery reduce the flexibility and interoperability required by Industry 4.0. In this paper a novel approach for the definition of a generic and technology-independent model for predictive maintenance is presented. Such model leverages on the concept of the Reference Architecture Model for Industry (RAMI) 4.0 Asset Administration Shell, as a means to achieve interoperability between different devices and to implement generic functionalities for predictive maintenance.

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Machine Learning Framework for Predictive Maintenance in Milling

Cited by 76 publications

References 5 publications

Investigation of Degradation and Upgradation Models for Flexible Unit Systems: A Systematic Literature Review

Investigation of Degradation and Upgradation Models for Flexible Unit Systems: A Systematic Literature Review

Smart Prognostics and Health Management (SPHM) in Smart Manufacturing: An Interoperable Framework

A Model for Predictive Maintenance Based on Asset Administration Shell

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