Condition monitoring of machine tools

Harris, Constance M.; Williams, John H.; Davies, Annette

doi:10.1080/00207548908942633

Cited by 14 publications

(4 citation statements)

References 5 publications

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“…This approach can be used to make CM an integral part of an overall plan for maintenance within the organisation, allowing management to match strategies as appropriate to particular applications. This is seen as being important in establishing an effective maintenance plan [12].…”

Section: Towards Implementing Cbm Systemsmentioning

confidence: 99%

The Case for Condition Based Maintenance

Prickett¹,

Eavery²

1991

Integrated Manufacturing Systems

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If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation.

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Section: Towards Implementing Cbm Systemsmentioning

confidence: 99%

The Case for Condition Based Maintenance

Prickett¹,

Eavery²

1991

Integrated Manufacturing Systems

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“…The know-how obtained from the data can be used in different ways [1,2] In the simplest case, the data is used for condition monitoring [3] and, if necessary, to detect deviations from predefined process settings or a norm behavior (e.g., anomaly detection [4]). The complex DM and ML algorithms are also often used to investigate the cause-effect relationships in the production processes [5].…”

Section: Introductionmentioning

confidence: 99%

A Holistic Quality Assurance Approach for Machine Learning Applications in Cyber-Physical Production Systems

2021

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With the trend of increasing sensors implementation in production systems and comprehensive networking, essential preconditions are becoming required to be established for the successful application of data-driven methods of equipment monitoring, process optimization, and other relevant automation tasks. As a protocol, these tasks should be performed by engineers. Engineers usually do not have enough experience with data mining or machine learning techniques and are often skeptical about the world of artificial intelligence (AI). Quality assurance of AI results and transparency throughout the IT chain are essential for the acceptance and low-risk dissemination of AI applications in production and automation technology. This article presents a conceptual method of the stepwise and level-wise control and improvement of data quality as one of the most important sources of AI failures. The appropriate process model (V-model for quality assurance) forms the basis for this.

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“…For machine tools, the reactivity mainly consists in maintaining the productive operation and/or in contributing to reduce downtime when a problem is detected. In order to improve the reactivity of machine tools, many functions have been developed for monitoring and/or diagnosing the machines (Harris et al, 1989;Isermann, 1993) and the productive processes (Dan and Mathew, 1990), and for accommodating or adapting (Carrillo and Rotella, 1997;Liu et al, 2001). Diagnostic functions contribute to reduce downtime by identifying faults without requiring maintenance operators.…”

Section: Introductionmentioning

confidence: 99%

Multi-agent framework based on smart sensors/actuators for machine tools control and monitoring

Desforges

Archimède

2006

Engineering Applications of Artificial Intelligence

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Throughout the history, the evolutions of the requirements for manufacturing equipments have depended on the changes in the customers' demands. Among the present trends in the requirements for new manufacturing equipments, there are more flexible and more reactive machines. In order to satisfy those requirements, this paper proposes a control and monitoring framework for machine tools based on smart sensor, on smart actuator and on agent concepts. The proposed control and monitoring framework achieves machine monitoring, process monitoring and adapting functions that are not usually provided by machine tool control systems. The proposed control and monitoring framework has been evaluated by the means of a simulated operative part of a machine tool. The communication between the agents is achieved thanks to an Ethernet network and CORBA protocol. The experiments (with and without cooperation between agents for accommodating) give encouraging results for implementing the proposed control framework to operational machines. Also, the cooperation between the agents of control and monitoring framework contributes to the improvement of reactivity by adapting cutting parameters to the machine and process states and to increase productivity.

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Condition monitoring of machine tools

Cited by 14 publications

References 5 publications

The Case for Condition Based Maintenance

The Case for Condition Based Maintenance

A Holistic Quality Assurance Approach for Machine Learning Applications in Cyber-Physical Production Systems

Multi-agent framework based on smart sensors/actuators for machine tools control and monitoring

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