Measurement and Evaluation for Prognostics and Health Management (PHM) for Manufacturing Operations – Summary of an Interactive Workshop Highlighting PHM Trends

Weiss, Brian A.; Brundage, Michael P.

doi:10.36001/ijphm.2021.v12i1.2653

Cited by 8 publications

(2 citation statements)

References 45 publications

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“…Specific data scenarios that typically occur with the same general data set characteristics in industrial application hold strong importance for the widespread implementation of PHM (Weiss & Brundage, 2021). These data scenarioswhich involve deficiencies that require their own methodological adaptation -are therefore addressed.…”

Section: Identification Of Data Scenarios Relevant For Industrial Applicationsmentioning

confidence: 99%

Creation of Publicly Available Data Sets for Prognostics and Diagnostics Addressing Data Scenarios Relevant to Industrial Applications

Fabian

Hagmeyer

Zeiler

2021

IJPHM

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For a successful realization of prognostics and health management (PHM), the availability of sufficient run-to-failure data sets is a crucial factor. The sheer number of given data points holds less importance than the full coverage of the potential state space. However, full coverage is a major challenge in most industrial applications. Among other things, high investment and operating costs as well as the long service life of many technical systems make it difficult to acquire complete run-to-failure data sets. Consequently, in industrial applications data sets with specific deficiencies are frequently encountered. The development of appropriate methods to address such data scenarios is a fundamental research issue. Therefore, the purpose of this paper is to provide facilitation for this research. Accordingly, the paper starts by specifying the value and availability of data in PHM. Subsequently, criteria for characterizing data sets are defined independent of the actual PHM application. The criteria are used to identify typical data scenarios with specific deficiencies that possess significant relevance for industrial applications. Thereafter, the most comprehensive overview of data sets suitable for PHM and currently publicly accessible is provided. Thereby, not all previously identified data scenarios with their specific deficiencies are addressed by at least one data set. A program is established for the aforementioned facilitation of further research. One objective of the program is to create data sets reflecting these data scenarios using a test bench. First, possible applications and their degradation processes to be studied on the test bench are briefly characterized. Thereby, the final decision to select filtration as a test bench application is argued. Subsequently, the test bench created is introduced, including a description of the functional concept, pneumatic layout and components involved, as well as the filter media and test dusts employed. Typical run-to-failure trajectories are illustrated. Thereafter, the data set published under the name Preventive to Predictive Maintenance is presented. Additionally, a schedule for future releases of data sets on further industry-relevant data scenarios is sketched.

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Section: Identification Of Data Scenarios Relevant For Industrial Applicationsmentioning

confidence: 99%

Creation of Publicly Available Data Sets for Prognostics and Diagnostics Addressing Data Scenarios Relevant to Industrial Applications

Fabian

Hagmeyer

Zeiler

2021

IJPHM

View full text Add to dashboard Cite

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“…Besides, Aye [9] proposed a gaussian process regression model for bearing prognostics, helping a manufacturing factory reduce down time so as to improve efficiency. From a more macro perspective, the National Institute of Standards and Technology of America [10] proposed a series of recommendation on monitoring, diagnosis, and prognosis capabilities in manufacturing operations in 2019. The policy of which provides an essential reference for the researchers all over the world to follow up relative research.…”

Section: Introductionmentioning

confidence: 99%

Design of a quality risk early warning system for discrete manufacturing based on stepped neural network

Shen,

Chu,

Duan

et al. 2024

Ninth International Symposium on Sensors, Mechatronics, and Automation System (ISSMAS 2023)

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The complexity of mechanical part manufacturing process calls for quality control from the production line level. Regarding the fuzzy mechanism, low efficiency and high cost of error diagnosis and resolution, AI methods are used to carry out prognostics that avoid the failure occurs. This paper proposes an error prediction model based on a new form of neural network constructed by serially connected branch networks, in which each branch network corresponds to a manufacturing stage. Then an early warning system for the production line concentrating on discrete manufacturing industry is developed based on the proposed model. Application research is carried out in a mechanical parts manufacturing factory where the prototype system is testified to be effective and meaningful. The study contributes to reduce the production shutdown and improve quality control capability for discrete manufacturing factories.

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Advances in Smart Maintenance for Sustainable Manufacturing in Industry 4.0

Hadjadji¹,

Karganroudi²,

Barka³

et al. 2023

Sustainable Manufacturing in Industry 4.0

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Measurement and Evaluation for Prognostics and Health Management (PHM) for Manufacturing Operations – Summary of an Interactive Workshop Highlighting PHM Trends

Cited by 8 publications

References 45 publications

Creation of Publicly Available Data Sets for Prognostics and Diagnostics Addressing Data Scenarios Relevant to Industrial Applications

Creation of Publicly Available Data Sets for Prognostics and Diagnostics Addressing Data Scenarios Relevant to Industrial Applications

Design of a quality risk early warning system for discrete manufacturing based on stepped neural network

Advances in Smart Maintenance for Sustainable Manufacturing in Industry 4.0

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