Scalable prognostic models for large-scale condition monitoring applications

2017 ◽  
Vol 49 (7) ◽  
pp. 698-710 ◽  
Author(s):  
Xiaolei Fang ◽  
Nagi Z. Gebraeel ◽  
Kamran Paynabar
Keyword(s):  
Condition Monitoring ◽  
Large Scale ◽  
Prognostic Models ◽  
Monitoring Applications

On the use of jerk and snap in condition monitoring of machinery – review and case studies

2021 ◽  
Vol 63 (8) ◽  
pp. 457-464
Author(s):  
S Lahdelma
Keyword(s):  
Condition Monitoring ◽  
Large Scale ◽  
Early Stage ◽  
Time Derivative ◽  
Additional Insight ◽  
Rolling Bearings ◽  
Monitoring Applications ◽  
Derivatives Of ◽  
Insight Into ◽  
Peak Value

The time derivatives of acceleration offer a great advantage in detecting impact-causing faults at an early stage in condition monitoring applications. Defective rolling bearings and gears are common faults that cause impacts. This article is based on extensive real-world measurements, through which large-scale machines have been studied. Numerous laboratory experiments provide additional insight into the matter. A practical solution for detecting faults with as few features as possible is to measure the root mean square (RMS) velocity according to the standards in the frequency range from 10 Hz to 1000 Hz and the peak value of the second time derivative of acceleration, ie snap. Measuring snap produces good results even when the upper cut-off frequency is as low as 2 kHz or slightly higher. This is valuable information when planning the mounting of accelerometers.

Case Studies of Proactive Condition Monitoring Applications

2014 ◽  
pp. 137-183
Author(s):  
Zhaklina Stamboliska ◽  
Eugeniusz Rusiński ◽  
Przemyslaw Moczko
Keyword(s):  
Case Studies ◽  
Condition Monitoring ◽  
Monitoring Applications

scholarly journals Feasibility and Evaluation of a Large-Scale External Validation Approach for Patient-Level Prediction in an International Data Network: Validation of models predicting stroke in female patients newly diagnosed with atrial fibrillation.

2020 ◽  
Author(s):  
Jenna Marie Reps ◽  
Ross Williams ◽  
Seng Chan You ◽  
Thomas Falconer ◽  
Evan Minty ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Large Scale ◽  
Data Science ◽  
Prediction Models ◽  
External Validation ◽  
Scale Up ◽  
R Package ◽  
Prognostic Models ◽  
Healthcare Data ◽  
Patient Level

Abstract Objective: To demonstrate how the Observational Healthcare Data Science and Informatics (OHDSI) collaborative network and standardization can be utilized to scale-up external validation of patient-level prediction models by enabling validation across a large number of heterogeneous observational healthcare datasets.Materials & Methods: Five previously published prognostic models (ATRIA, CHADS2, CHADS2VASC, Q-Stroke and Framingham) that predict future risk of stroke in patients with atrial fibrillation were replicated using the OHDSI frameworks. A network study was run that enabled the five models to be externally validated across nine observational healthcare datasets spanning three countries and five independent sites. Results: The five existing models were able to be integrated into the OHDSI framework for patient-level prediction and they obtained mean c-statistics ranging between 0.57-0.63 across the 6 databases with sufficient data to predict stroke within 1 year of initial atrial fibrillation diagnosis for females with atrial fibrillation. This was comparable with existing validation studies. The validation network study was run across nine datasets within 60 days once the models were replicated. An R package for the study was published at https://github.com/OHDSI/StudyProtocolSandbox/tree/master/ExistingStrokeRiskExternalValidation.Discussion: This study demonstrates the ability to scale up external validation of patient-level prediction models using a collaboration of researchers and a data standardization that enable models to be readily shared across data sites. External validation is necessary to understand the transportability or reproducibility of a prediction model, but without collaborative approaches it can take three or more years for a model to be validated by one independent researcher. Conclusion : In this paper we show it is possible to both scale-up and speed-up external validation by showing how validation can be done across multiple databases in less than 2 months. We recommend that researchers developing new prediction models use the OHDSI network to externally validate their models.

scholarly journals Feasibility and Evaluation of a Large-Scale External Validation Approach for Patient-Level Prediction in an International Data Network: Validation of models predicting stroke in female patients newly diagnosed with atrial fibrillation.

2020 ◽  
Author(s):  
Jenna Marie Reps ◽  
Ross D Williams ◽  
Seng Chan You ◽  
Thomas Falconer ◽  
Evan Minty ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Large Scale ◽  
Data Science ◽  
Prediction Models ◽  
External Validation ◽  
Scale Up ◽  
R Package ◽  
Prognostic Models ◽  
Healthcare Data ◽  
Patient Level

Abstract Background: To demonstrate how the Observational Healthcare Data Science and Informatics (OHDSI) collaborative network and standardization can be utilized to scale-up external validation of patient-level prediction models by enabling validation across a large number of heterogeneous observational healthcare datasets.Methods: Five previously published prognostic models (ATRIA, CHADS2, CHADS2VASC, Q-Stroke and Framingham) that predict future risk of stroke in patients with atrial fibrillation were replicated using the OHDSI frameworks. A network study was run that enabled the five models to be externally validated across nine observational healthcare datasets spanning three countries and five independent sites. Results: The five existing models were able to be integrated into the OHDSI framework for patient-level prediction and they obtained mean c-statistics ranging between 0.57-0.63 across the 6 databases with sufficient data to predict stroke within 1 year of initial atrial fibrillation diagnosis for females with atrial fibrillation. This was comparable with existing validation studies. The validation network study was run across nine datasets within 60 days once the models were replicated. An R package for the study was published at https://github.com/OHDSI/StudyProtocolSandbox/tree/master/ExistingStrokeRiskExternalValidation.Conclusion : This study demonstrates the ability to scale up external validation of patient-level prediction models using a collaboration of researchers and a data standardization that enable models to be readily shared across data sites. External validation is necessary to understand the transportability or reproducibility of a prediction model, but without collaborative approaches it can take three or more years for a model to be validated by one independent researcher. In this paper we show it is possible to both scale-up and speed-up external validation by showing how validation can be done across multiple databases in less than 2 months. We recommend that researchers developing new prediction models use the OHDSI network to externally validate their models.

scholarly journals An Open Source Framework Approach to Support Condition Monitoring and Maintenance

2020 ◽  
Vol 10 (18) ◽  
pp. 6360
Author(s):  
Jaime Campos ◽  
Pankaj Sharma ◽  
Michele Albano ◽  
Luis Lino Ferreira ◽  
Martin Larrañaga
Keyword(s):  
Condition Monitoring ◽  
Open System ◽  
Health Monitoring ◽  
Large Scale ◽  
Condition Based Maintenance ◽  
Monitoring Methods ◽  
Industry Standards ◽  
Framework Approach ◽  
Push Forward ◽  
Predictive Health

This paper discusses the integration of emergent ICTs, such as the Internet of Things (IoT), the Arrowhead Framework, and the best practices from the area of condition monitoring and maintenance. These technologies are applied, for instance, for roller element bearing fault diagnostics and analysis by simulating faults. The authors first undertook the leading industry standards for condition-based maintenance (CBM), i.e., open system architecture–condition-based maintenance (OSA–CBM) and Machinery Information Management Open System Alliance (MIMOSA), which has been working towards standardizing the integration and interchangeability between systems. In addition, this paper highlights the predictive health monitoring methods that are needed for an effective CBM approach. The monitoring of industrial machines is discussed as well as the necessary details are provided regarding a demonstrator built on a metal sheet bending machine of the Greenbender family. Lastly, the authors discuss the benefits of the integration of the developed prototypes into a service-oriented platform, namely the Arrowhead Framework, which can be instrumental for the remotization of maintenance activities, such as the analysis of various equipment that are geographically distributed, to push forward the grand vision of the servitization of predictive health monitoring methods for large-scale interoperability.

Open Platform for the Integration of Monitoring Applications

2002 ◽  
Author(s):  
Andreas Kahmen ◽  
Manfred Weck
Keyword(s):  
Control System ◽  
Machine Tool ◽  
Real Time ◽  
Condition Monitoring ◽  
Tool Condition Monitoring ◽  
Integration Platform ◽  
Open Platform ◽  
Tool Condition ◽  
Monitoring Applications ◽  
Monitoring Software

Process and machine tool condition monitoring are the keys to an increasing degree of automation and consequently to an increasing productivity in manufacturing. The realisation of monitoring functionality demands an extension of the control system. The prerequisite for these extensions are open interfaces in the NC-kernel. Nowadays controls with open NC-kernel interfaces are available on the market. However these interfaces are vendor specific solutions that do not allow the reuse of monitoring software in different controls. To overcome these limitations a platform with vendor neutral open real-time interface for the integration of monitoring functionality into the NC-kernel is presented in this paper. Additionally two realisations of the integration platform for different target systems are described.

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