Intelligent Real-Time Risk Analysis for Machines and Process Devices

Juuso, Esko; Galar, Diego

doi:10.1007/978-3-319-23597-4_17

Cited by 3 publications

(3 citation statements)

References 9 publications

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“…Applications are related to business functions in Enterprise resource planning (ERP) or maintenance functions in computerized maintenance management systems (CMMS). Smart field devices can include equipment monitoring applications which are used to help monitor and maintain the devices [23].…”

Section: Control Strategies and Maintenancementioning

confidence: 99%

Intelligent Performance Analysis with a Natural Language Interface

Juuso¹

2017

Management Systems in Production Engineering

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Abstract:Performance improvement is taken as the primary goal in the asset management. Advanced data analysis is needed to efficiently integrate condition monitoring data into the operation and maintenance. Intelligent stress and condition indices have been developed for control and condition monitoring by combining generalized norms with efficient nonlinear scaling. These nonlinear scaling methodologies can also be used to handle performance measures used for management since management oriented indicators can be presented in the same scale as intelligent condition and stress indices. Performance indicators are responses of the process, machine or system to the stress contributions analyzed from process and condition monitoring data. Scaled values are directly used in intelligent temporal analysis to calculate fluctuations and trends. All these methodologies can be used in prognostics and fatigue prediction. The meanings of the variables are beneficial in extracting expert knowledge and representing information in natural language. The idea of dividing the problems into the variable specific meanings and the directions of interactions provides various improvements for performance monitoring and decision making. The integrated temporal analysis and uncertainty processing facilitates the efficient use of domain expertise. Measurements can be monitored with generalized statistical process control (GSPC) based on the same scaling functions.

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Section: Control Strategies and Maintenancementioning

confidence: 99%

Intelligent Performance Analysis with a Natural Language Interface

Juuso¹

2017

Management Systems in Production Engineering

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“…Localised calculations can bring the extracted information to instantaneous use in automatic control or decision making. The long-term monitoring and tracking of cumulative stress gives valuable information for remaining useful life, RUL, calculations and prognostics, and short-term stress indices [8] indicate momentary high-stress periods, providing information about the causes of the wear and they can be used for control.…”

Section: Monitoring Wearmentioning

confidence: 99%

8 Application system design - Maintenance

Jantunen¹,

Karaila²,

Hästbacka³

et al. 2017

IoT Automation

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“…Cumulative stress indices obtained from vibration measurements have also been proposed for a Kaplan water turbine and a load haul dumper (Juuso 2014). Cumulative stress indices were previously introduced for the prediction of roller mill fatigue based on torque measurements (Juuso and Ruusunen 2013) and later extended to the real-time risk analysis of machines and process devices (Juuso and Galar 2016). The stress contributions were obtained using a datadriven non-linear scaling approach (Juuso and Lahdelma 2010).…”

Section: Introductionmentioning

confidence: 99%

Prediction of mechanical stress in roller leveler based on vibration measurements and steel strip properties

et al. 2017

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The continuous development of steel products generates new challenges for the maintenance of manufacturing machines in steel mills. Substantial mechanical stress is inflicted on the machines during the processing of modern high-strength steels. This increases the risks of damage and flaws in the processed material may appear if the capability of a machine is exceeded. Therefore, new approaches are needed to prevent the machine condition from deteriorating. This study introduces an approach to the prediction of mechanical stress inflicted on a roller leveler during the processing of cold steel strips. The relative stress level is indicated by features extracted from an acceleration signal. These features are based on the calculation of generalized norms. Steel strip properties are used as explanatory variables in regression models to predict values for the extracted vibration features. The models tested in this study include multiple linear regression, partial least squares regression and generalized regression neural network. The models were tested using an extensive data set from a roller leveler that is in continuous operation in a steel mill. The prediction accuracy of the best models identified indicates that the relative stress level inflicted by each steel strip could be predicted based on its properties.

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Intelligent Real-Time Risk Analysis for Machines and Process Devices

Cited by 3 publications

References 9 publications

Intelligent Performance Analysis with a Natural Language Interface

Intelligent Performance Analysis with a Natural Language Interface

8 Application system design - Maintenance

Prediction of mechanical stress in roller leveler based on vibration measurements and steel strip properties

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