A novel approach for data-driven process and condition monitoring systems on the example of mill-turn centers

Kißkalt, Dominik; Fleischmann, Hans; Kreitlein, Sven; Knott, Manuel; Franke, Jörg

doi:10.1007/s11740-018-0797-0

Cited by 11 publications

(3 citation statements)

References 19 publications

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“…In order to validate the capabilities of the developed software, a use case scenario is examined, where multiple MM are deployed. In this scenario -which originates from the project CoMoRes [13] -the wear state of ball screw drives in turn-mill-centers is monitored. Besides existing algorithms for the vibration analysis, torque analysis, backlash analysis and circularity analysis the state detection functionality based on an artificial neural network was used.…”

Section: Discussionmentioning

confidence: 99%

An Information Processing Framework Facilitating the Implementation of Condition Monitoring in Cyber-Physical Systems

Fleischmann

Kißkalt

Krusemark

et al. 2018

AMM

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Implementing condition monitoring functionality in production machinery often proves to be a difficult task. Device-and process-specific algorithms must be created while inhomogeneous industrial communication networks hinder the integration of control signals and process variables. Further challenges arise from the advance of flexible Cyber-Physical Systems (CPS) and the Industrial Internet of Things (IIoT). They demand a service-oriented condition monitoring architecture, which seamlessly adapts to quickly changing production topologies. Existing condition monitoring systems (CMS) and reference architectures for CMS however do not possess the capabilities to meet the requirements originating from CPS and the IIoT. This paper presents a software module serving as a generic framework to ease the implementation of decentralized condition monitoring functionalities. A decentralized component, the monitoring module, constitutes a part of a holistic condition monitoring architecture managed by a central server deployed on an edge or cloud server. Therefore, the monitoring module offers an interface through which its data processing flow and algorithms are entirely remotely configurable during operation. Algorithms are encapsulated in function blocks, which can easily be setup and interconnected. Scalability is ensured by the use of web technology optimized for efficient data handling and parallel data-processing. The examined use cases show the potential of the developed software module for its deployment in the use case of monitoring turn-mill-centers.

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Section: Discussionmentioning

confidence: 99%

An Information Processing Framework Facilitating the Implementation of Condition Monitoring in Cyber-Physical Systems

Fleischmann

Kißkalt

Krusemark

et al. 2018

AMM

Self Cite

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“…An other approach was followed by [28] who utilized several data-driven techniques in one condition monitoring pipeline to monitor not only a ball screw's condition but the condition of an entire machine tool in an unsupervised manner. They recorded vibration data first and estimated the signal's power spectral density (PSD).…”

Section: Condition Monitoring and Rul Estimationmentioning

confidence: 99%

Estimating remaining useful life of machine tool ball screws via probabilistic classification

Benker

Kleinwort

Zäh

2019

2019 IEEE International Conference on Prognostics and Health Management (ICPHM)

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Ball screws are key components in machine tool linear feed drives since they translate the motors' rotary motion into linear motion. With usage over time, however, tribological degradation of ball screws and the successive loss in preload can cause imprecise position accuracy and loss in manufacturing precision. Therefore condition monitoring (CM) of ball screws is important since it enables just in time replacement as well as the prevention of production stoppages and wasted material. This paper proposes an idea based on a probabilistic classification approach to monitor a ball screw's preload condition with the help of modal parameters identified from vibration signals. It will be shown that by applying probabilistic classification models, uncertainties with respect to degradation can be quantified in an intuitive way and therefore can enhance the basis of decision making. Furthermore, it will be shown how a probabilistic classification approach allows the estimation of remaining useful life (RUL) for ball screws when the user only has access to discrete preload observations.

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“…Nowadays, data-driven analysis methods have become a promising tool for the complex industrial issues (Ma and Jiang, 2011; Hou and Wang, 2013). A process identification system based on data-driven method was developed and evaluated in the actual industrial case (Dominik et al , 2018). It was reported that data-driven analysis results proved to be more accuracy than the mathematical dynamics model (Pan et al , 2016a; Pan et al , 2016b; Pan et al , 2020; Wang et al , 2018).…”

Section: Introductionmentioning

confidence: 99%

Flight data-driven intelligent prediction for fuselage vibration of helicopter

Deng¹,

Cheng²,

Lü³

2023

AEAT

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Purpose Helicopter fuselage vibration prediction is important to keep a safety and comfortable flight process. The helicopter vibration mechanism model is difficult to meet of demand for accurate vibration prediction. Thus, the purpose of this paper is to develop an intelligent algorithm for accurate helicopter fuselage vibration analysis. Design/methodology/approach In this research, a novel weighted variational mode decomposition (VMD)- extreme gradient boosting (xgboost) helicopter fuselage vibration prediction model is proposed. The vibration data is decomposed and reconstructed by the signal clustering results. The vibration response is predicted by xgboost algorithm based on the reconstructed data. The information transfer order between the controllable flight data and flight attitude are analyzed. Findings The mean absolute percentage error (MAPE), root mean square error (RMSE) and mean absolute error (MAE) of the proposed weighted VMD-xgboost model are decreased by 6.8%, 31.5% and 32.8% compared with xgboost model. The established weighted VMD-xgboost model has the highest prediction accuracy with the lowest mean MAPE, RMSE and MAE of 4.54%, 0.0162, and 0.0131, respectively. The attitude of horizontal tail and cycle pitch are the key factors to vibration. Originality/value A novel weighted VMD-xgboost intelligent prediction methods is proposed. The prediction effect of xgboost model is highly improved by using the signal-weighted reconstruction technique. In addition, the data set used is collected from actual helicopter flight process.

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A novel approach for data-driven process and condition monitoring systems on the example of mill-turn centers

Cited by 11 publications

References 19 publications

An Information Processing Framework Facilitating the Implementation of Condition Monitoring in Cyber-Physical Systems

An Information Processing Framework Facilitating the Implementation of Condition Monitoring in Cyber-Physical Systems

Estimating remaining useful life of machine tool ball screws via probabilistic classification

Flight data-driven intelligent prediction for fuselage vibration of helicopter

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