Applying a support vector machine for hollow ball screw condition-based classification using feature extraction

Huang, Yi-Cheng; Hsieh, Yi-Keng

doi:10.1177/0954405420958842

Cited by 9 publications

(22 citation statements)

References 22 publications

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“…For example, the Fisher criterion can be used to select the features most representative of the data as in Ref. [136].…”

Section: Featurementioning

confidence: 99%

“…Rotary encoder [136,151,153], linear encoder [136,151,153], current [136,153], torque [151] Vibration [40,45,103,148,151,153], temperature [40,45] MD [45], SOM [45], GMM [45], SVM [40,136,151,153], GPR [103,151], GA/KNN [153], GPC [103], CNN [148]…”

Section: Preload Lossmentioning

confidence: 99%

“…The preload of a hollow ball screw is diagnosed using a genetic algorithm/k-nearest neighbor (GA/ KNN) method [153]. A support vector machine (SVM) model is used to diagnose the health of a hollow ball screw drive [136]. Softmax classification is used with a DBN model to monitor the degradation trend of a ball screw [135].…”

Section: Machine Learning Modelsmentioning

confidence: 99%

See 2 more Smart Citations

Condition Monitoring of Machine Tool Feed Drives: A Review

Butler

Ziada

Stephenson

et al. 2022

Journal of Manufacturing Science and Engineering

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The innovations propelling the manufacturing industry towards Industry 4.0 have begun to maneuver into machine tools. Machine tool maintenance primarily concerns the feed drives used for workpiece and tool positioning. Condition monitoring of feed drives is the intermediate step between smart data acquisition and evaluating machine health through diagnostics and prognostics. This review outlines the techniques and methods that recent research presents for feed drive condition monitoring, diagnostics and prognostics. The methods are distinguished between being sensorless and sensor-based, as well as between signal-, model- and machine learning-based techniques. Close attention is given to the components of feed drives (ball screws, linear guideways and rotary axes) and the most notable parameters used for monitoring. Commercial and industry solutions to Industry 4.0 condition monitoring are described and detailed. The review is concluded with a brief summary and the observed research gaps.

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“…For example, the Fisher criterion can be used to select the features most representative of the data as in Ref. [136].…”

Section: Featurementioning

confidence: 99%

Section: Preload Lossmentioning

confidence: 99%

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Condition Monitoring of Machine Tool Feed Drives: A Review

Butler

Ziada

Stephenson

et al. 2022

Journal of Manufacturing Science and Engineering

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“…All the above mentioned contributions, except the work presented by Schopp, who made some limited experiments on a machine tool in production, developed systems to monitor the feed drive condition on a simplified, single axis feed drive. In fact, many more investigations performed on a simplified test bench can be found in the literature [16][17][18][19][20]. Transfers to a more realistic test bench, resembling a real machine tool, are rare, however.…”

Section: Related Workmentioning

confidence: 99%

Experimental derivation of a condition monitoring test cycle for machine tool feed drives

Benker

Junker

Ellinger

et al. 2021

Prod. Eng. Res. Devel.

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Due to their critical influence on manufacturing accuracy, machine tool feed drives and the monitoring of their condition has been a research field of increasing interest for several years already. Accurate and reliable estimates of the current condition of the machine tool feed drive’s components ball screw drive (BSD) and linear guide shoes (LGSs) are expected to significantly enhance the maintainability of machine tools, which finally leads to economic benefits and smoother production. Therefore, many authors performed extensive experiments with different sensor signals, features and components. Most of those experiments were performed on simplified test benches in order to gain genuine and distinct insights into the correlations between the recorded sensor signals and the investigated fault modes. However, in order to build the bridge between real use cases and scientific findings, those investigations have to be transferred and performed on a more complex test bench, which is close to machine tools in operation. In this paper, a condition monitoring test cycle is developed for such a test bench. The developed test cycle enables the recording of a re-producible data basis, on which models for the condition monitoring of BSDs and LGSs can be based upon.

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“…It is noteworthy that the inhomogeneous distribution of the wear depth could be accurately calculated to predict the preload degradation of ball screws. Huang et al 22 used the vector machine to diagnose the preload degradation of ball screws in the absence of prominent characteristic features in data. However, only the maximum wear depth in the raceway contact region was calculated in the above-mentioned studies, which cannot describe the wear profile and the wear process of the raceway accurately.…”

Section: Introductionmentioning

confidence: 99%

Preload degradation mechanism of double-nut ball screws revealed by an innovative wear distribution model

Wang

Sun

Gao

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part B:

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The preload of ball screws is generally used to confirm the ball screws with a high axial contact stiffness and a high positioning accuracy. Aiming at the preload degradation due to wear on the raceway of double-nut ball screws, a wear distribution model was proposed by calculating the contact stress distribution and the inhomogeneous relative sliding velocity distribution on the contact region to determine the wear profile of the raceway. Firstly, the contact stress distribution and the relative sliding velocity distribution on the contact surface were determined using the kinematics analysis and the force balance analysis. Subsequently, a wear distribution model was established to determine the wear depth distribution of the contact surface based on elastic-plastic contact mechanics of asperity with consideration of the microscopic characteristics of the raceway. Finally, the three-level step-stress accelerated degradation test based on the simulated model was designed and implemented on a self-designed bench to validate the proposed model. Experimentally measured friction torques of the ball screws were used to reflect the preload degradation, and they were compared with the results of the simulated model. The results showed that the degradation curves of the experimental friction torques and the wear profiles of screw raceways were in good agreement with the numerical results, confirming the correctness and effectiveness of the proposed model. The results indicated that the wear profile of the raceway could be accurately calculated based on the proposed model, clearly describing the wear process of the raceway surface and providing a more precise approach to predict preload degradation for ball screws.

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Applying a support vector machine for hollow ball screw condition-based classification using feature extraction

Cited by 9 publications

References 22 publications

Condition Monitoring of Machine Tool Feed Drives: A Review

Condition Monitoring of Machine Tool Feed Drives: A Review

Experimental derivation of a condition monitoring test cycle for machine tool feed drives

Preload degradation mechanism of double-nut ball screws revealed by an innovative wear distribution model

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