Study of ball screw system preload monitoring during operation based on the motor current and screw-nut vibration

Nguyễn, Tùng Lâm; Ro, Seung-Kook; Park, Jong-Kweon

doi:10.1016/j.ymssp.2019.05.036

Cited by 49 publications

(22 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this section, a simple approximation using lumped mass method is employed to model the screw shaft and this approach has been performed in Ref. [11,24,19,23]. The axial deformation δssz of screw shaft may be expressed as:…”

Section: Calculation For Position-dependent Elastic Restoring Force Of Lumped Screw Shaft Unitmentioning

confidence: 99%

See 1 more Smart Citation

A comprehensive nonlinear dynamic model for ball screw feed system with rolling joint characteristics

Zhang

et al. 2021

Nonlinear Dyn

View full text Add to dashboard Cite

Modern tendency of machine tools design requires more accurate model to predict the system dynamics, in order to anticipate its interaction with machining process. In this paper, a comprehensive dynamic model of ball screw feed system (BSFS) considering nonlinear kinematic joints is introduced to investigate the varying dynamic characteristics when worktable is subjected to combined load from six directions. The load-deformation relationship of each kinematic joint is dealt with a set of translational and angular spring elements. The nonlinear restoring force function of each joint involving coupling displacement is calculated. Based on the lumped mass method, the analytical 18-DOF dynamic equation is formulated by the analysis of force dependence between joints. Model verification tests are conducted. The worktable response exhibits the abundant and fascinating nonlinear phenomena arising in nonlinear joint and coupling effect. The nonlinear behavior behaves significant difference owing to the variations of excitation, platform position, screw length, preload and damping of joints. Thus, the model is promising for comprehension of machine dynamic behavior and for development of sophisticated control strategy.

show abstract

Section: Calculation For Position-dependent Elastic Restoring Force Of Lumped Screw Shaft Unitmentioning

confidence: 99%

“…Guo et al [22] performed a novel idea for the hybrid dynamic model of BSFS through combining classical dynamics theory and data driving. Nguyen et al [23] constructed a discrete dynamic model to investigate the interaction between preload, the displacement of working table and the axial natural frequency of screw-nut.…”

Section: Introductionmentioning

confidence: 99%

A comprehensive nonlinear dynamic model for ball screw feed system with rolling joint characteristics

Zhang

et al. 2021

Nonlinear Dyn

View full text Add to dashboard Cite

show abstract

“…Nguyen et al also used a simplified single axis feed drive. But instead of measuring and correlating signals from external accelerometers, they built a mechanical model of the feed drive, which allowed them to re-construct the current preload level of a ball screw by only measuring the feed drive table position and the motor current [4,14]. In [15], Tsai et al monitored the preload levels of linear guide shoes on a simplified single axis feed drive by placing external accelerometers on the work table and conducting an operational modal analysis.…”

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.

View full text Add to dashboard Cite

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.

show abstract

“…The results make sense for not only finding the appropriate model, but also predicting the RUL through temperature theory [5]. By evaluating the axial natural frequency from the motor current signal and axial vibrational signal, Nguyen et al propose a discrete dynamic model to characterize the degradation level [6]. The physicalmodel-based approach is suitable for a specific subject where the failure mechanism is well defined.…”

Section: Rul Prediction Based On Physical Modelsmentioning

confidence: 99%

A Hybrid Prognostics Deep Learning Model for Remaining Useful Life Prediction

Xie

et al. 2020

Electronics

View full text Add to dashboard Cite

Remaining Useful Life (RUL) prediction is significant in indicating the health status of the sophisticated equipment, and it requires historical data because of its complexity. The number and complexity of such environmental parameters as vibration and temperature can cause non-linear states of data, making prediction tremendously difficult. Conventional machine learning models such as support vector machine (SVM), random forest, and back propagation neural network (BPNN), however, have limited capacity to predict accurately. In this paper, a two-phase deep-learning-model attention-convolutional forget-gate recurrent network (AM-ConvFGRNET) for RUL prediction is proposed. The first phase, forget-gate convolutional recurrent network (ConvFGRNET) is proposed based on a one-dimensional analog long short-term memory (LSTM), which removes all the gates except the forget gate and uses chrono-initialized biases. The second phase is the attention mechanism, which ensures the model to extract more specific features for generating an output, compensating the drawbacks of the FGRNET that it is a black box model and improving the interpretability. The performance and effectiveness of AM-ConvFGRNET for RUL prediction is validated by comparing it with other machine learning methods and deep learning methods on the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset and a dataset of ball screw experiment.

show abstract

Study of ball screw system preload monitoring during operation based on the motor current and screw-nut vibration

Cited by 49 publications

References 22 publications

A comprehensive nonlinear dynamic model for ball screw feed system with rolling joint characteristics

A comprehensive nonlinear dynamic model for ball screw feed system with rolling joint characteristics

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

A Hybrid Prognostics Deep Learning Model for Remaining Useful Life Prediction

Contact Info

Product

Resources

About