Inferential based measurement of backlash in servo system

Chandrasekar, P.; Srinivasan, K.

doi:10.1016/j.matpr.2020.09.552

Cited by 5 publications

(5 citation statements)

References 6 publications

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“…This data is used to model the system states and parameters. MTs have many options of data streams for modeling 3 Some of this data comes via integrated sensors such as the encoders 4 and integrated torque/motor current, 5 or external sensors such as vibration, 6 and temperature sensors. 7 In addition to sensors, production data, such as production speed, machine reliability, or part quality can be used for evaluation.…”

Section: Data Collectionmentioning

confidence: 99%

Generating synthetic data for data-driven solutions via a digital twin for condition monitoring in machine tools

Sicard,

Butler,

et al. 2024

Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications II

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Machine tools (MT) are critical to modern manufacturing. They allow precision manufacturing of complex components at high volumes. MTs are large capital investments that require maintenance and monitoring to ensure they remain in good working condition. To best achieve reliability and high performance it is necessary to implement condition monitoring, fault detection and predictive maintenance. One solution for implementing these is by utilizing data-driven methods such as neural networks. One issue with any data-driven method is that they require large quantities of labeled data. This is especially difficult for fault detection applications as faults tend to be rare, and as a result, the datasets tend to be very imbalanced. One emerging technology that can be implemented to solve this issue is the digital twin (DT). DTs provide a solution for data collection, modeling, simulation, and smart services. One way that DTs can be used is to generate synthetic data which can be used for various data-driven methods. This data can be validated on a test bench to ensure its accuracy before implementation in production. Synthetic data generated from the DT model can be used to create a dataset for various condition monitoring DT services. This study involved the use of simulation software to generate synthetic data which was used to implement a fault detection algorithm for preload loss monitoring. This method has been demonstrated to be effective at identifying the current operating conditions of the system. This method shows promise to improve reliability and performance in MTs, and could be adapted to condition monitoring in other systems such as vehicles, buildings, and power generation.

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Section: Data Collectionmentioning

confidence: 99%

Generating synthetic data for data-driven solutions via a digital twin for condition monitoring in machine tools

Sicard,

Butler,

et al. 2024

Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications II

View full text Add to dashboard Cite

show abstract

“…[106]. An algorithm developed by Chandrasekar and Srinivasan measures backlash error using torque-limited positions [107]. Transient backlash error (TBE) models are also derived for both linear and rotational machine trajectories [105].…”

Section: Deterministic Modelsmentioning

confidence: 99%

“…Likewise, an exponential Wiener process is used to predict the RUL [112]. BPF [67,71], OMA [51,101,100] Preload loss -Vibration [52,102,103], force [104], Hall sensor [52] EMA [102,103], OMA [52], analytic [104] Backlash Rotary encoder [76,83,[105][106][107], linear encoder [76,108,109,105,106], NC data [105,110] -Error [83,108,109,105], EMA [110], algorithm [107], analytic [76,105], adaptive estimation [106]…”

Section: Stochastic Modelsmentioning

confidence: 99%

“…(2) Position command and linear encoder (position error) [108,109]. (3) Torque-limiting positions [107]. (4) Forward and reverse linear encoder positions (position reversal) [105].…”

Section: Backlash Monitoringmentioning

confidence: 99%

“…Data collected over a span of 12 months show that backlash worsens over time, suggesting that backlash can be used as an indicator of ball screw wear. Torque-limiting positions are used to monitor backlash in a prototype feed drive by Chandrasekar and Srinivasan [107]. An iterative algorithm that takes the difference between the torque-limited positions, measured by a rotary encoder, quantifies the backlash in the feed drive.…”

Section: Backlash Monitoringmentioning

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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