A defect-driven diagnostic method for machine tool spindles

Vogl, Gregory W.; Donmez, M A.

doi:10.1016/j.cirp.2015.04.103

Cited by 19 publications

(6 citation statements)

References 11 publications

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“…Vogl and Donmez developed a prototype spindle condition estimation device that uses a solenoid-driven mass to apply an impulse force to the housing of a machine tool spindle. Notable features of this design are the integration of two accelerometers and a force sensor into the device and a magnetic base, which allows the small device to be portable and attached to the spindle housings of different machines [14]. Bediz et al used a custom-made impact hammer excitation system which used a double-notch flexure actuated by an electromagnet to reliably excite frequencies up to 20 kHz in miniature ultra-high-speed spindles.…”

Section: Advanced Methods For Modal Analysis Data Collectionmentioning

confidence: 99%

Practical Estimation of Machine Tool Spindle Dynamics for Maintenance Decision Making

Chin,

DePavia,

Veldhuis

2024

Applied Sciences

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Condition-based maintenance of a machine tool spindle unit is known to reduce the total cost of operation in a manufacturing environment. However, standard vibration monitoring thresholds, which are based only on average vibration energy levels, do not account for the machine’s dynamics, such as dynamic stiffness and damping ratios, which, ultimately, control the final quality of a machined workpiece. In this study, a new mechanical device is proposed for the estimation of spindle dynamics characteristics through measurement of the frequency response function (FRF) of the spindle. The device is simple, robust in a manufacturing environment, small, and requires no power source. The new device is shown to be comparable in performance to conventional impact hammer testing, with the advantage of being fully automated and capable of dynamic assessment on a rotating spindle. Such a device allows a machine tool operator to routinely monitor the stiffness and damping ratios of the spindle unit and make informed maintenance decisions based on meaningful changes in operating conditions that impact production.

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Section: Advanced Methods For Modal Analysis Data Collectionmentioning

confidence: 99%

Practical Estimation of Machine Tool Spindle Dynamics for Maintenance Decision Making

Chin,

DePavia,

Veldhuis

2024

Applied Sciences

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“…There are classical machine learning approaches that, for instance, rely on Support Vector Machines (SVMs) [ 8 ]. However, these approaches use features of the time domain, e.g., Vogl and Donmez [ 9 ], or rely on features of the time–frequency domain, such as Prudhom et al [ 10 ]. Recently, these approaches have been supplemented with deep learning approaches, which use models based on artificial neural networks arranged in ‘deep’ stacked layers, such as Convolutional Neural Networks (CNN) [ 11 ] and Long Short-Term Memory (LSTM) networks [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

Transfer-Learning-Based Estimation of the Remaining Useful Life of Heterogeneous Bearing Types Using Low-Frequency Accelerometers

Schwendemann

Sikora

2023

J. Imaging

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Deep learning approaches are becoming increasingly important for the estimation of the Remaining Useful Life (RUL) of mechanical elements such as bearings. This paper proposes and evaluates a novel transfer learning-based approach for RUL estimations of different bearing types with small datasets and low sampling rates. The approach is based on an intermediate domain that abstracts features of the bearings based on their fault frequencies. The features are processed by convolutional layers. Finally, the RUL estimation is performed using a Long Short-Term Memory (LSTM) network. The transfer learning relies on a fixed-feature extraction. This novel deep learning approach successfully uses data of a low-frequency range, which is a precondition to use low-cost sensors. It is validated against the IEEE PHM 2012 Data Challenge, where it outperforms the winning approach. The results show its suitability for low-frequency sensor data and for efficient and effective transfer learning between different bearing types.

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“…The new approach Spindle Condition Estimate device (SCED) was developed to collect data and segregate the system dynamics from defect. The approach was shown capable of detecting a defect-driven diagnostic method for machine tool spindles (Vogl & Donmez, 2015). The Initial Measurement Unit (IMU) was developed for diagnosing the machine condition to minimise interrupt to production.…”

Section: Introductionmentioning

confidence: 99%

Fault detection and diagnosis of linear bearing in auto core adhesion mounting machines based on condition monitoring

Chommuangpuck

Wanglomklang

Srisertpol

2021

Systems Science & Control Engineering

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This study aims to increase machine reliability thereby preventing product defects from adhesive dispense and slider attachment by a fault detection and diagnostic technique. The experiment was set up to investigate the vibration signal and motor current. Six fault conditions of a linear bearing were set up. The approaches, including spectrum analysis, crest factor, and analysis of variance, are used for data analysis. It was found that the spectrum analysis was suitable for classifying the frequency domains and the statistics tool was successful in measuring the current. Fault detection and diagnosis results can forecast the status of the linear bearings.

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A defect-driven diagnostic method for machine tool spindles

Cited by 19 publications

References 11 publications

Practical Estimation of Machine Tool Spindle Dynamics for Maintenance Decision Making

Practical Estimation of Machine Tool Spindle Dynamics for Maintenance Decision Making

Transfer-Learning-Based Estimation of the Remaining Useful Life of Heterogeneous Bearing Types Using Low-Frequency Accelerometers

Fault detection and diagnosis of linear bearing in auto core adhesion mounting machines based on condition monitoring

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