A new diagnostic technique for ball screw actuators

Garinei, Alberto; Marsili, Roberto

doi:10.1016/j.measurement.2012.02.023

Cited by 19 publications

(11 citation statements)

References 7 publications

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“…Other methodologies utilize vibration and temperature signal data measurement by using suitable sensors and applying different feature extraction techniques for faults identification in critical elements [10], [11]. Health assessment of BS drives based on experimental and systematic studies was also performed for early fault diagnosis [12], [13]. Recently, the applications of machine learning and deep learning based intelligent techniques have rapidly increased for mechanical systems fault diagnosis [14], [15] and different approaches have been successfully applied for rotating machinery fault detection [16], bearings fault diagnosis [17] and gears fault classification [18].…”

Section: Brief Details Of Previous Workmentioning

confidence: 99%

An Intelligent Hybrid Scheme for Identification of Faults in Industrial Ball Screw Linear Motion Systems

et al. 2021

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Reliability of high precision linear motion system is one of the main concerns in industrial and military systems. The performance and repeatability of these systems are influenced by their respective linear drives and load bearings. A fault in these members severely affects the safe working of overall system. This paper gives a reliable intelligent approach to detect and classify faults for linear motion systems based on deep learning methods. Accuracy in faults identification is highly dependent on improved features extraction. For this purpose, a novel Residual Twin CNN (ResT-CNN) is proposed that uses combination of 1-D and 2-D CNN in parallel learning which improves features extraction performance; followed by knowledge base-Remnant-PCA (Kb-Rem-PCA) architecture in combination with multi-class support vector machine (Mc-SVM). This novel hybrid combination proved very effective in accurate faults identification. The performance of proposed methodology was also validated by IMS-UC (Intelligent Maintenance Systems-University of Cincinnati) public bearing dataset. The results confirm the effectiveness of proposed scheme in comparison to existing state of the art techniques.

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Section: Brief Details Of Previous Workmentioning

confidence: 99%

An Intelligent Hybrid Scheme for Identification of Faults in Industrial Ball Screw Linear Motion Systems

et al. 2021

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“…Degradation may be tracked periodically by data collection during a fixed-cycle test (Garinei & Marsili, 2012, Huang et al, 2010, Liao & Lee, 2009, Verl et al, 2009, Zhou et al, 2009, Zhou et al, 2011). During a fixed-cycle test, the machine tool axes are commanded to move via the same program (the fixed cycle) with the machine tool initially in the same state (temperature, etc.)…”

Section: Sensor Box Concept For Metrologymentioning

confidence: 99%

A Sensor-based Method for Diagnostics of Geometric Performance of Machine Tool Linear Axes

Vogl

Calamari

et al. 2016

Procedia Manufacturing

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A linear axis is a vital subsystem of machine tools, which are vital systems within many manufacturing operations. When installed and operating within a manufacturing facility, a machine tool needs to stay in good condition for parts production. All machine tools degrade during operations, yet knowledge of that degradation is illusive; specifically, accurately detecting degradation of linear axes is a manual and time-consuming process. Thus, manufacturers need automated and efficient methods to diagnose the condition of their machine tool linear axes without disruptions to production. The Prognostics and Health Management for Smart Manufacturing Systems (PHM4SMS) project at the National Institute of Standards and Technology (NIST) developed a sensor-based method to quickly estimate the performance degradation of linear axes. The multi-sensor-based method uses data collected from a ‘sensor box’ to identify changes in linear and angular errors due to axis degradation; the sensor box contains inclinometers, accelerometers, and rate gyroscopes to capture this data. The sensors are expected to be cost effective with respect to savings in production losses and scrapped parts for a machine tool. Numerical simulations, based on sensor bandwidth and noise specifications, show that changes in straightness and angular errors could be known with acceptable test uncertainty ratios. If a sensor box resides on a machine tool and data is collected periodically, then the degradation of the linear axes can be determined and used for diagnostics and prognostics to help optimize maintenance, production schedules, and ultimately part quality.

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“…Health monitoring methods for ball-screw faults of flight control EMA were deeply investigated by [5][6][7][8]. In [5], Balaban et al investigated a neural network method for detecting two ball-screw faults: a partial jam and spalls for a flight control actuator.…”

Section: Introductionmentioning

confidence: 99%

“…The separation between the spall and jam relied only on the vibration sensors. Garinei and Marsili in [6] developed a statistical diagnosis method for m damaged ball and return channel of a ball-screw actuator. A Hall effect mounted on the ball return channel for monitoring the motion's smoothness The statistical distributions of the Hall sensor data were estimated for differe ditions in order to decide the health status.…”

Section: Introductionmentioning

confidence: 99%

Spall Fault Quantification Method for Flight Control Electromechanical Actuator

2022

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Flight control electro-mechanical actuators (EMAs) are among the primary onboard systems that significantly influence the reliability and safety of unmanned aerial vehicles. Recent reliability studies have shown that the ball-screw element of a flight control EMA is subject to oscillating operating conditions that may initiate rapid degradation, such as fatigue spall defects. Accordingly, detecting and quantifying such faults are crucial for developing efficient fault prognostic and remaining useful life estimation capabilities. In this study, a vibration-based fault quantification method is developed to quantify the fatigue faults of a ball-screw mechanism of an EMA. The method is based on identifying the ball passing instants through a localized surface defect on the vibrational jerk rather than the vibrational acceleration measurement. The jerk is numerically determined from conventional accelerometers using a Savitzky–Golay differentiator. This method was successfully tested for ball bearings and it is adjusted in this paper for ball-screw faults. The experimental validation is investigated on a set of fault-seeded samples on NASA’s Ames Research Center Flyable Electro-Mechanical Actuator test stand.

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A new diagnostic technique for ball screw actuators

Cited by 19 publications

References 7 publications

An Intelligent Hybrid Scheme for Identification of Faults in Industrial Ball Screw Linear Motion Systems

An Intelligent Hybrid Scheme for Identification of Faults in Industrial Ball Screw Linear Motion Systems

A Sensor-based Method for Diagnostics of Geometric Performance of Machine Tool Linear Axes

Spall Fault Quantification Method for Flight Control Electromechanical Actuator

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