Prognosability study of ball screw degradation using systematic methodology

Пин, Ли; Jia, Xiaodong; Feng, Jianshe; Davari, Hossein; Qiao, Guifang; Hwang, Yih-Chyun; Lee, Jay

doi:10.1016/j.ymssp.2018.02.046

Cited by 84 publications

(38 citation statements)

References 18 publications

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“…In this work, only the monitoring and diagnosis of the preload loss were processed through the use of motor current, motor speed (sensor-less), optical linear scale, and vibration (sensor-rich) signals. A systematic methodology for ball-screw prognosis, health assessment, and remaining useful life was proposed by the authors of [22]. Future studies will compare various sensed signals using new machine-learning processes.…”

Section: Discussionmentioning

confidence: 99%

Diagnosis of the Hollow Ball Screw Preload Classification Using Machine Learning

2018

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The prognostic diagnosis of machine-health status is an emerging research topic. In this study, the diagnostic results of hollow ball screws with various ball-nut preloads were obtained using a machine-learning approach. In this method, ball-screw pretension, oil circulation, and ball-nut preload were considered. A feature extraction was used to determine the hollow ball-screw preload status on the basis of vibration signals, servo-motor speed, servo-motor current signals, and linear scale counts. Preloads with 2%, 4%, and 6% ball screws were predesigned, manufactured, and operated. Signal patterns with various preload features, servo-motor speeds, servo-motor current signals, and linear scale counts were classified using the support vector machine (SVM) algorithm. The features of the vibration signal were classified using the genetic algorithm/k-nearest neighbor (GA/KNN) method. The complex and irregular model of the ball-screw-nut preload could be learned and supervised using the driving motion current, ball-screw speed, linear scale positioning, and vibration signals of the ball screw. The experimental results indicate that the prognostic status of the ball-nut preload can be determined using the proposed methodology. The proposed diagnostic method can be used to prognosticate the health status of the machine tool.

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Section: Discussionmentioning

confidence: 99%

Diagnosis of the Hollow Ball Screw Preload Classification Using Machine Learning

2018

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“…Another approach, followed by [29], is the extensive search for failure-sensitive features as a first step. This was done by constructing as many features from vibration data as possible.…”

Section: Condition Monitoring and Rul Estimationmentioning

confidence: 99%

Estimating remaining useful life of machine tool ball screws via probabilistic classification

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Zäh

2019

2019 IEEE International Conference on Prognostics and Health Management (ICPHM)

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Ball screws are key components in machine tool linear feed drives since they translate the motors' rotary motion into linear motion. With usage over time, however, tribological degradation of ball screws and the successive loss in preload can cause imprecise position accuracy and loss in manufacturing precision. Therefore condition monitoring (CM) of ball screws is important since it enables just in time replacement as well as the prevention of production stoppages and wasted material. This paper proposes an idea based on a probabilistic classification approach to monitor a ball screw's preload condition with the help of modal parameters identified from vibration signals. It will be shown that by applying probabilistic classification models, uncertainties with respect to degradation can be quantified in an intuitive way and therefore can enhance the basis of decision making. Furthermore, it will be shown how a probabilistic classification approach allows the estimation of remaining useful life (RUL) for ball screws when the user only has access to discrete preload observations.

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“…The Health Index (HI) construction for machines are well studied in the area of PHM. Typically, the HI for machines or critical components is built from the measured data using signal processing techniques, machine learning and data mining techniques to quantify the health status at different times (P. Li et al, 2018 (1) Where is the selected th important feature. In this way, the multi-dimensional features are fused into a single variable as the final system HI.…”

Section: Health Index Constructionmentioning

confidence: 99%

A Deep Learning-Based Method for Cutting Parameter Optimization for Band Saw Machine

et al. 2019

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In the rough machining stage, band saw machines are widely used to cut various raw materials into the required dimensions. The replacement of blade due to the blade degradation accounts for a large part of the total cost of band saw machine users. Therefore, mitigating the blade degradation by dynamically optimizing the cutting parameters can produce great economic benefits and is also a good exploration for smart manufacturing. To achieve this goal, the Convolutional Neural Network (CNN) model is proposed to map the complicated relationship between the cutting parameters and blade degradation. Then a simulation technique is used to search the optimal cutting parameters based on the blade degradation estimation from the model output, which would help alleviate the blade degradation. The proposed optimization method is validated on the data collected during the real manufacturing process of band saw machines. The comparative results demonstrate that the obtained optimal cutting parameters can effectively extend the service life of the blade for a band saw machine.

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Prognosability study of ball screw degradation using systematic methodology

Cited by 84 publications

References 18 publications

Diagnosis of the Hollow Ball Screw Preload Classification Using Machine Learning

Diagnosis of the Hollow Ball Screw Preload Classification Using Machine Learning

Estimating remaining useful life of machine tool ball screws via probabilistic classification

A Deep Learning-Based Method for Cutting Parameter Optimization for Band Saw Machine

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