Wear Calculation-Based Degradation Analysis and Modeling for Remaining Useful Life Prediction of Ball Screw

Zhang, Li; Gao, Hongli; Dong, Dawei; Fu, Guoqiang; Liu, Qi

doi:10.1155/2018/2969854

Cited by 7 publications

(6 citation statements)

References 40 publications

(57 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For non-Gaussian problems, sequential Monte-Carlo methods, or particle filters (PFs), are proposed [132]. From a machine tool perspective, PFs are used for the prognosis and RUL calculation of ball screws [112][113][114].…”

Section: Stochastic Modelsmentioning

confidence: 99%

“…In Ref. [113], the degradation process of a ball screw is fit to an exponential model using a Weibull distribution shape parameter [113]. Likewise, an exponential Wiener process is used to predict the RUL [112].…”

Section: Stochastic Modelsmentioning

confidence: 99%

“…Linear encoder [111] -KF [111] Prognosis/RUL -Vibration [112][113][114] Exponential [112,113], PF [112][113][114] Sensing methods/ technologies -Vibration [115] K F [ 115] Other methods Rotary encoder [116], linear encoder [116], torque [116], NC data [117] -Adaptive estimation [116], parameter estimation [117] Linear guide/slide Wear/degradation Current [118] Vibration [119], strain [64], capacitance [119] Analytic [62,64,118,119] Preload and force -Vibration [102,120], strain [63], piezoresistive [62] EMA [102], OMA [120], empirical [63],…”

Section: Fault Detection/ Diagnosismentioning

confidence: 99%

“…Zhang et al [113] present an exponential model of a ball screw degradation process that attempts to quantify the amount of wear. The model uses the wear volume of the ball screw which is derived as a function of the axial load and stroke number.…”

Section: Prognosis and Remaining Useful Lifementioning

confidence: 99%

See 3 more Smart Citations

Condition Monitoring of Machine Tool Feed Drives: A Review

Butler

Ziada

Stephenson

et al. 2022

Journal of Manufacturing Science and Engineering

View full text Add to dashboard Cite

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.

show abstract

Section: Stochastic Modelsmentioning

confidence: 99%

Section: Stochastic Modelsmentioning

confidence: 99%

Section: Fault Detection/ Diagnosismentioning

confidence: 99%

Section: Prognosis and Remaining Useful Lifementioning

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

View full text Add to dashboard Cite

show abstract

“…Mechanical intelligent fault diagnosis methods are used to extract the hidden fault characteristics from the monitoring signals [4], [5] and automatically identify the health condition of machinery through intelligent algorithm, which are current researched hotspot in the field of fault diagnosis. Since Hinton [6] first proposed the concept of ''deep learning'' in 2006, deep learning has become an emerging researched hotspot in The associate editor coordinating the review of this manuscript and approving it for publication was Youqing Wang . academia and industry, and deep neural networks have also been successfully applied in different engineering fields, such as image recognition [7], text analysis [8], speech recognition [9], fault diagnosis [10]- [12] and remaining useful life prediction [13]- [15]. Jing et al [10] proposed a fault diagnosis method based on convolutional neural network, which learns features from the frequency domain data of the original vibration signals.…”

Section: Introductionmentioning

confidence: 99%

Deep Cost Adaptive Convolutional Network: A Classification Method for Imbalanced Mechanical Data

et al. 2020

Self Cite

View full text Add to dashboard Cite

Intelligent diagnosis is an important manner for mechanical fault diagnosis in the era of industrial big data, and deep network has received extensive attention in this field because of automatically learning features and classifying entered samples. As a classic deep learning model, Convolutional Neural Network has been applied in mechanical intelligent fault diagnosis. However, the limitation is that entered samples must be balanced to achieve satisfactory recognition rate. During the operation of machinery, the normal samples are abundant and the fault samples are rare. Therefore, the recognition rate of the minority category is minor when processing the imbalanced data with Convolutional Neural Network. To solve the above problem, an intelligent classification method for imbalanced mechanical data based on Deep Cost Adaptive Convolutional Network is proposed. According to this model, first, it learns intrinsic state characteristics in mechanical raw signals through multiple convolution and pooling operations. Second, it maps these characteristics to mechanical health condition by fully connected layers. Finally, the cost adaptive loss function adaptively assigns different misclassification costs for all categories and keeps updating them in training process to effectively classify the imbalanced mechanical data. The proposed method is verified by bearing data and milling cutter data with different imbalanced ratio, and compared with other methods. The experimental results show that the proposed method is robust and is able to effectively classify the imbalanced mechanical data.

show abstract