A deep learning-based recognition method for degradation monitoring of ball screw with multi-sensor data fusion

Zhang, Li; Gao, Hongli; Wen, Juan; Li, Shichao; Liu, Qi

doi:10.1016/j.microrel.2017.03.038

Cited by 62 publications

(36 citation statements)

References 38 publications

(45 reference statements)

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“…It has been shown that a deep belief network can achieve lower error rate compared to traditional methods in fault patterns classification [30]. Hence, using the advantages of both WPA and DBN ICR , we propose a hybrid leakage aperture identification method.…”

Section: Leak Apertures Recognition Based On Wpa Andmentioning

confidence: 99%

Pipeline Leak Aperture Recognition Based on Wavelet Packet Analysis and a Deep Belief Network with ICR

Lang

et al. 2018

Wireless Communications and Mobile Computing

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The leakage aperture cannot be easily identified, when an oil pipeline has small leaks. To address this issue, a leak aperture recognition method based on wavelet packet analysis (WPA) and a deep belief network (DBN) with independent component regression (ICR) is proposed. WPA is used to remove the noise in the collected sound velocity of the ultrasonic signal. Next, the denoised sound velocity of the ultrasonic signal is input into the deep belief network with independent component regression (DBN ICR ) to recognize different leak apertures. Because the optimization of the weights of the DBN with the gradient leads to a local optimum and a slow learning rate, ICR is used to replace the gradient fine-tuning method in conventional DBN for improving the classification accuracy, and a Lyapunov function is constructed to prove the convergence of the DBN ICR learning process. By analyzing the acquired ultrasonic sound velocity of different leak apertures, the results show that the proposed method can quickly and effectively identify different leakage apertures.

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Section: Leak Apertures Recognition Based On Wpa Andmentioning

confidence: 99%

Pipeline Leak Aperture Recognition Based on Wavelet Packet Analysis and a Deep Belief Network with ICR

Lang

et al. 2018

Wireless Communications and Mobile Computing

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“…The flowchart of the RUL prediction method based on PF algorithm is shown in Figure 12. The initial prediction point of ball screw is defined as the 120th point (120000 strokes), and the failure threshold of ball screw is set as when the amplitude of the acceleration signal overpassed 20g [15]. The PF algorithm is utilized to integrate the degradation model with measured dataset for parameters updating and RUL prediction after the degradation starts.…”

Section: Ball Screw Rul Prediction Using the Proposed Degradationmentioning

confidence: 99%

“…Liu et al [14] proposed an enhanced recurrent neural network to predict the RUL of lithium-ion battery. Zhang et al [15] presented a degradation recognition method based on deep belief networks and multisensor data fusion to monitor the degradation of ball screw. Machine learning-based method could be beneficial for complex machine whose mechanical principles are not straightforward so that developing an accurate model is impossible.…”

Section: Introductionmentioning

confidence: 99%

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

Zhang

Gao

Dong

et al. 2018

Mathematical Problems in Engineering

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Ball screw is a kind of precise transmission element in drive system of machine tool. In this paper, the degradation model of ball screw is proposed based on wear calculation-based degradation analysis and experimental data-based validation. At first, fatigue wear is analyzed to be the predominant degradation mode of ball screw. The wear volume formula of ball screw is derived as the function of working load and stroke number. Secondly, the degradation rate of ball screw is analyzed to be affected by the total degradation and wear rate. Based on this finding, the degradation model of ball screw is theoretically derived as an exponential model by inputting wear volume formula. Thirdly, experimental data-based cross-validation method is proposed to validate the exponential degradation model. Determination coefficients are calculated to evaluate the fitting degree between the degradation model and real degradation path. Next, run-to-failure test of ball screw is carried out to collect experimental data in different working conditions. The average determination coefficient of different working conditions is calculated as 0.7848, which indicates that the proposed model can well fit the actual degradation path. In addition, the proposed model is applied to predict remaining useful life (RUL) of the tested ball screw by using collected data. RUL is estimated in a high and stable accuracy after 168000 strokes. For further validation, comparison with linear model is performed. All results show that the exponential degradation model is reasonable and correct in reflecting the degradation process of ball screw.

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“…In these methods, the tensor structure of spatial-temporal data are considered and more effective representation can be extracted [11,12]. Although these methods have desirable performance in processing spatial-temporal data, they focus separately on how to extract useful features from raw data and construct an effective identification model of an abnormal process [13]. If extracted features cannot interpret abnormal processes sufficiently or the identification model does not understand the extracted features, the performance is not robust.…”

Section: Introductionmentioning

confidence: 99%

Identification of Abnormal Processes with Spatial-Temporal Data Using Convolutional Neural Networks

et al. 2020

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Identifying abnormal process operation with spatial-temporal data remains an important and challenging work in many practical situations. Although spatial-temporal data identification has been extensively studied in some domains, such as public health, geological condition, and environment pollution, the challenge associated with designing accurate and convenient recognition schemes is very rarely addressed in modern manufacturing processes. This paper proposes a general recognition framework for identifying abnormal process with spatial-temporal data by employing a convolutional neural network (CNN) model. Firstly, motivated by the pasting case study, the spatial-temporal data are transformed into process images for capturing spatial and temporal interrelationship. Then, the CNN recognition model is presented for identifying different types of these process images, leading to the identification of abnormal process with spatial-temporal data. The specific architecture parameters of CNN are determined step by step. According to the performance comparison with alternative methods, the proposed method is able to accurately identify the abnormal process with spatial-temporal data.

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A deep learning-based recognition method for degradation monitoring of ball screw with multi-sensor data fusion

Cited by 62 publications

References 38 publications

Pipeline Leak Aperture Recognition Based on Wavelet Packet Analysis and a Deep Belief Network with ICR

Pipeline Leak Aperture Recognition Based on Wavelet Packet Analysis and a Deep Belief Network with ICR

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

Identification of Abnormal Processes with Spatial-Temporal Data Using Convolutional Neural Networks

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