A Review on Convolutional Neural Network in Bearing Fault Diagnosis

Waziralilah, N. Fathiah; Abu, Aminudin; Lim, Meng Hee; Quen, Lee Kee; Elfakharany, Ahmed

doi:10.1051/matecconf/201925506002

Cited by 36 publications

(22 citation statements)

References 47 publications

(70 reference statements)

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“…The next is the pooling or sub-sampling layer for downsampling the features and merging semantically similar features into one. This layer reduces the dimension and parameter of the network [23]. The two commonly applied pooling operations are average pooling, which determines the average value of each patch on the activation map, and maximum pooling (or max pooling), which determines the maximum value for each patch of the feature map.…”

Section: Amentioning

confidence: 99%

CNN-Based Fault Detection for Smart Manufacturing

Neupane¹,

Kim

Seok

et al. 2021

Applied Sciences

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A smart factory is a highly digitized and networked production facility based on smart manufacturing. A smart manufacturing plant is the result of intelligent systems deployed in the factory. Smart factories have higher production volumes and are prone to machine failures when operating in almost all applications on a daily basis. With the growing concept of smart manufacturing required for Industry 4.0, intelligent methods for detecting and classifying bearing faults have become a subject of scientific research and interest. In this paper, a deep learning-based 1-D convolutional neural network is proposed using the time-sequence bearing data from the Case Western Reserve University (CWRU) bearing database. Four different sets of data are used. The proposed method achieves state-of-the-art accuracy even with a small amount of training data. For the sensitivity analysis of the proposed method, metrics such as precision, recall, and f-measure are determined. Next, we compare the proposed method with a 2-D CNN that uses two-dimensional image illustrations of raw data as input. This method shows the effectiveness of using 1-D CNNs over 2-D CNNs for time-sequence data. The proposed method is computationally inexpensive and outperforms the most complex and computationally intensive algorithms used for bearing fault detection and diagnosis.

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Section: Amentioning

confidence: 99%

CNN-Based Fault Detection for Smart Manufacturing

Neupane¹,

Kim

Seok

et al. 2021

Applied Sciences

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“…In another study, advance analytics and deep learning techniques have been presented with application to smart manufacturing [13]. A convolution neural network approach has been adopted to predict the bearing fault in a study presented by Waziralilah [14] et al A data-driven technique based on deep belief network has been investigated to predict the material removal rate [15]. A data analytics based predictive model has been developed to predict power consumption in manufacturing [16].…”

Section: Heatmentioning

confidence: 99%

“…The terms , , , , , and in the above ( 12) and (13) represents loss function, target value (output), predicted value, learning rate, number of leaf in the tree, regularization parameter and weight of the leaf respectively [23]. The loss function expressed in above ( 12) is mathematically defined in the forms of mean square error as shown in (14).…”

Section: E Extreme Gradient Boosting (Xgboost)mentioning

confidence: 99%

Real-Time Lime Quality Control through Process Automation

Tiwari¹,

Choudhary²,

Singh³

et al. 2021

IJESE

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In the steel industry - Tata steel, India, most of the lime produced in the lime plant is used in the steel-making process at LD shops. The quality of steel produced at LD shops depends on the quality of lime used. Moreover, the lime also helps in the crucial dephosphorization process during steel-making. The calcined lime produced in the lime plant goes to the laboratory for testing its final quality (CaO%), which is very difficult to control. To predict, control and enhance the quality of lime during lime making process, five machine-learning-based models such as multivariate linear regression, support vector machine, decision tree, random forest and extreme gradient boosting have been developed using different algorithms. Python has been used as a tool to integrate the algorithms in the models. Each model has been trained on the past 14 months’ data of process parameters, collected from level 1 sensor devices, to predict the future quality of lime. To boost the model’s prediction performance, hyper-parameter tuning has been performed using grid-search algorithm. A comparative study has been done among all the models to select a final model with the least root mean square error in predicting and control future lime quality. After the comparison, results show that the model incorporating support vector machine algorithm has least value of root mean square error of 1.23 in predicting future lime quality. In addition to this, a self-learning approach has also been incorporated into support vector machine model to enhance its performance further in realtime. The result shows that the performance has been boosted from 85% strike-rate in +/-2 error range to 90% of strike-rate in +/-1 error range in real-time. Further, the above predictive model has been extended to build a control model which gives prescriptions as output to control the future quality of lime. For this purpose, a golden batch of good data has been fetched which has shown the best quality of lime (≥ 94% of CaO%). A good range of process parameters has been extracted in the form of upper control limit and lower control limit to tune the set-points and to give the prescriptions to the user. The integration of these two models (Predictive model and control model) helps in controlling the quality of lime 12 hours before its final production of lime in lime plant. Results show that both models (Predictive model and control model) have 90% of strike-rate within +/-1 of error in real-time. Finally, a human machine interface has been developed to facilitate the user to take action based on control model’s output. Eventually this work is deployed as a lime making process automation to predict and control the lime quality.

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“…The conventional fault diagnosis approach employs various signal processing techniques to analyze the complex dynamic characteristics of the planetary gearbox from vibration signals [4]. However, with rapid advances in deep learning technology, there has been numerous research that enabled the fault diagnosis of the gearbox with the minimum requirement of signal processing methods [5], [6].…”

Section: Introductionmentioning

confidence: 99%

A Health Data Map-Based Ensemble of Deep Domain Adaptation Under Inhomogeneous Operating Conditions for Fault Diagnosis of a Planetary Gearbox

Youn

2021

IEEE Access

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A deep learning model trained under a specific operating condition of the gearbox often experiences an overfitting problem, which makes it impossible to diagnose faults under different operating conditions. To solve this problem, this paper proposes an ensemble of deep domain adaptation approaches with a health data map. As a fundamental approach to alleviate the domain shift problem due to inhomogeneous operating conditions, the vibration signal is transformed into an image-like simplified health data map that visualizes a tooth-wise fault of the gearbox. The simplified health data map enables the use of a conventional convolutional neural network (CNN) model. To solve the remaining domain shift problem even with the simplified health data map, this study employs a maximum classifier discrepancy (MCD), which is a typical domain adaptation method. To further enhance its performance, a discrepancyscale factor-based MCD and its ensemble approach are proposed. The proposed method is demonstrated with a 2 kW planetary gearbox testbed operated under stationary and non-stationary speed conditions. The results present that the proposed method outperforms conventional CNN and MCD even under the inhomogeneous operating condition of the gearbox.

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A Review on Convolutional Neural Network in Bearing Fault Diagnosis

Cited by 36 publications

References 47 publications

CNN-Based Fault Detection for Smart Manufacturing

CNN-Based Fault Detection for Smart Manufacturing

Real-Time Lime Quality Control through Process Automation

A Health Data Map-Based Ensemble of Deep Domain Adaptation Under Inhomogeneous Operating Conditions for Fault Diagnosis of a Planetary Gearbox

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