Adopting Artificial Neural Network for Wear Investigation of Ball Bearing Materials Under Pure Sliding Condition

Patil, Atul; Desai, Sumit; Patil, Lalit N.; Patil, Sarika A.

doi:10.18485/aeletters.2022.7.2.5

Cited by 13 publications

(15 citation statements)

References 19 publications

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“…Artificial Neural Network (ANN) is a machine learning algorithm that mimics the structure and function of the human brain [ 46 ]. ANNs are composed of interconnected node layers, containing an input layer, one or more hidden layers, and an output layer.…”

Section: Methodsmentioning

confidence: 99%

A New Deep Learning Framework for Imbalance Detection of a Rotating Shaft

Wisal

2023

Sensors

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Rotor unbalance is the most common cause of vibration in industrial machines. The unbalance can result in efficiency losses and decreased lifetime of bearings and other components, leading to system failure and significant safety risk. Many complex analytical techniques and specific classifiers algorithms have been developed to study rotor imbalance. The classifier algorithms, though simple to use, lack the flexibility to be used efficiently for both low and high numbers of classes. Therefore, a robust multiclass prediction algorithm is needed to efficiently classify the rotor imbalance problem during runtime and avoid the problem’s escalation to failure. In this work, a new deep learning (DL) algorithm was developed for detecting the unbalance of a rotating shaft for both binary and multiclass identification. The model was developed by utilizing the depth and efficacy of ResNet and the feature extraction property of Convolutional Neural Network (CNN). The new algorithm outperforms both ResNet and CNN. Accelerometer data collected by a vibration sensor were used to train the algorithm. This time series data were preprocessed to extract important vibration signatures such as Fast Fourier Transform (FFT) and Short-Time Fourier Transform (STFT). STFT, being a feature-rich characteristic, performs better on our model. Two types of analyses were carried out: (i) balanced vs. unbalanced case detection (two output classes) and (ii) the level of unbalance detection (five output classes). The developed model gave a testing accuracy of 99.23% for the two-class classification and 95.15% for the multilevel unbalance classification. The results suggest that the proposed deep learning framework is robust for both binary and multiclass classification problems. This study provides a robust framework for detecting shaft unbalance of rotating machinery and can serve as a real-time fault detection mechanism in industrial applications.

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Section: Methodsmentioning

confidence: 99%

A New Deep Learning Framework for Imbalance Detection of a Rotating Shaft

Wisal

2023

Sensors

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“…The squeeze operation applies global average pooling to convert the data into Z, with a shape of 1 × 1 × C 2 . This process expands the receptive field and encodes the entire spatial feature on a channel into a global feature, as shown in (4).…”

Section: Feature Extractormentioning

confidence: 99%

“…Bearings are critical rotating machinery components and one of these machines' weakest links [1,2]. Their performance directly affects the system's stable operation and production efficiency [3,4]. Taking an aerospace engine as an example, as shown in Figure 1, rolling bearings are easy to start at low temperatures and have small friction losses, wide operating ranges and strong resistance to oil cutoffs; engines use rolling bearings as main bearings [5,6].…”

Section: Introductionmentioning

confidence: 99%

Improved Adversarial Transfer Network for Bearing Fault Diagnosis under Variable Working Conditions

Wang,

Ahmed,

Chen

et al. 2024

Applied Sciences

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Bearings are one of the critical components of rotating machinery, and their failure can cause catastrophic consequences. In this regard, previous studies have proposed a variety of intelligent diagnosis methods. Most existing bearing fault diagnosis methods implicitly assume that the training and test sets are from the same distribution. However, in real scenarios, bearings have been working in complex and changeable working environments for a long time. The data during their working processes and the data used for model training cannot meet this condition. This paper proposes an improved adversarial transfer network for fault diagnosis under variable working conditions. Specifically, this paper combines an adversarial transfer network with a short-time Fourier transform to obtain satisfactory results with the lighter network. Then, this paper employs a channel attention module to enhance feature fusion. Moreover, this paper designs a novel domain discrepancy hybrid metric loss to improve model transfer learning performance. Finally, this paper verifies the method’s effectiveness on three datasets, including dual-rotor, a Case Western Reserve University dataset and the Ottawa dataset. The proposed method achieves average accuracy, surpassing other methods, and shows better domain alignment capabilities.

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“…However, CNNs lack memory and cannot extract dynamic features in data, while LSTM has a limited effect when handling high-dimensional data. However, LSTM must face the challenge of long-term dependence when processing sample sequences that are too long, and it is difficult to identify faults of similar features [ 17 , 18 , 19 ]. Therefore, CNNs and LSTM neural networks are combined to form a CNN-LSTM model, which uses the CNN layer to extract the short-term feature information of the fault adaptively and as the input to the LSTM layer after dimensionality reduction.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Rolling Bearing Based on HPSO Algorithm Optimized CNN-LSTM Neural Network

Tian

Fan

Feng

et al. 2023

Sensors

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The quality of rolling bearings is vital for the working state and rotation accuracy of the shaft. Timely and accurately acquiring bearing status and early fault diagnosis can effectively prevent losses, making it highly practical. To improve the accuracy of bearing fault diagnosis, this paper proposes a CNN-LSTM bearing fault diagnosis model optimized by hybrid particle swarm optimization (HPSO). The HPSO algorithm has a strong global optimization ability and can effectively solve nonlinear and multivariate optimization problems. It is used to optimize and match the parameters of the CNN-LSTM model and dynamically find the optimal value of the parameters. This model overcomes the problem that the parameters of the CNN-LSTM model depend on empirical settings and cannot be adjusted dynamically. This model is used for bearing fault diagnosis, and the accuracy rate of fault diagnosis classification reaches 99.2%. Compared with the traditional CNN, LSTM, and CNN-LSTM models, the accuracy rates are increased by 6.6%, 9.2%, and 5%, respectively. At the same time, comparing the models with different optimization parameters shows that the model proposed in this paper has the highest accuracy. The experimental results verified the superiority of the HPSO algorithm to optimize model parameters and the feasibility and accuracy of the HPSO-CNN-LSTM model for bearing fault diagnosis.

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Adopting Artificial Neural Network for Wear Investigation of Ball Bearing Materials Under Pure Sliding Condition

Cited by 13 publications

References 19 publications

A New Deep Learning Framework for Imbalance Detection of a Rotating Shaft

A New Deep Learning Framework for Imbalance Detection of a Rotating Shaft

Improved Adversarial Transfer Network for Bearing Fault Diagnosis under Variable Working Conditions

Fault Diagnosis of Rolling Bearing Based on HPSO Algorithm Optimized CNN-LSTM Neural Network

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