Recognition of rolling bearing running state based on genetic algorithm and convolutional neural network

Lu, Wanjie; Mao, Hongpeng; Lin, Fanhao; Chen, Zilin; Fu, Hua; Xu, Ya

doi:10.1177/16878132221095635

Cited by 4 publications

(4 citation statements)

References 8 publications

(8 reference statements)

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“…The method for diagnosing rolling bearing faults, based on deep learning, involves constructing a deep neural network model to automatically extract features and predict classifications of bearing fault data. Common deep learning methods include convolutional neural networks (CNNs) [ 13 ] and recurrent neural networks (RNNs) [ 14 ]. Guo Liang [ 15 ] determined the effectiveness of feature extraction through correlation analysis and utilized the useful feature set as input for an RNN, achieving superior diagnostic performance compared to self-organizing map methods.…”

Section: Introductionmentioning

confidence: 99%

The Fault Diagnosis of Rolling Bearings Is Conducted by Employing a Dual-Branch Convolutional Capsule Neural Network

Lu,

Liu,

Lin

2024

Sensors

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Currently, many fault diagnosis methods for rolling bearings based on deep learning are facing two main challenges. Firstly, the deep learning model exhibits poor diagnostic performance and limited generalization ability in the presence of noise signals and varying loads. Secondly, there is incomplete utilization of fault information and inadequate extraction of fault features, leading to the low diagnostic accuracy of the model. To address these problems, this paper proposes an improved dual-branch convolutional capsule neural network for rolling bearing fault diagnosis. This method converts the collected bearing vibration signals into grayscale images to construct a grayscale image dataset. By fully considering the types of bearing faults and damage diameters, the data are labeled using a dual-label format. A multi-scale convolution module is introduced to extract features from the data and maximize feature information extraction. Additionally, a coordinate attention mechanism is incorporated into this module to better extract useful channel features and enhance feature extraction capability. Based on adaptive fusion between fault type (damage diameter) features and labels, a dual-branch convolutional capsule neural network model for rolling bearing fault diagnosis is established. The model was experimentally validated using both Case Western Reserve University’s bearing dataset and self-made datasets. The experimental results demonstrate that the fault type branch of the model achieves an accuracy rate of 99.88%, while the damage diameter branch attains an accuracy rate of 99.72%. Both branches exhibit excellent classification performance and display robustness against noise interference and variable working conditions. In comparison with other algorithm models cited in the reference literature, the diagnostic capability of the model proposed in this study surpasses them. Furthermore, the generalization ability of the model is validated using a self-constructed laboratory dataset, yielding an average accuracy rate of 94.25% for both branches.

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Section: Introductionmentioning

confidence: 99%

The Fault Diagnosis of Rolling Bearings Is Conducted by Employing a Dual-Branch Convolutional Capsule Neural Network

Lu,

Liu,

Lin

2024

Sensors

Self Cite

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“…CNN-genetic algoritm (CNN-GA), a result of EDL development, plays a crucial role in hyperparameter optimization using a large search space of GA [32], representing a set of hyperparameters in CNN. CNN-GA achieved an average accuracy of 99.85% and training speed four times faster than the LeNet-5 model [33], [34]. Based on these advantages and performance, GA was chosen for hyperparameter optimization in the CNN model.…”

Section: Introductionmentioning

confidence: 99%

Convolutional neural network hyperparameters for face emotion recognition using genetic algorithm

Sam'an,

Safuan,

Munsarif

2024

IJEECS

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The development of artificial intelligence in facial emotion recognition (FER) is rapidly growing and has been widely applied in various fields. Deep learning (DL) techniques with evolutionary algorithms have become the preferred choice for solving various security, health, gaming, and other related problems. This research proposes the use of a genetic algorithm (GA) as the main method to optimize hyperparameters in the convolutional neural network (CNN) model for FER. The required computation time is approximately 37 hours 57 minutes 55 seconds, with generation 3 taking the longest time at around 16 hours 45 minutes 4 seconds. However, generation 3 achieved an accuracy of 76.11%, which is the highest compared to other generations. The results indicate that the more generations are involved, the higher the achievable accuracy. Furthermore, the proposed CNN-GA model in this study outperforms previous models that have been examined. Thus, this study makes a significant contribution to improving the understanding of using GAs to optimize the performance of CNN models for FER.

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“…Lan, et al [15] represent a diagnosis strategy based on operating conditions and pressure pulsation of the turbine in order to effectively monitor the operating state of hydraulic turbines. Lu, et al [16] propos a GA-CNN model to achieve automatic recognition of rolling bearing running state.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent IoT Sensing System for Rail Vehicle Running States Based on TinyML

Zhou

Chen

et al. 2022

IEEE Access

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Real-time identification of the running state is one of the key technologies for a smart rail vehicle. However, it is a challenge to accurately real-time sense the complex running states of the rail vehicle on an Internet-of-Things (IoT) edge device. Traditional systems usually upload a large amount of real-time data from the vehicle to the cloud for identification, which is laborious and inefficient. In this paper, an intelligent identification method for rail vehicle running state is proposed based on Tiny Machine Learning (TinyML) technology, and an IoT system is developed with small size and low energy consumption. The system uses a Micro-Electro-Mechanical System (MEMS) sensor to collect acceleration data for machine learning training. A neural network model for recognizing the running state of rail vehicles is built and trained by defining a machine learning running state classification model. The trained recognition model is deployed to the IoT edge device at the vehicle side, and an offset time window method is utilized for real-time state sensing. In addition, the sensing results are uploaded to the IoT server for visualization. The experiments on the subway vehicle showed that the system could identify six complex running states in real-time with over 99% accuracy using only one IoT microcontroller. The model with three axes converges faster than the model with one. The model recognition accuracy remained above 98% and 95%, under different installation positions on the rail vehicle and the zero-drift phenomenon of the MEMS acceleration sensor, respectively. The presented method and system can also be extended to edgeaware applications of equipment such as automobiles and ships.

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Recognition of rolling bearing running state based on genetic algorithm and convolutional neural network

Cited by 4 publications

References 8 publications

The Fault Diagnosis of Rolling Bearings Is Conducted by Employing a Dual-Branch Convolutional Capsule Neural Network

The Fault Diagnosis of Rolling Bearings Is Conducted by Employing a Dual-Branch Convolutional Capsule Neural Network

Convolutional neural network hyperparameters for face emotion recognition using genetic algorithm

An Intelligent IoT Sensing System for Rail Vehicle Running States Based on TinyML

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