Development of a LeNet-5 Gas Identification CNN Structure for Electronic Noses

Wei, Guangsheng; Li, Gang; Zhao, Jie; He, Anzhi

doi:10.3390/s19010217

Cited by 117 publications

(80 citation statements)

References 18 publications

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“…CNNs are often benchmarked on computer vision tasks but their impacts are far wider-reaching. Recently, the studies of [1] adopted a CNN model to perform gas identification as part of the wider research area of electronic noses (ENs).…”

Section: Introductionmentioning

confidence: 99%

“…When finding such a trade-off, two main approaches exist. (1) The first mechanism is to scale down a large model to fit the constraints of the target device as it seems reasonable to assume that if a large increase in the size of a state-of-the-art model results in a small improvement in accuracy, then a large reduction in model size would result in a small loss of performance. While this is true to an extent, the point at which accuracy starts to drop rapidly occurs while the model is still very large.…”

Section: Introductionmentioning

confidence: 99%

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IoTNet: An Efficient and Accurate Convolutional Neural Network for IoT Devices

Lawrence

Zhang

2019

Sensors

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Two main approaches exist when deploying a Convolutional Neural Network (CNN) on resource-constrained IoT devices: either scale a large model down or use a small model designed specifically for resource-constrained environments. Small architectures typically trade accuracy for computational cost by performing convolutions as depth-wise convolutions rather than standard convolutions like in large networks. Large models focus primarily on state-of-the-art performance and often struggle to scale down sufficiently. We propose a new model, namely IoTNet, designed for resource-constrained environments which achieves state-of-the-art performance within the domain of small efficient models. IoTNet trades accuracy with computational cost differently from existing methods by factorizing standard 3 × 3 convolutions into pairs of 1 × 3 and 3 × 1 standard convolutions, rather than performing depth-wise convolutions. We benchmark IoTNet against state-of-the-art efficiency-focused models and scaled-down large architectures on data sets which best match the complexity of problems faced in resource-constrained environments. We compare model accuracy and the number of floating-point operations (FLOPs) performed as a measure of efficiency. We report state-of-the-art accuracy improvement over MobileNetV2 on CIFAR-10 of 13.43% with 39% fewer FLOPs, over ShuffleNet on Street View House Numbers (SVHN) of 6.49% with 31.8% fewer FLOPs and over MobileNet on German Traffic Sign Recognition Benchmark (GTSRB) of 5% with 0.38% fewer FLOPs.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

IoTNet: An Efficient and Accurate Convolutional Neural Network for IoT Devices

Lawrence

Zhang

2019

Sensors

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“…The performance of different active functions in CNN is discussed in Ref. [24,25]. It is found that there is a problem of gradient disappearance in saturating nonlinear functions.…”

Section: Petalmentioning

confidence: 99%

“…It is found that there is a problem of gradient disappearance in saturating nonlinear functions. The unsaturated nonlinear function can not only solve those problems, but also accelerate the convergence speed and improve the performance of CNN [25][26][27]. Therefore, the appropriate activation function in this paper is selected among Softplus, LReLU, PReLU, RReLU and ELU functions.…”

Section: Petalmentioning

confidence: 99%

Fine-grained fault recognition method for shaft orbit of rotary machine based on convolutional neural network

Wu¹,

Feng²,

Sun³

et al. 2019

J. vibroeng.

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In the fault diagnosis of the shaft orbit of rotating machinery, there are few prejudgments about the severity of the faults, which is very important for fault repair. Therefore, a fine-grained recognition method is proposed to detect different severity faults by shaft orbit. Since different shaft orbits represent different type and different severity of faults, the convolutional neural network (CNN) is applied for identifying the shaft orbits to recognize the type and severity of the fault. The recognition rate of proposed fine-grained fault identification method is 97.96 % on the simulated shaft orbit database, and it takes only 0.31 milliseconds for the recognition of single sample. Experimental result indicated that the classification performance of the proposed method are better than the traditional machine learning models. Moreover, the method is applied for the identification of the measured shaft orbits of rotor with different degree of imbalance faults, and the testing accuracy of the experiments in measured shaft orbits is 97.14 %, which has verified the effectiveness of the proposed fine-grained fault recognition method.

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“…The LeNet-5 network developed by LeCun et al [31] is a classic 2D CNN model, which has been successfully applied to Alzheimer's disease recognition [32], traffic sign recognition [33], facial expression recognition [34], gas recognition [35], pedestrian detection [36] and other fields. Due to LeNet-5 network has a relatively simple structure and a powerful classification capability, this paper employs LeNet-5 network for rolling-element bearing fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Rolling-Element Bearing Fault Diagnosis Using Improved LeNet-5 Network

Wan

Chen

et al. 2020

Sensors

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To address the problems of low recognition accuracy, slow convergence speed and weak generalization ability of traditional LeNet-5 network used in rolling-element bearing fault diagnosis, a rolling-element bearing fault diagnosis method using improved 2D LeNet-5 network is put forward. The following improvements to the traditional LeNet-5 network are made: the convolution and pooling layers are reasonably designed and the size and number of convolution kernels are carefully adjusted to improve fault classification capability; the batch normalization (BN) is adopted after each convolution layer to improve convergence speed; the dropout operation is performed after each full-connection layer except the last layer to enhance generalization ability. To further improve the efficiency and effectiveness of fault diagnosis, on the basis of improved 2D LeNet-5 network, an end-to-end rolling-element bearing fault diagnosis method based on the improved 1D LeNet-5 network is proposed, which can directly perform 1D convolution and pooling operations on raw vibration signals without any preprocessing. The results show that the improved 2D LeNet-5 network and improved 1D LeNet-5 network achieve a significant performance improvement than traditional LeNet-5 network, the improved 1D LeNet-5 network provides a higher fault diagnosis accuracy with a less training time in most cases, and the improved 2D LeNet-5 network performs better than improved 1D LeNet-5 network under small training samples and strong noise environment.Sensors 2020, 20, 1693 2 of 23 clustering [10], back propagation neural network (BPNN) [11], etc. The traditional rolling-element bearing fault diagnosis methods have been widely used, but with the increasing complexity of vibration signals, these methods have a certain limitation; however, the deep learning methods have a greater advantage in analyzing complicated and non-stationary vibration signals.The deep learning methods can automatically extract fault features from vibration signals [12], recently there are many researches are conducted on rolling-element bearing fault diagnosis using deep learning. Yin et al. [13] extracted the original features of vibration signals through time-domain analysis, frequency-domain analysis and wavelet transform, and obtained the low-dimensional features from 38 original features using the nonlinear global algorithm, and the low-dimensional features array is input into the deep belief network (DBN) to evaluate the performance status of rolling-element bearing. Liu et al. [14] obtained the spectrogram of vibration signals through STFT, used the stacked sparse auto-encoder (SAE) to automatically extract fault features, and employed the softmax regression to identify the fault type of rolling-element bearing. Liu et al. [15] used the recurrent neural network (RNN) to classify the faults of rolling-element bearing, and adopted the gated recurrent unit based denoising auto-encoder to enhance fault classification accuracy. Among different deep learning methods, compared with DBN, ...

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Development of a LeNet-5 Gas Identification CNN Structure for Electronic Noses

Cited by 117 publications

References 18 publications

IoTNet: An Efficient and Accurate Convolutional Neural Network for IoT Devices

IoTNet: An Efficient and Accurate Convolutional Neural Network for IoT Devices

Fine-grained fault recognition method for shaft orbit of rotary machine based on convolutional neural network

Rolling-Element Bearing Fault Diagnosis Using Improved LeNet-5 Network

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