Misfire Detection in Spark Ignition Engine Using Transfer Learning

Abstract

Misfire detection in an internal combustion engine is an important activity. Any undetected misfire can lead to loss of fuel and power in the automobile. As the fuel cost is more, one cannot afford to waste money because of the misfire. Even if one is ready to spend more money on fuel, the power of the engine comes down; thereby, the vehicle performance falls drastically because of the misfire in IC engines. Hence, researchers paid a lot of attention to detect the misfire in IC engines and rectify it. Drawbacks of conventional diagnostic techniques include the requirement of high level of human intelligence and professional expertise in the field, which made the researchers look for intelligent and automatic diagnostic tools. There are many techniques suggested by researchers to detect the misfire in IC engines. This paper proposes the use of transfer learning technology to detect the misfire in the IC engine. First, the vibration signals were collected from the engine head and plots are made which will work as input to the deep learning algorithms. The deep learning algorithms have the capability to learn from the plots of vibration signals and classify the state of the misfire in the IC engines. In the present work, the pretrained networks such as AlexNet, VGG-16, GoogLeNet, and ResNet-50 are employed to identify the misfire state of the engine. In the pretrained networks, the effect of hyperparameters such as back size, solver, learning rate, and train-test split ratio was studied and the best performing network was suggested for misfire detection.

Ensemble Deep Learning for Wear Particle Image Analysis

“…Furthermore, confusion matrices comparing the predicted and actual classes (i.e., travelled distances) of the testing data in its rows and columns as illustrated in Figure 4a-c were employed to assess the level of prediction of each model. Despite featuring good overall accuracy, the MobileNet V2 featured more than double or even triple the number of misclassifications (12), which indicates a lack of confidence throughout the classification in all four categories (300, 600, 900, and 1200 km), in comparison with Inception V3 (5) and Xception (4). In comparison to the individually trained DL approaches, the ensemble methods combining the three pre-trained deep neural networks featured a superior accuracy of 98.75%, which points towards a higher generalizability of the technique.…”

“…The spatial size of the convolved features could be decreased by the pooling layer, lowering the dimensions allowed to decrease the computational costs of data processing. After the convolutional layer, the output was passed through one or more fully connected layers to perform the classification task [12]. The final output was a probability distribution over the possible classes.…”

Deep learning based fault detection of automobile dry clutch system using spectrogram plots

“…Technique used Mechanical system [23] Recurrent Neural Network [24] Optimized Transfer Learning Bearings [25] Hierarchical diagnosis networks [26] Transfer Learning Internal Combustion Engine [27] Deep Residual Learning Planetary Gearbox [28] Deep Learning Induction Motors [29] Convolution Neural Network Hydraulic Pump [30] Deep Learning Wind turbines a real-world dry friction clutch system, enabling accurate fault diagnosis. The detailed methodology employed for the fault diagnosis of the clutch system is outlined in figure 1.…”

Transfer Learning Based Fault Detection for Suspension System Using Vibrational Analysis and Radar Plots

“…Deep Learning Techniques Used Mechanical System [31] Optimized deep belief network Rolling Bearing [32] Deep convolution neural network [33] Ensemble learning method [34] Stacked auto encoder Gearbox [35] Transfer learning Spark ignition engine [36] Stacked denoising auto encoder Centrifugal Pumps [37] Deep belief network Induction Motor [38] Artificial neural networks Wind Turbines…”