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.

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Clutch is an essential component in an automotive transmission system that helps in power transmission from the engine to the gearbox. Continuous operation of the clutch leads to degradation, damage, and reduced lifetime of internal components. Such factors can provoke the occurrence of various faults, which left unmonitored will result in clutch damage and seizure. Hence, continuous monitoring of the clutch is necessary to minimize unwanted breakdowns. This paper presents a condition monitoring technique based on vibration analysis to monitor the fault occurrence of different components. A machine learning approach is carried out to classify various faults. Such as fingers worn, pressure plate broken, pressure plate worn, friction material loss, and tangential strip bent. Feature extraction is performed on the acquired vibration signals using statistical learning process. The most important and significant features are selected from the extracted features by J48 decision tree algorithms. Further, feature classification is done with the help of Bayes based classifiers and the obtained results are compared to predict the best in class classifier for real-time.

The performance of photovoltaic modules (PVMs) degrades due to the occurrence of various faults such as discoloration, snail trail, burn marks, delamination, and glass breakage. This degradation in power output has created a concern to improve PVM performance. Automatic inspection and condition monitoring of PVM components can handle performance-related issues, especially for installed capacity where no trained personnel are available at the location. This paper describes a deep learning-based technique involving convolutional neural networks (CNNs) to extract features from aerial images obtained from unmanned aerial vehicles (UAVs) and classify various types of fault occurrences using cloud computing and Internet of things (IoT). The algorithm used demonstrates a binary classification with high accuracy by comparing individual faults with good condition. Efficient and effective fault detection can be observed from the results obtained.

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