Fault diagnosis of monoblock centrifugal pumps using pre-trained deep learning models and scalogram images

Abstract

The monoblock centrifugal pump (MCP) is widely utilized in a diverse range of applications encompassing residential and industrial usage. Sectors such as agriculture, civil projects, mine dewatering, and numerous other industrial applications have employed centrifugal pumps. Despite their extensive usage, these pumps are susceptible to faults and failures due to the presence of critical components that are prone to issues such as bearing faults, sealing problems, cavitation and impeller faults. Therefore, conducting timely fault diagnosis becomes crucial to ensure uninterrupted operation. To address this, the technique of transfer learning, a form of deep learning, is employed. This method entails utilizing prior knowledge from previous operations to improve fault diagnostic performance in monoblock centrifugal pumps. Specifically, scalogram images derived from vibration signals collected during experimental setups were used in fault diagnosis. The study classified faults in monoblock centrifugal pumps using fifteen pre-trained networks including DenseNet-201, GoogLeNet, VGG-19, InceptionResNetV2, Xception, ShuffleNet, VGG-16, InceptionV3, ResNet101, ResNet-50, EfficientNetb0, NasNetmobile, ResNet-18, AlexNet and MobileNet-v2. The highest classification accuracy was obtained by carefully adjusting the hyperparameters which were subsequently employed in the fault classification process. AlexNet, one of the pre-trained network models, showcased remarkable capabilities by achieving a perfect classification accuracy of 100% within a relatively fast computation time of 18 s. This approach employs a reliable and effective process for discovering defects from the start, lowering the risk of possible damage and ensuring the seamless operation of the system.