Fault prediction using fuzzy convolution neural network on IoT environment with heterogeneous sensing data fusion - Dharmendra Singh Rajput; Gaurav Meena; Malika Acharya; Krishna Kumar Mohbey (Measurement: Sensors, 2023)
Using multi-source sensing data based on the Internet of things and fusion in conjunction with fuzzy convolutional neural networks to classify and predict mechanical failures has emerged as a very pertinent area of research. This study presents a fuzzy convolutional neural network-based technique to extract and diagnose the defect indicators utilizing the bearing dataset from Case Western Reserve University (CWRU). In addition, the proposed model's effectiveness is evaluated with the help of a model based on a direct convolution neural network. The strategy based on Fuzzification yielded superior results when applied to a fully linked layer for classification. When applied to the bearing dataset, the proposed approach produces an average classification accuracy of 99.87% using the fuzzy layer. The proposed method is evaluated compared to other methods based on machine learning and deep learning. The accuracy achieved using the proposed method is superior to that achieved using the existing approaches in every imaginable working environment. The proposed approach can be used to diagnose mechanical motor faults and provide earlier recommendations.