This paper presents a new sensory system based on advanced algorithms and machine learning techniques that provides sensory gloves with the ability to ensure real-time connection of all connectors in the cabling of a cockpit module. Besides a microphone, the sensory glove also includes a gyroscope and three accelerometers that provide valuable information to allow the selection of the appropriate signal time windows recorded by the microphone of the glove. These signal time windows are subsequently analyzed by a convolutional neural network, which indicates whether the connection of the components has been made correctly or not. The development of the system, its implementation in a production industry environment and the results obtained are analyzed.
Keywords: automotive industry; convolutional neural networks; electronics; industry 4.0; machine learning; sensory gloves; smart sensing.