An Experimental Case Study for the Course of ‘Testing Technology and Data Processing’

‘Testing Technology and Data Processing (TTDP)’ is one of the core courses for the undergraduates in mechanical engineering subject. This paper designs an experimental case to improve the students’ abilities in signal acquisition, preprocessing, feature extraction, and artificial intelligence (AI)-based pattern recognition. The case study is based on an internet of things (IoT) node that integrating with accelerometer, microphone, and magnetic sensors. The order tracking algorithm and a double-layer bidirectional long short-term memory (DBiLSTM) model are used to process the multi-sensor data for condition monitoring and fault diagnosis of a motor. The students’ feedback demonstrates that the designed case improves their interests to this course, and also improves their abilities in engineering practice.