Machine Learning in Fault Diagnosis of Electromechanical Devices Using Vibration Measurements

Mechanical engineering is evolving from conventional engineering principles to contemporary electronic mechanical engineering concepts as a result of the significant advancements in science and technology. Mechanical engineering has entered a new stage of growth where the fusion of artificial intelligence technology and mechanical engineering has become a hotspot due to new advancements and breakthroughs. Artificial intelligence is usually seen from the perspective of mechanical and electronic engineering. By using the powerful data processing capabilities of computers, can solve a wide range of challenging issues and boost automation in mechanical and electronic engineering. The fundamental problem with traditional mechanical fault detection is that machines can only identify slight defects because technical conditions are challenging and complex. The utilization process is complicated as a result. This tendency necessitates the constant advancement of equipment fault diagnosis technologies. The field of mechanical engineering has advanced to a new level of development that includes artificial intelligence. The main goal is to categorize mechanical failure analysis using computers’ advanced data processing abilities to deal with various difficult problems in mechanical engineering. ML techniques will be used to analyze the acquired dataset. The Random Forest algorithm produced results at the highest rate of 88.6%.