Since modern times,Chinas industry has developed rapidly,showing a trend of scale,complexity,and integration.The long-term,stable,and efficient operation of mechanical equipment is the key to the rapid development of industry.Mechanical equipment is prone to sudden shutdowns due to long-term operation,reducing the economic benefits of the factory,and even posing a threat to human life safety.Therefore,mechanical equipment fault detection is of great significance for the development of Chinas industry and the improvement of economic benefits.At present,the common fault detection methods are mainly manual detection and vibration monitoring.Manual detection and inspection cycles are long,faults cannot be detected in a timely manner,and subjectivity is strong.Vibration detection requires high installation requirements for detection equipment.Because the sound of mechanical equipment changes when it is damaged,and the collection method of sound signals is non-contact,which is convenient for installation,sound signals are gradually applied to the field of fault detection of mechanical equipment.Fault detection technology based on sound signals can detect mechanical equipment faults in a timely manner,repair them in a timely manner,and reduce economic losses.The research progress of mechanical equipment in the field of fault detection both domestically and internationally were reviewed from the perspectives of machine learning and deep learning,and proposed the future research focus and reference direction of mechanical equipment fault detection technology based on acoustic signals,aiming to provide reference for fault diagnosis of mechanical equipment.