the operating environment of electromechanical equipment is complex, and current methods cannot obtain high-precision fault identification results of electromechanical equipment. In addition, the fault identification time of electromechanical equipment is long and the real-time performance is poor. In order to obtain more ideal fault identification results of electromechanical equipment, a laser sensor based signal acquisition method for electromechanical equipment fault identification is designed. Firstly, a laser degree sensor is used to collect the working status signals of electromechanical equipment, and the working status signals of electromechanical equipment are preprocessed to extract relevant features for fault identification. Then, the features are used as inputs to the machine learning algorithm, and the types of electromechanical equipment faults are used as outputs. A classifier for electromechanical equipment fault identification is established through training. Finally, the performance of electromechanical equipment fault identification is analyzed through specific simulation experiments. The results show that the method proposed in this paper can identify faults in electromechanical equipment with an accuracy of over 95%. The identification time of electromechanical equipment is controlled within 5 seconds, and the overall identification effect is better than the current typical electromechanical equipment fault identification method.