Noise-Driven Approaches to Intelligent Fault Diagnosis of Mechanical Systems

This chapter delves into noise’s crucial role in mechanical early - stage intelligent fault diagnosis, especially in boosting diagnostic accuracy and detection ability. Traditional intelligent fault diagnosis methods often treat noise as a disturbance and adopt noise - elimination strategies to obtain signals. Nevertheless, this chapter presents a new viewpoint, emphasizing the potential of Positive - Incentive (PI) noise. PI noise isn’t just a disturbance but can strengthen weak fault features embedded in noisy signals, thereby enhancing the accuracy, robustness, and environmental adaptability of fault diagnosis. To verify this theory, a new intelligent fault diagnosis model combining multi - channel information fusion and noise injection is proposed and experimentally validated. The model leverages the complementarity of multi - channel data to enhance feature extraction and expression. While noise injection doesn’t suppress model performance, it improves the model’s ability to identify weak fault features. Experimental results show that in hydraulic motor and rolling element bearing fault diagnosis, the noise - injected model achieves up to 4.09% higher diagnostic accuracy than the non - injected one, fully proving PI noise’s positive role. This indicates that strategic noise injection can enhance model robustness and accuracy in practical fault diagnosis tasks. Moreover, this chapter explores multi - channel information fusion’s application potential in mechanical fault diagnosis. By integrating data from different channels, the model captures more fault - information dimensions, thereby significantly improving the diagnostic system’s overall performance, especially in complex mechanical systems. Experimental verification demonstrates the huge advantages of multi - channel data fusion in boosting fault diagnosis accuracy and system stability. Finally, this chapter offers a new perspective on noise - enhanced intelligent fault diagnosis, presents the innovative combination of noise injection and multi - channel information fusion, and gives theoretical and practical guidance for future industrial fault diagnosis system design.