Research on bearing fault diagnosis technology based on machine learning

Abstract

As industrial equipment complexity continues to rise, the importance of bearings within these systems has become more critical, given their pivotal role in equipment functionality. Bearing faults can result in severe production accidents and safety issues. Hence, there is an urgent need for advanced bearing fault diagnosis technology. This study concentrates on rolling bearings, analyzing their structural characteristics and key parameters to classify fault types—inner race faults, rolling element faults, and outer race faults. Utilizing a dataset of 80 sets of bearing factory data, time and frequency domain analyses are conducted, establishing seven feature parameters (five in the time domain and two in the frequency domain). This data is organized into a 7-dimensional matrix for subsequent analysis and model development. The K-Means algorithm is chosen for its effectiveness in automatically recognizing fault patterns in rolling bearings. Training on the 7-dimensional matrix identifies four clustering centers corresponding to normal conditions, inner race faults, rolling element faults, and outer race faults. The fault diagnosis system is implemented using Python, and algorithm optimization improves efficiency. The study concludes with insights drawn from the analysis and proposes optimization methods, which contributing to advancing bearing fault diagnosis technology, particularly addressing industrial equipment reliability and safety concerns.