Impact pulse method to evaluate the coefficient of rolling bearing faults

**Impact Pulse Method to Evaluate the Coefficient of Rolling Bearing Faults** Home > Bearing Knowledge > Impact Pulse Method to Evaluate the Coefficient of Rolling Bearing Faults Source: China Bearing Network | Time: 2013-07-29 --- Rolling bearings play a critical role in the machinery industry, with their condition directly influencing the performance and reliability of equipment. Due to the frequent failures caused by bearing issues, the accurate evaluation of bearing health has become a key concern for engineers and maintenance technicians. Among various methods, the Shock Pulse Method (SPM) has been widely used in industrial applications. However, traditional SPM relies on experience-based curves that are often developed under ideal conditions, which may not reflect the complex and variable environments found in real-world operations. Industrial systems are typically nonlinear and multi-dimensional, with numerous uncertain factors that make it difficult to establish precise models. As a result, the SPM method, while useful, struggles to adapt to real-time conditions. To address this challenge, an adaptive approach was introduced, incorporating artificial neural networks to self-adjust the correction coefficients used in bearing fault detection. This paper discusses the application of a self-learning module based on the Backpropagation (BP) neural network. The system was implemented at the plastics factory of Yangzi Petrochemical Company in China, where multiple rolling bearings were monitored online. By integrating operator feedback and historical data, the BP network was trained to adjust the SPM identification curve dynamically, improving the accuracy of fault detection over time. The SPM method measures two key parameters: dBc (Carpet Value), which reflects high-frequency random vibrations, and dBm (Maximum Value), which indicates low-frequency impact pulses. These values are crucial for identifying early signs of bearing wear or damage. However, the relationship between these values is highly dependent on the specific bearing and operating conditions, making it challenging to apply a one-size-fits-all approach. To enhance the system’s adaptability, the BP network was pre-trained using both field data and operator experience. This allowed the model to gradually learn the unique characteristics of each bearing, leading to more accurate fault assessments. After deployment, the system successfully reduced false alarms and improved the reliability of monitoring, contributing to safer and more efficient operation. In conclusion, the integration of the BP neural network with the SPM method provides a powerful solution for real-time bearing condition monitoring. This adaptive approach enables the system to evolve with the changing conditions of the machine, ensuring reliable fault detection even in complex industrial environments. --- **Related Bearing Knowledge:** - Oscillation Motor Imported Bearing Device - Selection of Door Bearings Based on Grease Analysis - Accuracy Grade of Bearings and Self-Smoothing Process - Four Typical Hydrodynamic Bearings and Their Application Environments Link: http://www.chinabearing.net Please cite China Bearing Network: http://www.chinabearing.net Previous: The Reason for the Abnormal Phenomenon of NTN Rolling Bearings – High-Quality Tapered Roller Bearing 32009X

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