**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
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Rolling bearings are essential components in machinery, playing a critical role in the performance and reliability of mechanical systems. Their condition directly influences the operation quality of equipment, and failures due to bearing issues often lead to frequent downtime. As a result, accurately assessing the condition of rolling bearings has become a key focus for engineers and maintenance technicians involved in condition monitoring.
In industrial settings, one of the most widely used methods for evaluating bearing health is the Shock Pulse Method (SPM). However, this method relies on empirical curves that are typically developed under ideal conditions—such as controlled load and speed. In real-world environments, the operating conditions are complex, involving nonlinear dynamics, unpredictable factors, and varying loads. These challenges make it difficult to apply traditional SPM curves effectively in practical scenarios.
To address these limitations, a self-learning system was developed to adaptively assess and correct the coefficient of bearing condition. This approach integrates artificial neural networks (ANN) with the SPM method, enabling the system to learn from real-time data and adjust its evaluation over time. The system was implemented at the plastics factory of Yangzi Petrochemical Company, where multiple rolling bearings were monitored online using the SPM technique. A self-adjustment coefficient method based on a Backpropagation (BP) neural network was introduced to improve the accuracy of fault detection.
The BP neural network consists of an input layer, a hidden layer, and an output layer. It uses a nonlinear activation function, such as the Sigmoid function, to model complex relationships between high-frequency (dBc) and low-frequency (dBm) values obtained from the SPM sensor. The system is trained using historical data collected from field operators, allowing it to gradually adapt to the specific characteristics of the target machine.
During operation, the BP network continuously evaluates the bearing's condition by comparing measured values with expected thresholds. If discrepancies arise, the network adjusts its internal weights to better reflect the actual state of the bearing. This adaptive learning process ensures that the system becomes more accurate over time, reducing false alarms and improving overall diagnostic reliability.
The implementation of this system at Yangzi Petrochemical proved highly effective. After a year of use, the system significantly reduced unexpected bearing failures, contributing to safer and more efficient operations. The revised SPM curves, adjusted using the BP network, now closely match the real-world conditions of the PP granulation process.
In conclusion, the integration of the SPM method with a self-learning BP neural network provides a powerful solution for monitoring rolling bearings in complex industrial environments. By adapting to the unique characteristics of each machine, the system enhances the accuracy and reliability of bearing condition assessments, ensuring long-term operational stability.
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