Rolling bearings are a crucial component of rotating machinery and also one of the parts most prone to failure.
In the recent past, we’ve discussed an array of theoretical and practical knowledge concerning bearings, ranging from fundamental concepts, motor bearing configurations, to potential issues in motor bearings.
From our perspective as writers, understanding the basics of bearings is fundamental, but the application experience of bearings is what we encounter most in our daily work.
Therefore, for engineers, understanding the operating conditions of bearings, or knowing the operating status of bearings, and discerning the trends of bearing operation through these parameters, is an essential path for future development.
With the continuous enhancement of monitoring methods for bearings and mechanical equipment, and thanks to the significant improvement in data acquisition and analysis capabilities in recent times, we can now carry out more proactive and comprehensive monitoring on bearings from a data perspective.
Of course, with the advancement of computer technology, the monitoring technology for bearings will inevitably become increasingly intertwined with the development of Internet+ and big data technologies.
Furthermore, bearings are not only important components in motors but also indispensable in various other equipment, including gear reducers and wind turbines. In fact, bearings are a critical part in any rotating machinery that we can think of.
Furthermore, we will delve into the integration of industrial internet and big data technologies with bearing application techniques. Intelligent manufacturing marks a new direction for industrial development, and we believe you are constantly receiving fresh information about the fusion of the internet and traditional industrial technologies in your daily work.
We hope that our understanding across different fields can provide you with content that aligns more closely with modern industrial development trends.
Bearings, as crucial load-bearing components in rotating machinery, account for nearly 30% of mechanical failures. Therefore, in addition to having a thorough understanding of bearing applications, we must recognize the importance of bearing inspection, as well as the purpose and significance of bearing monitoring through innovative data methods.
Monitoring and Diagnosis: Purpose and Significance
The normal operation of rolling bearings significantly affects the reliability, accuracy, and lifespan of the overall machine. According to related statistics, after implementing fault diagnosis techniques in production, accident rates have dropped by nearly 70%, and maintenance costs have decreased by nearly 40%.
Through the application of bearing condition monitoring technology, we can understand the performance of bearings and conduct early inspection, analysis, and prediction of potential failures. This, in turn, enhances the management standards of the equipment and boosts maintenance efficiency.
The evolution of bearing fault diagnosis
In the early stages, bearing fault diagnosis primarily relied on human hearing. This method is still in use in many companies today, although the tools have been upgraded to devices like electronic stethoscopes.
With technological advancements, an increasing number of vibration instruments are being deployed for the condition monitoring of rolling bearings. These instruments utilize the root mean square (RMS) or peak values of vibration displacement, velocity, and acceleration to determine potential bearing faults. By reducing our reliance on experience, these instruments significantly improve the accuracy of monitoring and diagnosis.
In the 1960s, a bearing company invented a pulse count method to detect bearing damage, significantly enhancing the accuracy and timeliness of rolling bearing failure diagnosis.
The NB series bearing monitors, born in the 70s and 80s, utilized the 1~15kHz range of bearing vibration signals to measure the root mean square and peak of bearing failures. By filtering at low frequencies, sensitivity was improved.
With the development of rolling bearing kinematics and dynamics, a profound understanding was gained on the relationship between frequency components of bearing vibration signals and bearing geometric dimensions and defect types.
The resonance frequency of rolling bodies, vibration of rolling bearings and defects, and the relationship between uneven dimensions and wear are most representative.
Currently, with the continuous updates in monitoring technology and data algorithm technology, we can perform trend analysis of bearing and equipment states based on the monitored time-domain signals. Simultaneously, we can make further judgments and location of bearing failures based on the transformed frequency-domain signals.
As for how these information technologies are applied to the bearing industrial applications, we will continue to update this content. We also hope to explore more new methods of bearing and Internet+ technology integration based on this topic.
