回转支承监测方法及其在起重机中的应用和存储介质
By combining real-time audio and vibration monitoring with neural network analysis, the prediction error problem of slewing bearing monitoring in existing technologies has been solved, enabling reliable judgment of the slewing bearing status and adaptive lubrication, thus ensuring the safe operation of the crane.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- SPECIAL EQUIP SAFETY SUPERVISION INSPECTION INST OF JIANGSU PROVINCE
- Filing Date
- 2022-12-02
- Publication Date
- 2026-07-17
AI Technical Summary
Existing slewing bearing monitoring methods cannot effectively predict their failures or service life, and rely on operator experience, leading to inaccurate predictions and failing to guarantee the safe and reliable operation of crane slewing bearings.
By acquiring the drive signal and ambient sound of the slewing bearing in real time, audio monitoring data is generated, which is then processed for noise reduction before detection. Combined with vibration monitoring, detection results are generated and lubricant is added. The bearing condition is judged using neural networks and fitting analysis, and abnormal information is output.
This improves the reliability and reference value of slewing bearing monitoring, avoids misjudgment of occasional abnormal data, enables adaptive lubricant filling, and further determines whether the bearing's working condition is abnormal.
Smart Images
Figure CN116142972B_ABST