Predictive maintenance has emerged as an effective tool for curbing maintenance costs,yet prevailing research predominantly concentrates on the abnormal phases.Within the ostensibly stable healthy phase,the reliance o...Predictive maintenance has emerged as an effective tool for curbing maintenance costs,yet prevailing research predominantly concentrates on the abnormal phases.Within the ostensibly stable healthy phase,the reliance on anomaly detection to preempt equipment malfunctions faces the challenge of sudden anomaly discernment.To address this challenge,this paper proposes a dual-task learning approach for bearing anomaly detection and state evaluation of safe regions.The proposed method transforms the execution of the two tasks into an optimization issue of the hypersphere center.By leveraging the monotonicity and distinguishability pertinent to the tasks as the foundation for optimization,it reconstructs the SVDD model to ensure equilibrium in the model’s performance across the two tasks.Subsequent experiments verify the proposed method’s effectiveness,which is interpreted from the perspectives of parameter adjustment and enveloping trade-offs.In the meantime,experimental results also show two deficiencies in anomaly detection accuracy and state evaluation metrics.Their theoretical analysis inspires us to focus on feature extraction and data collection to achieve improvements.The proposed method lays the foundation for realizing predictive maintenance in a healthy stage by improving condition awareness in safe regions.展开更多
We report a fiber Bragg grating（FBG）-based sensor for the simultaneous measurement of a train bearing’s vibration and temperature. A pre-stretched optical fiber with an FBG and a mass is designed for axial vibratio...We report a fiber Bragg grating（FBG）-based sensor for the simultaneous measurement of a train bearing’s vibration and temperature. A pre-stretched optical fiber with an FBG and a mass is designed for axial vibration sensing. Another multiplexed FBG is embedded in a selected copper-based alloy with a high thermal expansion to detect temperature. Experiments show that the sensor possesses a high resonant frequency of 970 Hz, an acceleration sensitivity of 27.28 pm/g, and a high temperature sensitivity of 35.165 pm/℃. A resonant excitation test is also carried out that demonstrates the robustness and reliability of the sensor.展开更多
基金Supported by Sichuan Provincial Key Research and Development Program of China(Grant No.2023YFG0351)National Natural Science Foundation of China(Grant No.61833002).
文摘Predictive maintenance has emerged as an effective tool for curbing maintenance costs,yet prevailing research predominantly concentrates on the abnormal phases.Within the ostensibly stable healthy phase,the reliance on anomaly detection to preempt equipment malfunctions faces the challenge of sudden anomaly discernment.To address this challenge,this paper proposes a dual-task learning approach for bearing anomaly detection and state evaluation of safe regions.The proposed method transforms the execution of the two tasks into an optimization issue of the hypersphere center.By leveraging the monotonicity and distinguishability pertinent to the tasks as the foundation for optimization,it reconstructs the SVDD model to ensure equilibrium in the model’s performance across the two tasks.Subsequent experiments verify the proposed method’s effectiveness,which is interpreted from the perspectives of parameter adjustment and enveloping trade-offs.In the meantime,experimental results also show two deficiencies in anomaly detection accuracy and state evaluation metrics.Their theoretical analysis inspires us to focus on feature extraction and data collection to achieve improvements.The proposed method lays the foundation for realizing predictive maintenance in a healthy stage by improving condition awareness in safe regions.
基金supported in part by the National Natural Science Foundation of China(Nos.51605348 and 51605344)in part by the Natural Science Foundation of the Hubei Province(No.2016CFB116)in part by the Open Research Fund of the Hubei Digital Manufacturing Key Laboratory(No.SZ1801)
文摘We report a fiber Bragg grating（FBG）-based sensor for the simultaneous measurement of a train bearing’s vibration and temperature. A pre-stretched optical fiber with an FBG and a mass is designed for axial vibration sensing. Another multiplexed FBG is embedded in a selected copper-based alloy with a high thermal expansion to detect temperature. Experiments show that the sensor possesses a high resonant frequency of 970 Hz, an acceleration sensitivity of 27.28 pm/g, and a high temperature sensitivity of 35.165 pm/℃. A resonant excitation test is also carried out that demonstrates the robustness and reliability of the sensor.
