Double-layer structure of seal coating which consisted of a Ni5Al bond coating and a Ni25 graphite top coating were prepared on steel substrate of gas turbine compressor cylinder block.Bond coating was prepared by atm...Double-layer structure of seal coating which consisted of a Ni5Al bond coating and a Ni25 graphite top coating were prepared on steel substrate of gas turbine compressor cylinder block.Bond coating was prepared by atmospheric plasma spraying and top coating was prepared by flame spraying.The microstructure,mechanical properties and abradability of the coating were characterized by scanning elec-tron microscope(SEM),hardness tester,universal testing machine,thermal shock testing machine and abradability testing machine.The res-ults show that the overall spraying structure of the seal coating is uniform,the nickel metal phase is the skeleton supporting the entire coat-ing,and the coating is well bonded without separation.The seal coating has a bonding strength of not less than 7.7 MPa,excellent thermal stability,and thermal shock resistance cycle numbers at 500℃more than 50;the scratch length,deepest invasion depth and wear amount of the coating increase with rise of test temperature,with almost no coating adhesion,indicating that the seal coating has excellent abradability.展开更多
Compressors play an important role in day-to-day operation in most oil and gas platforms,especially in the case for maintaining gas pressure in transportation pipe.Its complex problem to detect the sensors health and ...Compressors play an important role in day-to-day operation in most oil and gas platforms,especially in the case for maintaining gas pressure in transportation pipe.Its complex problem to detect the sensors health and abnormality as the sensor reading would reflect the various states of the compressor.In ideal situation,sensor readings offer vast amounts of information on compressor health and could possibly indicate early fault of machines.Furthermore,due to harsh site and process operating conditions,sensors are often found to have drifted or failed,and there is no standard methodology to predict abnormality apart from applying emerging industrial smart sensor technologies.In this paper,we investigate a minimalist approach for detecting abnormality of compressor's shaft's RPM sensor.As the sensors in the compressor are correlated,we first use the outputs of other sensors to predict the shaft's RPM using regression-based models(neural networks and multiple linear regression).Second,we calculate the histogram of residuals by taking the difference between the predicted sensor value and the actual sensor value plus the abnormality in terms of bias/miscalibration and noise.The histogram of residuals can be used for sensor abnormality monitoring.In general,sensor states can be monitored by observing the shifting of the mean in the histogram of residuals.The sensor readings contaminated with noise can be seen by a shifted mean whose value is between the normal condition mean and the biased condition mean.This method is compact and would be relevant to monitor irregularity of the sensors.展开更多
基金supported by Zhejiang Provincial Science and Technology Plan Project(Grant No.2022C01118).
文摘Double-layer structure of seal coating which consisted of a Ni5Al bond coating and a Ni25 graphite top coating were prepared on steel substrate of gas turbine compressor cylinder block.Bond coating was prepared by atmospheric plasma spraying and top coating was prepared by flame spraying.The microstructure,mechanical properties and abradability of the coating were characterized by scanning elec-tron microscope(SEM),hardness tester,universal testing machine,thermal shock testing machine and abradability testing machine.The res-ults show that the overall spraying structure of the seal coating is uniform,the nickel metal phase is the skeleton supporting the entire coat-ing,and the coating is well bonded without separation.The seal coating has a bonding strength of not less than 7.7 MPa,excellent thermal stability,and thermal shock resistance cycle numbers at 500℃more than 50;the scratch length,deepest invasion depth and wear amount of the coating increase with rise of test temperature,with almost no coating adhesion,indicating that the seal coating has excellent abradability.
文摘Compressors play an important role in day-to-day operation in most oil and gas platforms,especially in the case for maintaining gas pressure in transportation pipe.Its complex problem to detect the sensors health and abnormality as the sensor reading would reflect the various states of the compressor.In ideal situation,sensor readings offer vast amounts of information on compressor health and could possibly indicate early fault of machines.Furthermore,due to harsh site and process operating conditions,sensors are often found to have drifted or failed,and there is no standard methodology to predict abnormality apart from applying emerging industrial smart sensor technologies.In this paper,we investigate a minimalist approach for detecting abnormality of compressor's shaft's RPM sensor.As the sensors in the compressor are correlated,we first use the outputs of other sensors to predict the shaft's RPM using regression-based models(neural networks and multiple linear regression).Second,we calculate the histogram of residuals by taking the difference between the predicted sensor value and the actual sensor value plus the abnormality in terms of bias/miscalibration and noise.The histogram of residuals can be used for sensor abnormality monitoring.In general,sensor states can be monitored by observing the shifting of the mean in the histogram of residuals.The sensor readings contaminated with noise can be seen by a shifted mean whose value is between the normal condition mean and the biased condition mean.This method is compact and would be relevant to monitor irregularity of the sensors.
