Composite materials are increasingly used in the aerospace industry.To fully realise the weight saving potential along with superior mechanical properties that composites offer in safety critical applications,reliable...Composite materials are increasingly used in the aerospace industry.To fully realise the weight saving potential along with superior mechanical properties that composites offer in safety critical applications,reliable Non-Destructive Testing(NDT)methods are required to prevent catastrophic failures.This paper will review the state of the art in the field and point to highlight the success and challenges that different NDT methods are faced to evaluate the integrity of critical aerospace composites.The focus will be on advanced certificated NDT methods for damage detection and characterization in composite laminates for use in the aircraft primary and secondary structures.展开更多
Acoustic emission(AE) has been studied for monitoring the condition of mechanical seals by many researchers, however to the best knowledge of the authors, typical fault cases and their effects on tribological behaviou...Acoustic emission(AE) has been studied for monitoring the condition of mechanical seals by many researchers, however to the best knowledge of the authors, typical fault cases and their effects on tribological behaviour of mechanical seals have not yet been successfully investigated. In this paper, AE signatures from common faults of mechanical seals are studied in association with tribological behaviour of sealing gap to develop more reliable condition monitoring approaches. A purpose-built test rig was employed for recording AE signals from the mechanical seals under healthy and faulty conditions. The collected data was then processed using time domain and frequency domain analysis methods. The study has shown that AE signal parameters: root mean squared(RMS) along with AE spectrum, allows fault conditions including dry running, spring out and defective seal faces to be diagnosed under a wide range of operating conditions. However, when mechanical seals operate around their transition point, conventional signal processing methods may not allow a clear separation of the fault conditions from the healthy baseline. Therefore an auto-regressive(AR) model has been developed on recorded AE signals to classify different fault conditions of mechanical seals and satisfactory results have been perceived.展开更多
基金the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this researchsupported by EPSRC grant EP/R002495/1the European Metrology Research Programme through grant 17IND08。
文摘Composite materials are increasingly used in the aerospace industry.To fully realise the weight saving potential along with superior mechanical properties that composites offer in safety critical applications,reliable Non-Destructive Testing(NDT)methods are required to prevent catastrophic failures.This paper will review the state of the art in the field and point to highlight the success and challenges that different NDT methods are faced to evaluate the integrity of critical aerospace composites.The focus will be on advanced certificated NDT methods for damage detection and characterization in composite laminates for use in the aircraft primary and secondary structures.
文摘Acoustic emission(AE) has been studied for monitoring the condition of mechanical seals by many researchers, however to the best knowledge of the authors, typical fault cases and their effects on tribological behaviour of mechanical seals have not yet been successfully investigated. In this paper, AE signatures from common faults of mechanical seals are studied in association with tribological behaviour of sealing gap to develop more reliable condition monitoring approaches. A purpose-built test rig was employed for recording AE signals from the mechanical seals under healthy and faulty conditions. The collected data was then processed using time domain and frequency domain analysis methods. The study has shown that AE signal parameters: root mean squared(RMS) along with AE spectrum, allows fault conditions including dry running, spring out and defective seal faces to be diagnosed under a wide range of operating conditions. However, when mechanical seals operate around their transition point, conventional signal processing methods may not allow a clear separation of the fault conditions from the healthy baseline. Therefore an auto-regressive(AR) model has been developed on recorded AE signals to classify different fault conditions of mechanical seals and satisfactory results have been perceived.
