Automatic fault monitoring using acoustic emissions
نویسندگان
چکیده
Techniques for automatic monitoring of faults in machinery are being considered as a means to safely simplify or dispense with expensive periodic fault inspection procedures. This paper presents results from an ongoing investigation into the feasibility of using Acoustic Emissions (AEs) for automatic detection of microcrack formation/growth in machine components.
منابع مشابه
Condition Monitoring and Management from Acoustic Emissions
This paper analyses acoustic emission energy signals acquired under mixed load conditions with one induced fault. With Mean field independent components analysis is applied to an observation matrix build from successive acoustic emission energy revolution signals. The paper presents novel results that provide remarkable automatic grouping of the observed signals equivalent to the grouping obtai...
متن کاملAcoustic condition monitoring of wind turbines: tip faults
As a significant and growing source of the world’s energy, wind turbine reliability is becoming a major concern. At least two fault detection techniques for condition monitoring of wind turbine blades have been reported in early literature, i.e. acoustic emissions and optical strain sensors. These require off-site measurement. The work presented here offers an alternative non-contact fault dete...
متن کاملPreventive Maintenance of Centralized HVAC Systems: Use of Acoustic Sensors, Feature Extraction, and Unsupervised Learning
In this paper, we propose a predictive maintenance scheme for centralized HVAC systems by autonomous monitoring and analyzing their acoustic emissions. Our proposed solution allows a building to be retrofitted to monitor its HVAC without having to modify the existing infrastructure. Our approach is to employ an energy-efficient, low-cost, and distributed acoustic sensing platform to capture and...
متن کاملVery High Frequency Monitoring System for Engine Gearbox and Generator Health Management
In cooperation with the major propulsion engine manufacturers, the authors are developing and demonstrating a unique very high frequency (VHF) vibration monitoring system that integrates various vibroacoustic data with intelligent feature extraction and fault isolation algorithms to effectively assess engine gearbox and generator health. The system is capable of reporting on the early detection...
متن کاملSelf-organizing map neural network for transient signal classification in mechanical diagnostics
Acoustic Emissions (AE), generated by the formation and growth of micro-cracks in metal components, provide us with a promising mechanical fault detection technique in monitoring complex-shaped components in helicopters and aircraft. A major challenge for an AE-based fault detection algorithm is to distinguish crack related AE signals from other interfering transient signals, such as fretting r...
متن کامل