Browse Topic: Fault detection
As per Committee/Henry E. Harschburger recommendations
Rolling element bearing failures form one of rotating equipment's most critical failure modes. Vibration analysis has been successfully used for bearing fault detection and diagnostics but does not estimate the spall length of the bearing. An estimate of the spall length would provide insight into the degrading reliability of a drivetrain as the fault propagates. This would improve the timeliness of scheduling a maintenance action. In this paper, a synthetic tachometer signal is generated from the bearing fault itself. It is synchronous to the rolling element, allowing for a time-domain representation of waveform using the time-synchronous average. From this, an estimate of the length of the bearing fault can be determined.
Sealed electronic components are the basic components of aerospace equipment, but the issue of internal loose particles greatly increases the risk of aerospace equipment. Traditional material recognition technology has a low recognition rate and is difficult to be applied in practice. To address this issue, this article proposes transforming the problem of acquiring material information into the multi-category recognition problem. First, constructing an experimental platform for material recognition. Features for material identification are selected and extracted from the signals, forming a feature vector, and ultimately establishing material datasets. Then, the problem of material data imbalance is addressed through a newly designed direct artificial sample generation method. Finally, various identification algorithms are compared, and the optimal material identification model is integrated into the system for practical testing. The results show that the proposed material
Loose particles are a major problem affecting the performance and safety of aerospace electronic components. The current particle impact noise detection (PIND) method used in these components suffers from two main issues: data collection imbalance and unstable machine-learning-based recognition models that lead to redundant signal misclassification and reduced detection accuracy. To address these issues, we propose a signal identification method using the limited random synthetic minority oversampling technique (LR-SMOTE) for unbalanced data processing and an optimized random forest (RF) algorithm to detect loose particles. LR-SMOTE expands the generation space beyond the original SMOTE oversampling algorithm, generating more representative data for underrepresented classes. We then use an RF optimization algorithm based on the correlation measure to identify loose particle signals in balanced data. Our experimental results demonstrate that the LR-SMOTE algorithm has a better data
ABSTRACT
ABSTRACT
This work introduces the use of "global" stochastic models to detect and identify rotor failures in multicopters under different operating conditions, turbulence, and uncertainty. The identification of an extended class of time-series models known as Vector-dependent Functionally Pooled AutoRegressive models, which are characterized by parameters that depend on both forward velocity and gross weight, using scalar or vector aircraft response signals under white noise excitation has been described. A concise overview of the residual based statistical decision making schemes for fault detection and identification of rotor failures is provided. The scalar and vector statistical models, along with residual variance and residual uncorrelatedness methods were validated and their effectiveness was assessed by a proof-of-concept application to aircraft flight for healthy and faulty states under severe turbulence and intermediate operating conditions. The results of this study demonstrate the
A robust framework for fault detection and identification of rotor degradation in multicopters while effectively rejecting the effects of gusts is introduced. The rotor fault detection and identification methods employed in this study are based on excitation-response signals of the aircraft under ambient turbulence to distinguish between an aircraft response to gusts and rotor faults. A concise overview of the development of statistical time series model for healthy aircraft using the aircraft attitudes as the output and controller commands as the input is presented. This model is utilized to extract quality features for training a simple neural network to perform effective online rotor fault detection and identification in a hexacopter exceptional speed of making a decision and accuracy of fault classification. It is shown that using a statistical time series model assisted neural network employed for online monitoring is capable of rejecting gusts, sensitive to even 20% rotor
Emerging vertical flight concepts being proffered for solutions to the Future Vertical Lift (FVL) mission set such as compound high speed rotorcraft can be designed with multiple, coupled control effectors thus creating redundant systems in one or two more axes to generate control forces and moments which allow for a range of trim states. In the FVL mission area future rotorcraft will be asked to fly into high threat environments where potential failure modes can be encountered due to enemy fire or mechanical failure causing reduction of the safe flight envelope. Fault detection creates options to increase the survivability of the crew and passengers allowing an emergency flight envelope to be proposed. One of the more serious potential failures due to enemy fire is a loss of yaw control. Faults in yaw control can be detected in a compound rotorcraft with a vectored thrust ducted propeller (VTDP) or similar anti-torque thruster. An online Kalman filter (KF) for a dimensional yaw moment
This work introduces the use of statistical time series methods to detect rotor failures in multicopters. A concise overview of the development of various time series models using scalar or vector signals, statistics, and fault detection methods is provided. The fault detection methods employed in this study are based on parametric time series representations and response-only signals of the aircraft state, as the external excitation is non-observable. The comparative assessment of the effectiveness of scalar and vector statistical models and several residual-based fault detection methods are presented in the presence of external disturbances, such as various levels of turbulence and uncertainty, and for different rotor failure scenarios. The results of this study demonstrate the effectiveness of all the proposed residual-based time series methods in terms of prompt rotor fault detection, although the methods based on Vector AutoRegressive (VAR) models exhibit improved performance
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