Abdel Bayoumi

Finite element simulation of high-pressure water-jet assisted metal cutting

Abstract Experimental studies have shown that improved metal cutting efficiency can be obtained when a high-pressure water/coolant jet is injected at the tool–chip interface. The pressure exerted on the chip face by the jet is expected to reduce, for example, friction along the tool–chip interface, temperature rise in the chip and the workpiece, the cutting force, and residual stress in the finished workpiece, leading to a longer tool life and a better surface integrity for the finished workpiece. This paper presents the results of finite element simulations of high-pressure water-jet assisted orthogonal metal cutting, in which the water jet is injected directly into the tool–chip interface through a small hole on the rake face of the tool. The mechanical effect of the high-pressure water jet is approximated as a pressure loading at the tool–chip interface. The frictional interaction along the tool–chip interface is modeled by using a modified Coulomb friction law. Chip separation is modeled by a nodal release technique and is based on a critical stress criterion. The effect of temperature, strain rate and large strain is considered. Cooling effect of the high-pressure jet on the temperature distribution is modeled with a convective heat-transfer coefficient. The effect of water jet hole position and pressure is studied. Contour plots showing the distributions of steady-state temperature and stress and the residual stress are presented. The simulation results show a reduction in temperature, the cutting force and residual stresses for water-jet assisted cutting conditions. The mechanical effect of the water jet is found to reduce the contact pressure and shear stress along the tool–chip interface and also the contact zone length for certain water jet hole locations.

Quadratic-Nonlinearity Index Based on Bicoherence and its Application in Condition Monitoring of Drive-Train Components

A new concept of the quadratic-nonlinearity power-index spectrum, QNLPI(f), that can be used in signal detection and classification, is proposed based on the bicoherence spectrum. The proposed QNLPI(f) is derived as a projection of the 3-D bicoherence spectrum into 2-D spectrum that quantitatively describes how much of the mean square power at a certain frequency f is generated by nonlinear quadratic interaction between different frequencies. The proposed index, QNLPI(f), can be used to simplify the study of bispectrum and bicoherence signal spectra. It also inherits useful characteristics from the bicoherence such as high immunity to additive gaussian noise, high capability of nonlinear-system identifications, and amplification invariance. Concept of the proposed index and its computational considerations are discussed first using computer-generated data and then applied to real-world vibration data from a helicopter drive train to assess health conditions of different mechanical faults as a part of condition-based maintenance.

Advanced Time–Frequency Mutual Information Measures for Condition-Based Maintenance of Helicopter Drivetrains

A new concept of nonparametric signal detection and classification technique is proposed using mutual information measures in the time-frequency domain. The time-frequency-based self-information and mutual information are defined in terms of the cross time-frequency distribution. Based on time-frequency mutual information theory, this paper presents applications of the proposed technique to real-world vibration data obtained from a dedicated condition-based-maintenance experimental test bed. Baseline, unbalanced, and misaligned experimental settings of helicopter drivetrain bearings and shafts are quantitatively distinguished by the proposed techniques. With imbalance quantifiable by variance in the in-phase mutual information and misalignment quantifiable by variance in the quadrature mutual information developed and presented herein, machine health classification can be accomplished by use of statistical bounding regions.

Analysis of nonlinear vibration-interaction using higher order spectra to diagnose aerospace system faults

For efficient maintenance of a diverse fleet of aging air- and rotorcraft, effective condition based maintenance (CBM) must be established based on rotating components monitored vibration signals. Traditional linear spectral analysis techniques of the vibration signals, based on auto-power spectrum, are used as common tools of rotating components diagnoses. Unfortunately, linear spectral analysis techniques are of limited value when various spectral components interact with one another due to nonlinear or parametric process. In such a case, higher order spectral (HOS) techniques are recommended to accurately and completely characterize the vibration signals. Since the nonlinearities result in new spectral components being formed with coherency in phase, the detection of such phase coherence may be carried out with the aid of higher order spectra. In this paper, we use the bispectrum as a higher order spectral analysis tool to investigate nonlinear wave-wave interaction in vibration signals. Accelerometer data has been collected from baseline tests of accelerated conditioning in tail rotor drive-train components of an AH-64 helicopter drive-train research test bed simulating drive-train conditions. Through bispectrum analysis, we compare the harmonics interaction patterns contained in vibration signals from different physical setting of helicopter drive train and compare that with classical power spectral density plots. The analysis advances the development of higher order statistics and two dimensional frequency health indicators in order to qualify health conditions in mechanical systems.

Applications of time-frequency analysis for aging aircraft component diagnostics and prognostics

The classical time-frequency distributions represent time- and frequency-localized energy. However, it is not an easy task to analyze multiple signals that have been simultaneously collected. In this paper, a new concept of non-parametric detection and classification of the signals is proposed using the mutual information measures in the time-frequency domain. The time-frequency-based self and mutual information is defined in terms of cross time-frequency distribution. Based on the time-frequency mutual information theory, this paper presents applications of the proposed technique to real-world vibration data. The baseline and misaligned experimental settings are quantitatively distinguished by the proposed technique.

Design of advanced time-frequency mutual information measures for aerospace diagnostics and prognostics

Accelerometer data has been gathered from accelerated conditioning in grease lubricated and lubrication deprived gear meshes in AH-64 helicopter intermediate and tail rotor gearbox, which are commonly problematic components of the Apache helicopter platform. These tests were performed in a controlled drive-train research test bed, simulating drive-train conditions to improve diagnostic and prognostic capabilities of Condition Based Maintenance (CBM) practices in Integrated Vehicle Health Monitoring System - Health Usage Monitoring System (IVHMS-HUMS) and other comparable CBM packages, as monitored by a standardized Digital Source Collector (DSC) system. Time-frequency representations of vibration measurement collected from two spaced sensors are used to provide signature analysis of transient system harmonics. Furthermore, the time-frequency mutual information advanced signal processing technique is then proposed and validated using vibration data. The measure advances the development of mutual information health indicators to quantify degradation of the helicopter power train. The AH-64 test systems perform under stress in realistic loading conditions and lifetime accelerated aircraft aging is monitored using the proposed advanced signal processing techniques for baseline tests for comparison with faulted conditions.1 2

Condition monitoring of helicopter drive shafts using quadratic-nonlinearity metric based on cross-bispectrum

Based on cross-bispectrum, quadratic-nonlinearity coupling between two vibration signals is proposed and used to assess health conditions of rotating shafts in an AH-64D helicopter tail rotor drive train. Vibration data are gathered from two bearings supporting the shaft in an experimental helicopter drive train simulating different shaft conditions, namely, baseline, misalignment, imbalance, and combination of misalignment and imbalance. The proposed metric shows better capabilities in distinguishing different shaft settings than the conventional linear coupling based on cross-power spectrum.

Quadratic-nonlinearity power-index spectrum and its application in condition based maintenance (CBM) of helicopter drive trains

This paper introduces a quadratic-nonlinearity powers-index spectrum (QNLPI(f)) measure that describes quantitatively how much of the mean square power at certain frequency f is generated by nonlinear quadratic interaction between different frequencies inside signal spectrum. The proposed index QNLPI(f) is based on the bicoherence spectrum, and the index can be simply seen as summary of the information contained in the bicoherence spectrum in two dimensional graph which makes it easier to interpret. The proposed index is studied first using computer generated data and then applied to real-world vibration data from a helicopter drive train to characterize different mechanical faults. This work advances the development of health indicators based on higher order statistics to assess fault conditions in mechanical systems.

Information extraction from helicopter maintenance records as a springboard for the future of maintenance text analysis

This paper introduces a novel application of information extraction techniques to extract data from helicopter maintenance records to populate a database. The goals of the research are to preprocess the text-based data for further use in data mining efforts and to develop a system to provide a rough analysis of generic maintenance records to facilitate in the development of training corpora for use in machine-learning for more refined information extraction system design. The Natural Language Toolkit was used to implement partial parsing of text by way of hierarchical chunking of the text. The system was targeted towards inspection descriptions and succeeded in extracting the inspection code, description of the part/action, and date/time information with 80.7% recall and 89.9% precision.

A wavelet–based index for fault detection and its application in condition monitoring of helicopter drive–train components

This paper presents a new condition indicator using wavelet analysis for the purpose of fault detection in an AH–64 gearbox. Historically, vibration–based condition indicators from employed component monitoring equipment are derived from both temporal and spectral domain analysis. However, these indicators failed to accurately capture high order correlations for the gearbox study addressed in this paper. An improved approach is necessary to overcome limitations of traditional vibrational monitoring techniques. The proposed condition indicator is derived from the Morlet continuous wavelet .transform The power spectra obtained from the wavelet transform coefficients at a certain scale or frequency are added together and then are normalised to one composite signal, denoted by a numeric index. Concepts of the wavelet index are discussed. This index is applied using real–world vibration data from a tail rotor gearbox with an output seal leak as part of condition–based maintenance practices. Results demonstrate potential of the proposed wavelet index to more effectively capture the fault when compared to gearbox condition indicators. [Received 24 January 2014; Revised 28 August 2014; Accepted 3 October 2014]