Nouredine Ouelaa

Gears and Bearings Combined Faults Detection Using Hilbert Transform and Wavelet Multiresolution Analysis

Rotating machinery represents a significant class of mechanical systems, in which gears and bearings are the important and frequently encountered components that find wide spread industrial applications. Currently, vibratory analysis becomes the most reliable tool for the conditional maintenance of rotating machines. The associated techniques evolved so much that one passed from a simple late detection to the diagnosis, even the prediction. Researches in this field are directed towards the possibility of anearly detection, several signal processing techniques, often adapted to a precise defect type, were established throughout these last years. From those, demodulation by wavelets multiresolution analysis combined with Hilbert transform (Envelope analysis), which allowing making filtering and the demodulation at the same time. The objective of this work is the application of this technique for the detection of the faults in the rotatory machines and more particularly the gears and bearings combined faults.

Prediction of Cutting Tool’s Optimal Lifespan Based on the Scalar Indicators and the Wavelet Multi-resolution Analysis

The objective of this article is to propose Wavelet Multi-Resolution Analysis as an effective tool allowing the improvement of the sensitivity of the scalar indicators for the identification of cutting tool’s degradation state during the machining of X200Cr12 steel. Indeed, these indicators are very sensitive to the variations in the temporal signal related directly to the vibrations induced during turning operation. Nevertheless, their reliability is immediately limited in the presence of intense levels of random noise and other machine components. In addition to the Wavelet Multi-Resolution Analysis (WMRA) which brought a solution to this problem, one proposes a new spectral indicator, which one called overall level, to separate the phases characterizing the tool’s wear. The results obtained from this article made it possible to study the phenomena of vibration associated with machining, and to locate the transition point from the normal wear period to the accelerated wear period.

Experimental Study of Combined Gear and Bearing Faults by Sound Perception

One presented in this work a vibro acoustic analysis of various signals in the case of one or several combined defects such as bearings and gears defects. The objective is to identifying each of the defects even when it combined. We begin by studying the temporal and spectral scalar indicators; a perceptive analysis of the sounds corresponding to different types of defects have been established to investigate the sensitivity of listeners to the combined defects, and the ability to distinguish between defects with different types and natures. According to the study of the vibrational indicators and of the listening test, the results are well preventative of the evolution of different defects gravity. For sound perception, the listeners could classify the sounds according to the type and the level of defects gravities.

Fault Diagnosis Through the Application of Cyclostationarity to Signals Measured

In this paper we have used a frequency modulation method for detecting faults in the plain bearings and the gear teeth defects. This method is based mainly on the analysis of some cyclostationarity non-stationary signals. Indeed, a cyclostationary signal has hidden periodicities; that is to say, it is not periodic in the strict sense but some statistical properties of the signal are periodic. This frequency is used to identify the spectral correlation which has the advantage of being a function of a single variable frequency instead of two. The experimental validation is performed on the basis of signals measured in an industrial environment (turbogenerator). The application of this method to non stationed signals has helped to highlight very clearly the presence of defects in the bearings of the gearbox, which has been difficult to demonstrate by spectral analysis.

Prediction of Tool Wear in the Turning Process Using the Spectral Center of Gravity

The recent increase in machining productivity is closely related to longer tool life and good surface quality. In the present study, an experimental technique is proposed to evaluate the performance of a cemented carbide inset during the machining of AISI D3 steel. The aim of this technique is to find a relationship between the vibratory state of the cutting tool and the corresponding wear during machining in order to detect the beginning of the transition period to excessive wear. A spectral indicator named spectral center of gravity, SCG, is proposed to highlight the three phases of tool wear using the spectra of the accelerations measured. Very promising results are obtained which can be used to underpin an industrial monitoring system capable of detecting the onset of transition to excessive wear and alerting the user of the end of the tool’s life. The purpose of this study is to review the vibration analysis techniques and to explore their contributions, advantages and drawbacks in monitoring of tool wear.

Optimization of a Maintenance Policy in Industrial Field: Case Study

This article concerns the optimization of the maintenance policy existing in the biggest fertilizer products company in Algeria. The work comprises in the first part an assessment of the current policy using an audit of the maintenance function performed on the basis of the Lavina questionnaire. Improvement suggestions were presented. Secondly, one proposes the application of the “Optimization of the Maintenance by Reliability” approach as an effective tool for the optimization of the production equipment’s dependability (safe of operation). An application of this approach is carried out, for example, on a steam-turbine generator; a vital machine in the production process. Reliability study using Weibull’s model and a Failure Modes Effects Criticality Analysis (FMECA) have been carried out. This application has allowed, for the studied machine, selecting the optimal maintenance management method based on vibration analysis using appropriate tools. The same approach developed for the steam-turbine generator can then be applied for the other production equipment.