Browsing by Author "Benkedjouh, Tarak"

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A data-driven prognostic approach based on wavelet transform and extreme learning machine
(2017) Laddada, Sofiane; Benkedjouh, Tarak; Si- Chaib, M. O.; Drai, R.
The monitoring of a cutting tool is needed for the prediction of impending faults and estimating its Remaining Useful Life (RUL). Implementing a robust Prognostic and Health Management (PHM) system for a high speed milling CNC cutter remains a challenge for various industries to reach improved quality, reduced downtime, increased system safety and lower production costs. The purpose of the present paper is health assessment and RUL estimation of the cutting tool machines. To do so, an approach based the use of Wavelet Packet Transform (WPT) and Extreme Learning Machine (ELM) for tool wear condition monitoring is proposed. Among the main steps is feature extraction where the relevant features of raw data are computed in the form of nodes energy using WPT. The extracted features are then fed to the learning algorithm ELM; the main idea is that ELM involves nonlinear regression in a high dimensional feature space for mapping the input data via a nonlinear function to build a prognostics model. The method was applied to real world data gathered during several cuts of a milling CNC tool. Results showed the significance performances achieved by the WPT and ELM for tool wear condition monitoring.
Remaining useful life prediction of cutting tools using wavelet packet transform and extreme learning machine
(Institute of electrical andelectronic engineering /Laboratory of signals and systems (LSS), 2018) Laddada, Sofiane; Benkedjouh, Tarak; Ouali Si-Chaib, Mouhamed; Drai, Redouane
Online tool wear prediction is a determining factor to the success of smart manufacturing operations. The implementation of sensors based Prognostic and Health Management (PHM) system plays an important role in estimating Remaining Useful Life (RUL) of cutting tools and optimizing the usage of Computer Numerically Controlled (CNC) machines. The present paper deals with health assessment and RUL estimation of the cutting tool machines based on Wavelet Packet Transform (WPT) and Extreme Learning Machine (ELM). This approach is done in two phases: a learning (offline) phase and a testing (online) phase. During the first phase, the WPT is used to extract the relevant features of raw data computed in the form of nodes energy. The extracted features are then fed to the learning algorithm ELM in order to build an offline model. In the online phase, the constructed model is exploited for assessing and predicting the RUL of cutting tool. The main idea is that ELM involves nonlinear regression in a high dimensional feature space for mapping the input data via a nonlinear function to build a prognostics model. The method was applied to real world data gathered during several cuts of a milling CNC tool. The performance of the proposed method is evaluated through the accuracy metric. Results showed the significance performances achieved by the WPT and ELM for early detection and accurate prediction of the monitored cutting tools.
Tool wear condition monitoring based on wavelet transform and improved extreme learning machine
(Sage journals, 2019) Laddada, Sofiane; Ouali Si-Chaib, Mouhamed; Benkedjouh, Tarak; Drai, Redouane
In machining process, tool wear is an inevitable consequence which progresses rapidly leading to a catastrophic failure of the system and accidents. Moreover, machinery failure has become more costly and has undesirable consequences on the availability and the productivity. Consequently, developing a robust approach for monitoring tool wear condition is needed to get accurate product dimensions with high quality surface and reduced stopping time of machines. Prognostics and health management has become one of the most challenging aspects for monitoring the wear condition of cutting tools. This study focuses on the evaluation of the current health condition of cutting tools and the prediction of its remaining useful life. Indeed, the proposed method consists of the integration of complex continuous wavelet transform (CCWT) and improved extreme learning machine (IELM). In the proposed IELM, the hidden layer output matrix is given by inverting the Moore–Penrose generalized inverse. After the decomposition of the acoustic emission signals using CCWT, the nodes energy of coefficients have been taken as relevant features which are then used as inputs in IELM. The principal idea is that a non-linear regression in a feature space of high dimension is involved by the extreme learning machine to map the input data via a non-linear function for generating the degradation model. Then, the health indicator is obtained through the exploitation of the derived model which is in turn used to estimate the remaining useful life. The method was carried out on data of the real world collected during various cuts of a computer numerical controlled tool.