Intelligent Health Monitoring of Machine Bearings Based on Feature Extraction

This document is the Accepted Manuscript of the following article: Mohammed Chalouli, Nasr-eddine Berrached, and Mouloud Denai, ‘Intelligent Health Monitoring of Machine Bearings Based on Feature Extraction’, Journal of Failure Analysis and Prevention, Vol. 17 (5): 1053-1066, October 2017. Under embargo. Embargo end date: 31 August 2018. The final publication is available at Springer via DOI: https://doi.org/10.1007/s11668-017-0343-y.Finding reliable condition monitoring solutions for large-scale complex systems is currently a major challenge in industrial research. Since fault diagnosis is directly related to the features of a system, there have been many research studies aimed to develop methods for the selection of the relevant features. Moreover, there are no universal features for a particular application domain such as machine diagnosis. For example, in machine bearing fault diagnosis, these features are often selected by an expert or based on previous experience. Thus, for each bearing machine type, the relevant features must be selected. This paper attempts to solve the problem of relevant features identification by building an automatic fault diagnosis process based on relevant feature selection using a data-driven approach. The proposed approach starts with the extraction of the time-domain features from the input signals. Then, a feature reduction algorithm based on cross-correlation filter is applied to reduce the time and cost of the processing. Unsupervised learning mechanism using K-means++ selects the relevant fault features based on the squared Euclidian distance between different health states. Finally, the selected features are used as inputs to a self-organizing map producing our health indicator. The proposed method is tested on roller bearing benchmark datasets.Peer reviewe

Modeling of a two stages electrostatic air precipitation process using response surface modeling

Any industrial process needs to work with the optimal operating conditions and thus the evaluation of their robustness is a critical issue. A modeling of a laboratoryscale wire-to-plane two stages electrostatic precipitator for guiding the identification of the set point, is presented this in paper. The procedure consists of formulating recommendations regarding the choice of optimal values for electrostatic precipitation. A twostages laboratory precipitator was used to carry out the experiments, with samples of wood particles of average granulometric size 10 μm. The parameters considered in the present study are the negative applied high voltage of the ionization stage, the positive voltage of the collection stage and the air speed. First, three “one-factor-at-a-time” experiments were performed followed by a factorial composite design experiments, based on a two-step strategy: 1) identify the domain of variation of the variables; 2) set point identification and optimization of the process

Modeling of A Two Stages Electrostatic Air Precipitation Process using Response Surface Modeling

Any industrial process needs to work with the optimal operating conditions and thus the evaluation of their robustness is a critical issue. A modeling of a laboratoryscale wire-to-plane two stages electrostatic precipitator for guiding the identification of the set point, is presented this in paper. The procedure consists of formulating recommendations regarding the choice of optimal values for electrostatic precipitation. A twostages laboratory precipitator was used to carry out the experiments, with samples of wood particles of average granulometric size 10 μm. The parameters considered in the present study are the negative applied high voltage of the ionization stage, the positive voltage of the collection stage and the air speed. First, three “one-factor-at-a-time” experiments were performed followed by a factorial composite design experiments, based on a two-step strategy: 1) identify the domain of variation of the variables; 2) set point identification and optimization of the process