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

Residual-based scheme for detection and characterization of faults in lithium-ion batteries

This work proposes a real-time scheme to monitor the occurrence of faults and perform fault characterization. Faults, in this context, correspond to changes in the parameters of the system being monitored. The method relies on the concept of Analytical Redundancy Relation (ARR), which can be defined as the evaluation of the mathematical constraints of the physical model of the system given the real, noisy measurements. The algorithm consists of two modules: a detection strategy that relies on the regular application of an ARR-based hypothesis test in discrete time-steps; and an optimization procedure to estimate the changes undergone after a fault. By selecting a set of feasible solutions from the output of the optimization algorithm, the method also sheds some light on the uncertainty associated to the estimated quantities. The methodology is tested on simulated data of lithium-ion batteries in unmanned aerial vehicles