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

Bearing Health Condition Monitoring: Time Domain Analysis

Condition Monitoring is one of the most important techniques for maintenance of a machine. This is done in order to increase the safety and reliability of the machine. There are several types of condition monitoring for aircraft engine bearing health monitoring such as, Oil Debris Monitoring, Temperature Monitoring, and Vibration Monitoring. Among these, vibration monitoring is found to be the most widely used technique. The current work focuses on the Time Domain Analysis in Vibration Monitoring of a rolling element bearing. This type of analysis depends on several statistical features which are used to identify the defects in the bearing. The extraction of these features is done by a method called feature extraction. It involves calculating features using the raw vibration data and also finding the features using time derivative and time integral of the vibration data. In addition to that, a Graphical User Interface (GUI) has been designed which enables the user to give input data and obtain the fault analysis and plots of time domain analysi