Intelligent Health Monitoring of Machine Bearings Based on Feature Extraction
- PDF / 2,257,461 Bytes
- 14 Pages / 595.276 x 790.866 pts Page_size
- 53 Downloads / 223 Views
TECHNICAL ARTICLE—PEER-REVIEWED
Intelligent Health Monitoring of Machine Bearings Based on Feature Extraction Mohammed Chalouli
. Nasr-eddine Berrached . Mouloud Denai
Submitted: 20 January 2017 ASM International 2017
Abstract 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 crosscorrelation filter is applied to reduce the time and cost of the processing. Unsupervised learning mechanism using Kmeans?? 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 selforganizing map producing our health indicator. The
M. Chalouli (&) N. Berrached Intelligent Systems Research Laboratory (LARESI), USTO-MB University, Oran, Algeria e-mail: [email protected] N. Berrached e-mail: [email protected] M. Denai School of Engineering and Technology, University of Hertfordshire, Hatfield, UK e-mail: [email protected]
proposed method is tested on roller bearing benchmark datasets. Keywords Failure diagnosis Bearing faults Time-domain features Condition-based maintenance Health indicators Relevant features Fault feature extraction
Introduction Bearing fault is one of the foremost causes of breakdown in rotating machines. It represents over 40% of the motor faults according to the research conducted by Electric Power Research Institute (EPRI) [1, 2]. Most of the existing faults diagnosis methods can identify many bearing faults, but often cannot recognize the fault level accurately [3]. Also, the diversity of symptoms which can develop from the same fault makes diagnostics even harder. This is why the diagnosis of these critical components has grown strongly in the industrial world as the desire to obtain more efficient and safer production line becomes indispensable. Replacing a bearing before its end of life leads to unnecessary downtime and parts cost. If, on the other hand, the bearing is used till its end of lifetime leads to unplanned downtime, safety and environmental risks and subsequent damage of other parts. Thus, the most appropriate maintenance plan in this case is the condition-based main
Data Loading...
