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Research Papers

Mahalanobis Taguchi System (MTS) as a Prognostics Tool for Rolling Element Bearing Failures

[+] Author and Article Information
Ahmet Soylemezoglu

Department of Engineering Management and Systems Engineering, Missouri University of Science and Technology, Rolla, MO 65401soylemez@mst.edu

S. Jagannathan

Department of Computer and Electrical Engineering, Missouri University of Science and Technology, Rolla, MO 65409sarangap@mst.edu

Can Saygin1

Mechanical Engineering Department, University of Texas San Antonio, San Antonio, TX 78249can.saygin@utsa.edu

1

Corresponding author.

J. Manuf. Sci. Eng 132(5), 051014 (Oct 05, 2010) (12 pages) doi:10.1115/1.4002545 History: Received September 07, 2009; Revised August 27, 2010; Published October 05, 2010; Online October 05, 2010

In this paper, a novel Mahalanobis–Taguchi system (MTS)-based fault detection, isolation, and prognostics scheme is presented. The proposed data-driven scheme utilizes the Mahalanobis distance (MD)-based fault clustering and the progression of MD values over time. MD thresholds derived from the clustering analysis are used for fault detection and isolation. When a fault is detected, the prognostics scheme, which monitors the progression of the MD values, is initiated. Then, using a linear approximation, time to failure is estimated. The performance of the scheme has been validated via experiments performed on rolling element bearings inside the spindle headstock of a microcomputer numerical control (CNC) machine testbed. The bearings have been instrumented with vibration and temperature sensors and experiments involving healthy and various types of faulty operating conditions have been performed. The experiments show that the proposed approach renders satisfactory results for bearing fault detection, isolation, and prognostics. Overall, the proposed solution provides a reliable multivariate analysis and real-time decision making tool that (1) presents a single tool for fault detection, isolation, and prognosis, eliminating the need to develop each separately and (2) offers a systematic way to determine the key features, thus reducing analysis overhead. In addition, the MTS-based scheme is process independent and can easily be implemented on wireless motes and deployed for real-time monitoring, diagnostics, and prognostics in a wide variety of industrial environments.

Copyright © 2010 by American Society of Mechanical Engineers
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References

Figures

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Figure 1

Components of a rolling element bearing (image courtesy of Timken Co.)

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Figure 8

Rolling element bearing failure cause-effect diagram

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Figure 9

Progression of cage defect frequency over time for cage defect tests

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Figure 2

MD-based fault clustering

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Figure 3

Illustration of an angle based fault isolation

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Figure 10

Progression of cage defect frequency over time for inner race defect tests

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Figure 11

Progression of cage defect frequency over time for outer race defect tests

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Figure 12

MD-based fault clusters

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Figure 13

Fault detection and isolation scheme

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Figure 14

Fault clusters using cage fault as the normal case

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Figure 15

Detection of the fault progression

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Figure 16

Time evolution of MD values for cage failure test 6

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Figure 17

Time to failure estimation for cage failure test 6

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Figure 4

Linearly aligned fault clusters

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Figure 5

Sensor placement on the bearings

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Figure 6

Taig MicroMill CNC machine testbed

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Figure 7

CNC machine facing operation tool path

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