Research Papers

Seeded Fault Testing and Classification of Dynamically Loaded Floating Ring Compressor Bearings

[+] Author and Article Information
Markus Holzenkamp

Department of Mechanical Engineering,
Rochester Institute of Technology,
76 Lomb Memorial Dr., Rochester, NY 14623
e-mail: markus.holzenkamp@gmail.com

Jason R. Kolodziej

Department of Mechanical Engineering,
Rochester Institute of Technology,
76 Lomb Memorial Dr., Rochester, NY 14623
e-mail: jrkeme@rit.edu

Stephen Boedo

Department of Mechanical Engineering,
Rochester Institute of Technology,
76 Lomb Memorial Dr., Rochester, NY 14623
e-mail: sxbeme@rit.edu

Scott Delmontte

Dresser-Rand Company,
Painted Post, NY 14870
e-mail: SDelmontte@Dresser-Rand.com

1Corresponding author.

Manuscript received June 22, 2015; final manuscript received September 3, 2015; published online January 4, 2016. Assoc. Editor: Ioannis Kougioumtzoglou.

ASME J. Risk Uncertainty Part B 2(2), 021003 (Jan 04, 2016) (17 pages) Paper No: RISK-15-1079; doi: 10.1115/1.4031566 History: Received June 22, 2015; Accepted September 03, 2015

This paper investigates a variety of signal-monitoring and data-driven processing techniques to classify seed faults imposed on floating ring main crankshaft compressor bearings. Simulated main bearing shaft motion using an adaptation of the mobility method is first applied to demonstrate the plausibility of the method. Condition monitoring for three different fault types is experimentally investigated through seeded fault testing. A novel method for feature extraction utilizes a fast Fourier frequency-domain transformation coupled with a binning method that uses information across the entire frequency range. A principal component transformation process is then applied to reduce the dimension of the frequency-based feature vector to a small set of generalized features. A Bayesian classifier on the generalized features designed through seeded fault training data is shown to have excellent classifier performance across all fault types. A single-axis position measurement of the crankshaft shows the most promising results compared to a traditional accelerometer on the bearing housing and a novel accelerometer on the crankshaft. The single-axis measurement provides a cost-efficient alternative method to the two-axis orbit measurement typically used for traditional journal bearings.

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Fig. 4

Effect of viscosity on predicted shaft motion: Cj=35  μm and Cs=31  μm

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Fig. 5

Effect of radial clearance on predicted shaft motion, SAE 30 oil

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Fig. 6

Effect of grooving on predicted shaft motion, SAE 30 oil

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Fig. 3

Main bearing load components (journal to sleeve)

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Fig. 2

Floating ring main journal bearing geometry

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Fig. 1

D-R ESH-1 reciprocating compressor at Rochester Institute of Technology (RIT) (3628 kg, 3  m×1.8  m)

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Fig. 7

Proposed classification methodology

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Fig. 8

Raw x-direction shaft data from the simulation

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Fig. 9

Hamming windowed FFT’s of the shaft motion data for the three health classes with a frequency resolution of 0.53 Hz

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Fig. 10

Binned FFT data used to demonstrate the methodology

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Fig. 11

Basic classification process

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Fig. 19

Clearance variation—100% load: bearing housing accelerometer raw single (left) and binned FFT (right)

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Fig. 20

Clearance variation—100% load: classification (1.1% miss classification)

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Fig. 21

Classification performance for validation data sets for the clearance variation. Top: bearing housing accelerometer (13.3%, 23.7%, 7.7%); middle: LVDT (0%, 0%, 0%); and bottom: wireless crankshaft accelerometer (50.3%, 55.7%, 59.3%) (misclassification is shown in parenthesis)

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Fig. 12

Final classifier output for simulated example (top) (0% misclassification), percentage of eigenvalue representation, first two components are 87% and 10%, respectively (bottom)

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Fig. 13

Schematic showing the location and orientation of the grooved bearing in the compressor

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Fig. 14

Bearing with grooves: solid model (left); implementation: two grooves (center); implementation: one groove (right)

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Fig. 15

Bearing with feedhole obstruction: solid model (left); implementation: 75% (center), 50% (right)

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Fig. 26

Classification performance for validation data sets for the oil feedhole obstruction. Top: bearing housing accelerometer (33.0%, 35.3%, 17.7%); middle: LVDT (0.3%, 0%, 0%); and bottom: wireless crankshaft accelerometer (34.3%, 36.0%, 26.7%) (misclassification is shown in parenthesis)

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Fig. 16

Bearing housing accelerometer mounted inside crankcase (left) and wireless accelerometer mounted to flywheel (right)

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Fig. 17

LVDT mounting bracket and mounted on the outside of the crankcase (flywheel removed)

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Fig. 18

Main bearing temperature from start to steady-state

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Fig. 22

Grooved bearing—50% load: LVDT raw signal (left) and binned FFT (right)

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Fig. 23

Grooved bearing—50% load: classification (0% misclassification)

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Fig. 24

Classification performance for validation data sets for the grooved bearing. Top: bearing housing accelerometer (20.7%, 19.0%, 20.3%); middle: LVDT (0%, 0%, 0%); and bottom: wireless crankshaft accelerometer (23.3%, 28.7%, 24.0%) (misclassification is shown in parenthesis)

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Fig. 25

Enlarged view of the healthy and double-grooved bearing classification from the bearing housing accelerometer for zero load

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Fig. 27

Classification performance for validation data sets for the oil viscosity. Top: bearing housing accelerometer (14.3%, 19.0%, 2.0%); middle: LVDT (12.7%, 17.3%, 6.0%); and bottom: wireless crankshaft accelerometer (52.7%, 65.0%, 48.0%) (misclassification is shown in parenthesis)




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