A Retrospective Approach for Establishing Wear Signatures in Lubricating Oils

Brian Byrne

Volume of Data

• 40 zettabytes (1 trillion GB) of data will exist worldwide by 2020

• 2011 produced enough data to fill 57.5 billion 32 GB iPads – enough to build a Great iPad Wall of China twice the height of the original

• Extracting the usable data is the key

Maintenance Philosophy

• Distribution of random variables• Outputs probability percentile; where does this

data point belong in the set?• Standard deviation (σ) – deviation from mean (µ)• Z-scores - µ + σ• Typically used to determine absolute limits for

lube oil analyses

Normal Distributions

Is a Distribution Normal?

• Testing for relative normality- Skewness - Kurtosis- Significance

Skewness

0.826989

Test for significance of skew

0.369274

Kurtosis 0.602106

• Distribution fitting, Log, Weibull…• Transformations – replacement of a variable

to a function of that variable (invertible)

Outliers- Median- Inter-quartile range- Major & minor

Median 18.00

Q1 12.00

Q3 26.00

IQR (Q3-Q1) 14.00

Minor Outlier 47.00

Major Outlier 68.00

Iron (Fe) in Rail Final Drive Fleet

Initial Windsored Minors Removed Majors & Minors

Avg. 34.0 26.9 21.6 19.1

Ϭ 80.2 31.9 22.8 18.1

2xϬ 160.4 63.8 45.6 36.2

3xϬ 240.6 95.7 68.4 54.2

4xϬ 320.8 127.6 91.2 72.3

5xϬ 401.1 159.6 114.0 90.4

Median 13

Q1 6 3

Q3 35 117.6

IQR (Q3-Q1) 29 128

Minor Outlier 78.5

Major Outlier 122

Skewness 9.924946 1.948394 1.8157838 1.390495

Kurtosis 1.022931

Manipulation of Data

*Population size – 390 from 336 assets

Case Study

- Upper caution limit 1 x Z-Score (µ + 1σ) = 37.2 PPM

- Upper critical limit 2 x Z-Score (µ + 2σ) = 55.3 PPM

- Values vary hugely from OEM guidelines

Iron in Final Drive Fleet

0

10

20

30

40

50

60

70

80

90

100

5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 More

Freq

uenc

y

Bin

Iron (Fe) in Final Drive Fleet

Frequency

2 per. Mov. Avg. (Frequency)

• Positive skew • Elevated kurtosis

• Non-normal distribution• Manipulated by removing outliers

Case Study

• Transform & validate- Create a symmetrical distribution- Construct a confidence interval

• Inversely transformed back to real data

*Population size – 390 from 336 assets

Iron in Rail Engine Fleet Relative Normality

Skewness

0.826989

Test for significance of skew

0.369274

Kurtosis 0.602106

Avg. 19.7

Ϭ 10.5

2xϬ 21.1

3xϬ 31.6

4xϬ 42.2

5xϬ 52.7

Median 18.00

Q1 12.00

Q3 26.00

IQR (Q3-Q1) 14.00

Minor Outlier 54.00

Major Outlier 68.00

General Normality Test- Kurtosis -2 to +2 **- Skew -0.8 to +0.8 **

** George, D., & Mallery, M. (2010)

* Population size – 180 samples from 180 assets

0

5

10

15

20

25

30

35

40

45

50

5.0 10.0 15.0 20.0 25.0 30.0 35.0 40.0 45.0 50.0 55.0 60.0

Freq

uenc

yBin

Case Study

Relating Back to Oil Analysis

• Identification of statistical indicators (condemning limits)• Connecting triggers or combinations thereof to conditions

– requires equipment owner input• Automating & standardising diagnostics• Justifying further analysis

Fe Cr Ni Mo Al Pb Cu Sn

69 1 1 1 1 31 469 3

Fe Cr Ni Mo Al Pb Cu Sn

1239 17 5 1 1 38 867 5

Case Study

*Population size – 180 samples from 180 assets

Rail Engine Example Indicators

• Zinc ↓ (Zn) and anti-wear component ↓ (additive breakdown)• Oxidation ↑ & sulfation ↑• Sulfation ↑, soot ↑ and TBN ↑ (false positive BN)• Silicon ↑ (Si) with Aluminium ↑ (Al) – dirt ingress• Copper, tin, lead ↑ – bearing wear• Iron (Fe) ↑, Aluminium (Al) ↑ & low but elevated chrome (Cr) ↑

- liners, pistons, piston rings

Zn TBN OX NIT SUL

1386.0 10.1 10.4 5.6 17.1

1376.0 11.1 11.8 6.3 17.9

735.0 18.0 75.0 0.0 42.4

Antiwear Comp.

104.2

102.7

33.9

Fe Cr

2.0 1.0

5.0 1.0

10.0 1.0

36.0 4.0

22.0 2.0

18.0 2.0

76.0 6.0

57.0 4.0

69.0 5.0

49.0 3.0

Case Study

*Population size – 180 samples from 180 assets

Relating Back to Equipment

• Retrospective review of maintenance intervals & failures• Mapping of indicators from failure back to normal

operation• Correlation between statistical generated triggers &

retrospective indicators • Generation of collaborative ‘wear signatures’

Case Study

Rail Engine – Review of Wearing Engines Case Study

Fe Al Cu Si Soot30 Jan 16 39 4 12 14 0.427 Nov 15 33 3 2 17 0.806 Oct 15 13 2 1 11 0.4

32.2 = µ + 1σ Trend Trend Trend 0.8 = µ + 1σ

Fe Pb Si Wtr Soot22 Mar 16 47 1 22 181.4 0.708 Jan 16 10 2 12 10 0.309 Nov 15 8 2 12 10 0.227 Oct 15 15 6 9 10 0.809 Oct 15 12 3 11 10 0.4

43.8 = µ + 2σ 4.9 = µ + 2σ Trend Trend 0.8 = µ + 1σ

Initial indicators observed five months prior to failureMasked thereafter by oil top-ups before returning

Indications well before advanced wear regimeMuch more conservative than OEM limits

Examples of subtle indicators

Rail Engine – Mapping from Failure Case Study

• Bearing failure in a rail engine – Babbitt, copper & steel- Incremental increase in copper until exceeds spectrometer

capabilities (depends on sampling interval)- Lead increases of the order of 2/3 PPM significant

(4.9 PPM exceeds 95.4% confidence interval from data)- Fuel dilution levels of 1% or more a major contributor- Level of iron signifying issues, much lower than expected

and outlined by OEM (Iron of 40.8 exceeds 2 Z-score index)

Rail Engine – Mapping from Failure

Mag x500 HTCast Iron fatigue chunk from case hardened part

Rail Engine – Mapping from Failure Previous Oil Analysis

Four subtle indicators well below OEM absolute limits

x

Rail Gearbox – Mapping from Failure

• Bearing failure in a rail gearbox – brass alloy cage rivets popping- Crack propagation occurs very quickly- Can still run with rivets fractured- Reviewed failures showed increase in copper (often single figure PPM) on

previous oil analysis in 75% of cases (sample frequency)

XRF Results on Failure Surface

Rail Gearbox – Mapping from Failure

Conclusions

• Three levels of limit: Absolute (Dynamic/Floating), Trend, Conditional• Failure data can be transformed to conditional triggers & applied• Can interface with a dashboard & automate diagnostics• Can be applied to contaminants & chemical properties• Integrates with a proactive program & justifies investigation campaigns • Ultimately saves money, improves reliability & increases safety

Collaboration of Numerical & Failure Data

Thank you for your time

QUESTIONS?