Advanced Oil Condition and Debris Analysis

                      

Oil elemental analysis, oil quality characteristics, and debris

The specific oil quality contamination events that are the most dominant failure modes relevant to the target lubrication system are: water contamination, addition of incorrect oil, fuel dilution, and degraded oil. The current method of determining dominant failure modes in wear debris and oil quality is periodic oil sampling, and off-line testing, where the following standard  oil analysis tests are performed on oil4:

Elemental Analysis / Oil Debris

Atomic emission spectroscopy (AES) – wear debris and dirt

LaserNetFines (LNF) – silhouette of particle, plus size and shape

Ferrography – particle size and shape and sorted by ferrous / non-ferrous

Oil Quality

FTIR (bench and handheld) – lubricant condition and contamination

Viscometer – lube viscosity

Crackle test – water contamination

Karl Fisher – water contamination

Flashpoint – fuel contamination

Fuel meter – fuel contamination

Particle counting – for hydraulic cleanliness – fine particulate contamination

In order to realize a sensor that can enhance the timeliness and overall effectiveness of periodic oil sampling for corrective maintenance actions, the on-line sensor must provide data that is similar in concept or utility to many of these tests. 

Oil Debris Sensing

Oil debris is part of the end-of-life process of a mechanical component, such as a gearbox or oil-wetted bearing. The sensor must therefore be capable of correctly identifying the wear particulate produced by gear tooth wear or bearing spall. Correctly identifying the size and type of wear metals provides an indication of the component that is failing as well as the severity of the failure. Commercially available oil debris monitor (ODM) have been developed with consideration of these critical detection requirements.

An on-line inductive sensor typically detects nearly 100% of ferrous (Fe) and some non-ferrous (non-Fe) metallic wear debris particles above a minimum threshold size (typically 100-200 µm). The sensor counts each particle, determines particle makeup (Fe or non-Fe), and sizes the particles into bins (200-300 µm, 300-400 µm, etc.). The total mass of debris is updated in real-time. Size, count, mass, and makeup of wear particles have been shown to provide condition indication for aircraft bearings and provide diagnostic and prognostic information about bearing health and remaining useful life and allows for on-line discrimination of component damage vs. normal wear debris.

Additional Resources

Please check out some of my earlier publications on these capabilities at:

https://www.machinerylubrication.com/Read/138/real-time-oil-analysis

https://www.sbir.gov/node/5323

https://www.navysbir.com/06_1/123.htm

https://www.slideshare.net/Carl-Byington/cbm-sensing-by-carl-byington-of-phm-design 

About the Author:

Carl Byington developed and patented oil sensor technologies for Impact Technologies, Sikorsky Aircraft, and Lockheed Martin. Carl Byington became an expert in prognostics and health management (PHM) technologies and next-generation condition-based maintenance plus (CBM+) solutions. He currently consults in these technical areas at his PHM Design company, located in Georgia. 

Diagnostic Performance Metrics for PHM Algorithms

Carl Byington developed and patented diagnostic and prognostics technologies for Impact Technologies, Sikorsky Aircraft, and Lockheed Martin. Carl Byington became an expert in prognostics and health management (PHM) technologies and next-generation condition-based maintenance (CBM) solutions. He currently consults in these technical areas with his PHM Design company, located in Georgia. In this latest blog series he discusses how to verify and validate diagnostic algorithms for a specific application. See here for more about Carl Stewart Byington. 

Algorithm Concept

Diagnostic algorithms are typically qualified on specific types of faults with limited test data and engineering judgment of intended applicability. Mechanical system ailments in gearboxes for instance may vary from root cause conditions such as shaft misalignment; to fatigue events such as spalled bearings; to slower wear processes related to scuffing wear on gears. The accuracy of the fault detection and diagnostic processes for such a wide array of problems will not only depend on the algorithm’s sensitivity to signal- to-noise ratio but also load level, failure type and flight condition. Diagnostic algorithms that are sensitive to faulted conditions yet relatively insensitive to confounding conditions are desirable for a broader range of application in equipment health monitoring. Such generalized algorithms, though, may be less sensitive to early fault detection. In order to assess the risk associated with using certain diagnostic algorithms, qualify them for a range of use, determine desirable thresholds to produce known false alarm rates, and develop fusion approaches, we need to evaluate the detection performance and diagnostic accuracy using established performance metrics. Here are some specific metrics we can use for each step.

Decision Matrix Construct

The following Decision Matrix defines the cases used to evaluate fault detection. It is based on hypothesis testing methodology and represents the possible fault-detection combinations that may occur.

 Detection Decision Matrix

Outcome	Fault (F1)	No Fault (F0)	Total
Positive (D1) (detected)	a Number of defected faults	b Number of false alarms	a+b Total number of alarms
Negative (D0) (not detected)	c Number of missed faults	d Number of correct rejections	c+d Total number of non-alarms
	a+c Total number of faults	b+d Total number of fault-free cases	a+b+c+d Total number of cases

From this matrix, the detection metrics can readily be computed. The Probability of detection given a fault (a.k.a. sensitivity) assess the detected faults over all potential fault cases:

                                        (1)

The probability of false alarm (POFA) considers the proportion of all fault-free cases that trigger a fault detection alarm.

                                       (2)

The Accuracy is used to measure the effectiveness of the algorithm in correctly distinguishing between a fault-present and fault-free condition. The metric uses all available data for analysis (both fault and no fault):

Accuracy=         (3)

Diagnostic metrics are used to evaluate classification algorithms, typically consider multiple fault cases, and are based upon the confusion matrix concept. The matrix, illustrates the results of classifying data into several categories. A confusion matrix shows actual defects (as headings across the top of the table) and how they were classified (as headings down the first column). The shaded diagonal represents the number of correct classifications for each fault in the column and subsequent numbers in the columns represent incorrect classifications. Ideally, the numbers along the diagonal should dominate. The confusion matrix can be constructed using percentages or actual cases witnessed.

The probability of isolation (Fault Isolation Rate - FIR) is the percentage of all component failures that the classifier is able to unambiguously isolate. It is calculated using:

                                                (4)

                                                               (5)

              Ai = the number of detected faults in component i that the monitor is able to isolate                             unambiguously as due to any failure mode (Numbers on the diagonal of the Confusion Matrix).

              Ci = the number of detected faults in component i that the monitor is unable to                                     isolate unambiguously as due to any failure mode (Number off the diagonal of the Confusion Matrix).

An alternative metric that is also useful is the Kappa Coefficient, which represents how well an algorithm is able to correctly classify a fault with a correction for chance agreement.

         (6)

where:

N(obs in agreement) = sum of diagonals in matrix

N(exp in agreement) = sum{[sum of row)/N]*(sum of column)} for diagonals

N(total) = total number of observations

The metrics presented here form the basis for analysis of current detection and diagnostic algorithm effectiveness. The issue that confronts the PHM designer and researcher now is to generate realistic estimates of these metrics given the currently limited baseline data and faulted data available. The basis for an effective statistical analysis, given these limitations, will be discussed in a separate entry.

Additional Resources

A full review of detection and diagnostic metrics is summarized in a paper by Carl Byington, et al. It is available here:

http://www.humsconference.com.au/Papers2003/HUMSp404.pdf

Carl Byington’s related publications can be found at:

https://www.researchgate.net/profile/Carl-Byington

Carl Byington may be contacted for specific consulting engagements at:

https://phmdesign.com/contact-us/