Failing (burned) exhaust valves are “rare events” but they’re the leading cause of cylinder replacement, and exhaust valve failure is a significant cause of piston engine power-loss incidents. Compression leakdown testing has traditionally been used to identify failing exhaust valves, but it is an imperfect test at best, and requires spark plug removal. Visual borescopy is a much more reliable method of evaluating valve health, but it also requires access to the cylinder innards through the spark plug hole. Wouldn’t it be great if there was a non-invasive way to assess valve health? Enter FEVA—Failing Exhaust Valve Analytics.

Digital engine monitors, now ubiquitous among small GA aircraft, provide a window into the combustion process and the health of the cylinder itself. We’ve long observed that failing exhaust valves often cause small, slow, rhythmic EGT oscillations that are fairly easy to spot in a graphical plot of the data. In 2015, we developed FEVA, a heuristic algorithm that scans all uploaded engine monitor data for this signature. We’ve been using this to alert owners to the possibility that they may have a failing exhaust valve and that a borescope inspection of the cylinder is suggested. But to be honest, we were less than thrilled about the accuracy of the FEVA algorithm. That led to the development of FEVA 2.0, introduced in Spring of 2020, and now, FEVA 2.1.

FEVA 2.0 was the improved version of FEVA released in Spring of 2020. FEVA 2.0 used machine learning technology and an expanded data set to predict failed exhaust valves, and presented the results in a new way.

FEVA 2.1 uses the same machine learning technology as FEVA 2.0. But FEVA 2.1 uses a larger set of predictive variables than 2.0, and includes data from an aircraft’s entire recent flight history.

FEVA 2.1 is included in all SavvyMx, SavvyQA, and SavvyAnalysis Pro plans at no additional cost.

Mike Busch has written and lectured extensively on the subject of exhaust valve health. Here are links to his articles, webinars and his book on the topic:

Articles:

• Don’t fail me Now!
• Exhaust Valve Failures
• Borescope Ascendancy
• Compression in Context
• EGT Myths Debunked

Webinars:

• Exhaust Valve Failures
• Borescope Ascendancy
• The EGT Myth
• All About Cylinders
• Engine Monitors

Book:

The book Mike Busch on Engines also has an extensive discussion of exhaust valves.

Yes it does. The rhythmic EGT oscillation is still one of the predictors in FEVA 2.1.

The FEVA 2.1 model includes over 35 variables, mostly from your engine monitor data, to make its predictions. It looks for hidden patterns in the data — patterns that might not be apparent to the human eye. It’s very difficult to identify which specific variables are causing the model to predict higher failure probabilities for certain cylinders than for others because the model is a very complex function created by an ensemble of decision trees. We use this “random forests” machine learning model because it is quite powerful for the number of training examples we have. The drawback is that it is somewhat hard to tease out the effects of individual variables.

When an exhaust valve failure does occur, Savvy’s clients often ask us “What did I do wrong? How could I have prevented the failure?” The answer is: You probably did nothing wrong, and you probably could not have prevented the failure. We have found that exhaust valve failure is caused primarily by factors outside the control of pilots and owners, such as variations in assembly tolerances and materials of cylinder assemblies. However, if you would like us to review your powerplant management technique using data from a specific flight, simply request analysis of the flight in the normal way and note that you would like us to focus on your operating technique.

When we test the FEVA 2.1 model against a set of test data we find that FEVA 2.1 is able to predict the valve failure risk category with a high statistical significance. Additionally, FEVA 2.1 shows a 20% improvement over FEVA 2.0 in predictive performance. It’s important to remember, however, that the FEVA 2.1 report is a “screening” tool that tells you when a more definitive diagnostic borescope inspection is called for. An analogy in medicine might be the PSA test for prostate cancer. The PSA cannot diagnose prostate cancer, but it can tell you when a definitive test such as a biopsy is advisable.

Math geeks make up a large proportion of the Savvy team! FEVA 2.1 uses a supervised machine learning model, meaning that that model is “trained” using data from flights of aircraft with known valve condition. It is a model that classifies valves into one of three risk categories: below average risk of failure, average risk, and higher than average risk. There are many types of classification models. The one currently used for FEVA 2.1 is a Random Forests ensemble model. We believe this is the most appropriate model for our particular application and the dataset we have at the present time.

A one-second sample interval is best because some of the measures used in FEVA 2.1 need high data granularity for accuracy. If your equipment doesn’t support a sample rate as short as one second, program it to the shortest sample interval it does support (e.g., two seconds).

Very possibly. As of now we don’t have an automated way to flag a failing engine monitor probe, although such capability is on the SavvyAI team’s drawing board. If you suspect a bad probe, you should seek advice from a Savvy analyst or account manager.

FEVA 2.1 can only use flights for which the algorithm can identify a cruise segment. The algorithm has an easier time identifying a cruise segment if the data contains altitude information or outside air temperature. It can work with less data, but very basic monitors which record only EGT and CHT are not supported by FEVA 2.1. Such basic monitors will still be supported by FEVA 1.0.