Sperry Rail has been active in non-destructive testing for rail faults for more than 90 years. In a drive to deliver greater efficiency in track monitoring, the supplier has developed Artificial Intelligence (AI) and machine learning applications that it is now installing in regular service.
As demand for track access grows, infrastructure managers from around the
world face a daily battle to keep their railways operational during limited
Predictive and preventative rather than interval-based maintenance is the
Holy Grail for infrastructure managers. AI and machine learning applications
are increasingly facilitating these strategies. Sperry’s solution, Elmer, named
after Dr. Elmer Sperry, the company’s founder, is an AI system that relies on
neural networks—a set of algorithms designed to recognize patterns—to identify
crack signatures in rail scans.
Sperry’s vehicle-mounted ultrasound, induction and vision systems record
hundreds of miles each day, generating vast quantities of data that are now analyzed
using a scalable cloud computing solution running the Elmer neural networks.
Since July 1, 2019, all of Sperry’s non-stop fleet in North America has been
processed using Elmer while additional North American as well as European,
African and South American clients are set to benefit as their systems become
compatible with Elmer in the future.
The technology, including AI, neural networks and training data, was all
developed in-house. The overall objective is to minimize the number of possible
suspect points identified on the track. This will reduce the amount of
information presented to analysts, who will continue to make the final choice
of identifying the false positives before sending the suspected faults out for
validation on the track itself. By focusing the attention of analysts on fewer
suspect points, the intention is to reduce the risk of error and the amount of
time people spend inspecting track. It is also possible for Elmer to target
large and priority faults.
In developing Elmer, Sperry faced the challenge of separating non-flaw
detections from genuine flaw detections. This is currently done with a
rules-based pre-processing system that highlights potential flaws, which are
then assessed by the human analysts and identified in the field.
The neural network approach was influenced by methods used in medicine,
where networks classify features in scanned images. Figure 1 shows a schematic
of a composite neural network that has been developed to process one or more
sets of transducer inputs through a range of deep Convolutional Neural Networks
The CNNs identify patterns of key features in the signal and then combine
this feature knowledge within a fully connected layer. This layer can
optionally be fed into a recurrent neural network made up of Long Short-Term Memory
cells (LSTMs), which take account of the local data context within the wider
data stream, such as the presence of other nearby features.
The final output layer classifies the input dataset, identifying if it is a
known feature. The structure is implemented in TensorFlow, the open-source AI
mathematics library developed by Google. For different feature detections,
paths into the neural network are turned on or off depending on which signals
the network can learn from. For example, bolt hole crack (BHC) detection uses
only the ultrasound inputs; transverse defect detection can use ultrasound and
induction; surface damage and welds use the eddy current signal.
To detect BHCs, it is necessary to find several thousand examples of
ultrasound signatures of good bolt holes and cracked bolt holes, as well as
examples of other features that are not of interest but that the neural network
must be trained to ignore.
Figures 2 and 3 show examples of benign bolt holes and cracked bolt hole
signatures, respectively, with colors representing different transducer
reflections. There is a huge variation in the ultrasound signatures, but they
follow a number of basic patterns. This situation is typical of a problem that
is very hard to define with rules but can work well with neural networks if
there is enough variety in the training examples.
Once the neural network is trained, it must be validated using data to which
it has not previously been exposed. This helps to ensure that the knowledge is generalized
and is not picking up non-general features of the set examples.
Having passed validation, the neural network can be used to make predictions
for the location of features in each of the day’s recordings. The recordings
pass through the neural network using overlapping windows of data and are
marked depending on what it is predicted to contain.
Figure 4 shows an example of a few meters of ultrasound data from a rail.
The colored stripes indicate what the neural network predicted when the data
window was under the cross hairs. Green is used for BHCs (such as currently
under the crosshairs); red marks rail end detection; blue marks good bolt
holes; yellow marks bond pins. There will usually be a number of repeat
indications as a feature passes through the neural network window, including
occasional false indications. These are used to vote for the recognition that will
be reported at a location. With a well-trained network, false positives are
reduced by a factor of three or more.
Just like in the bolt hole case, thousands of examples were required to
train a network to detect transverse defects (TDs). In this case, there were a
number of items with transverse signatures, most of which were benign, but some
of which were flaws. In the classic rules-based recognizer these could not be
separated so all were indicated as potential suspects. This results in a large
amount of work for a human analyst to remove the benign indications.
The benign indications are often easy for a human to recognize, but because
of the variety, difficult to define with rules. This situation is good to
address with neural networks. It was decided to break down the transverse
signatures into various categories of benign items and flaws. Each result from
the neural network carries a confidence level that it was correctly categorized.
This can be used to bias the likelihood of incorrect detections being either
false positives or false negatives. The safest failure mode is to bias flaw
detections to be more likely to give false positives (over-marking) and benign
detections to be more likely to give false negatives (under-marking). With a
well-trained network, it is possible to tune the balance to identify all
significant TDs while reducing the amount of over-marking for the human analyst
Early results show that a well-trained network has the ability to categorize
the indications, and this can be used to reduce the false positives by a factor
of 10 or more by removing all benign-type indications. Furthermore, the
“obvious” TD categories (large TDs, reverse TDs) can be confidently identified
as critical defects. This leaves the more ambiguous small TDs and surface defects,
which currently still rely on classic rules-based detection.
This approach of identifying the highest risk flaws accurately while
removing the low risk features means that as the neural network is improved,
the middle ground of more ambiguous items will be squeezed. A classic rules-based
approach will continue to be used where appropriate—for example, to meet
definition-based procedural actions defined by some railways.
Development of the system was followed by work on validation. A validation
set was constructed where hundreds of flaws were mixed in a data set containing
many thousands of non-flaws, with particular emphasis on indicators, which have
similarities to flaws. This validation set is constantly updated with new
examples, including any false identifications, to make sure it is
representative of what might be found when the network is in use. Whenever a
neural network is trained with new data, it is run against the validation set,
and the results are compared with previous versions.
Current development work is focused on three areas: improving the scope of
the AI to eventually offer comprehensive analysis of all fault types; building
a Big Data infrastructure, which is essential to manage the vast amounts of
data used in AI projects; and improving the scope and robustness of the
processing pipeline to manage the throughput to high internal service level
“It is important to realize that the level of engineering required to
convert an AI/machine learning proof of concept to a successful product or
process is an order of magnitude more challenging than an equivalent
software-only project,” says Sperry Vice President Global Business Development Robert
DiMatteo “As a result, Sperry puts a significant emphasis on ensuring the
system is robust and that AI’s continual learning is easy and auditable.”
Sperry has developed proofs of concept that analyze eddy current data to
find features, as well as prototypes that can extract features from induction
sensors. These two incremental technologies offer a broad-scope view of the
rail, and permit enhanced data analysis that will ultimately be rolled into the
Elmer process. Ultimately, these systems, and possible vision systems, will
support a combined assessment of the rail, where each system can enhance the
results of the other.
Credit: Google News