Automated Failure Detection Public Transit Company
Case Study
BACKGROUND
CLIENT - ELECTRIC TRAIN OPERATOR
Our client is a North American train and bus operator. Their focus is safety, train performance, and cost optimization.
They came to Machinery Analytics with the goal to maximize their efficiency through quicker and more accurate train failure identification. Identifying failures in a timely matter would allow their team to more quickly and efficiently address them.
We have so far created an algorithm that can identify abnormal events up to days/hours before maintenance events, without any prior knowledge of the system.
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We started by processing 50GB or 100M data rows of maintenance and operational data.
This image shows a very small section of the data.
As you can see, it's very complex and time-varying.
STARTING DATASET
We were able to use the data given to develop an algorithm that achieved the following:
identified system failures up to 160 hours before the maintenance event occured
with no prior knowledge of train system
OUR MODEL
RESULTS
looked at
8 distinct trains
able to identify
3 failure types
able to detect failures
160 hours before event occurred
Are you facing similar obstacles in your organization?
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