A Post Processing Framework for Analyzing Test Data from Vehicles
Ladda ner
Publicerad
Författare
Typ
Examensarbete för masterexamen
Program
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Today at Volvo Cars, thousands of logs are taken from the test cars containing
massive amounts of data, in the Measurement Data Format (MDF). This data should
be processed and analyzed. This thesis seeks to design and implement a system
that can automatically analyze the vehicles’ logs. There is a lot of prior work in
related fields, but we weren’t able to find anything as expressive as this system.
Our work focuses on analyzing signal values over time, and correlating multiple
simultaneous signals, using a flexible rule-based system based on a custom domainspecific
language. This language is designed and developed for this project, and
represents most of our contribution. An analysis consists of the user writing a
number of rules in this language, representing (for example) "signal X may not
remain zero during a consecutive 15-minute period" or "signal X may not be 4 or
higher unless signal Y is 6 or higher", and telling the system to run said rules on
an MDF file. The language interpreter is written in Python, using a small piece of
C++ to interface with an MDF parser. We also implemented a manager process
that coordinates the analysis processes, informs the user of the progress, and queues
up work if the system has insufficient capacity to run more analysis processes. The
system has enjoyed a positive reception at Volvo Cars, exceeding planned problem
sizes already in the testing phase; the system could handle the increased load with
only small changes, and still offers plenty of potential to increase performance and
scalability even further.
Beskrivning
Ämne/nyckelord
Automotive industry, post-processing, domain-specific language, rule-based language