CAN trace investigation
Analyze message timing, payload values, state transitions, and the surrounding network context.
CAN & Vehicle Network Analysis
CAN Log Analysis
Investigate CAN and CAN-FD recordings to identify communication issues, signal behavior, intermittent events, and the conditions behind vehicle problems.
Discuss Your CAN IssueProblems I can investigate
Find the behavior behind the symptom
- 01
A CAN message disappears or arrives intermittently
Investigate message timing, network conditions, event context, and surrounding signals to understand when communication is lost or behaves unexpectedly.
- 02
A signal value does not match expected vehicle behavior
Compare decoded values, scaling, DBC definitions, timing, and related signals to determine whether the issue comes from interpretation, communication, or system behavior.
- 03
An intermittent vehicle event is difficult to reproduce
Analyze long recordings and correlate signals around the event to isolate the conditions that occur immediately before and during the issue.
- 04
The DBC does not appear to match the recorded traffic
Review message identifiers, DLC, byte layout, scaling, signal behavior, and observed network traffic to identify inconsistencies.
- 05
The recording contains CAN errors or unstable communication
Examine error frames, timing, communication gaps, and surrounding events to identify evidence of an unstable bus or controller state.
What I can help with
Focused engineering work in Vehicle Networks & Communication
Signal decoding and validation
Decode signals using available DBC information and verify whether the interpretation matches observed behavior.
Event correlation
Identify which signals or states change before, during, and after a reported event.
Intermittent issue detection
Search long recordings for recurring conditions, anomalies, communication gaps, or event signatures.
DBC consistency checks
Compare DBC definitions with recorded identifiers, payload layouts, scaling, and live signal behavior.
Automated log analysis
Develop focused Python analysis when repetitive investigation across recordings would otherwise be manual.
Engineering findings
Summarize the relevant observations and evidence for debugging, validation, or further development.
What to send
Start with the evidence you already have
BLF recording
A relevant timestamp or event marker is helpful when available.
ASC recording
A relevant timestamp or event marker is helpful when available.
DBC
Useful for signal-level interpretation, but not always required.
MF4/MDF measurement
Useful when CAN activity needs to be correlated with measured vehicle signals.
CAN dump
Raw or exported frames can be used when a native recording is unavailable.
Problem description
A short description of expected and actual behavior is enough to begin.
How the analysis works
From recorded data to engineering findings
- 01
Understand the issue
Review the reported behavior, available recordings, DBCs, timestamps, and expected vehicle behavior.
- 02
Prepare the data
Load and normalize the relevant CAN or CAN-FD recordings and identify the signals, messages, and time ranges most likely to matter.
- 03
Investigate
Analyze timing, signal changes, message behavior, correlations, and event context to narrow down the cause.
- 04
Automate where useful
When the issue spans large datasets or repeated measurements, create focused scripts to detect the relevant patterns automatically.
- 05
Deliver findings
Provide the engineering findings, relevant evidence, and agreed supporting scripts or documentation.
What you receive
Deliverables matched to the investigation
Engineering analysis
A concise record of the observations, supporting evidence, and the most defensible findings from the available data.
CAN decoding results
Relevant decoded messages, signals, event timelines, and interpretation notes where the supplied data permits them.
Python analysis script
A focused, maintainable analysis script when repeatable detection or extraction is part of the agreed scope.
DBC findings or corrections
Documented DBC inconsistencies and, where agreed and supported by evidence, targeted definition corrections.
Technologies & formats
Automotive data and analysis environments
- CAN
Controller Area Network
A priority-based broadcast bus used for in-vehicle control communication.
- CAN-FD
CAN Flexible Data-rate
An extension of CAN with larger payloads and a faster data phase.
- DBC
CAN Database
A text format describing CAN messages, signals, nodes, scaling, and metadata.
- BLF
Binary Logging Format
A Vector binary container for timestamped bus communication.
- ASC
ASCII Log Format
A human-readable Vector format for timestamped bus events.
- MF4
MDF 4 Measurement
The fourth-generation ASAM MDF format for synchronized measurement data.
- CANoe
Vector CANoe
A development, simulation, test, and analysis environment for vehicle networks.
- CANalyzer
Vector CANalyzer
A Vector environment for recording and analyzing vehicle-network communication.
- Python
Python
A general-purpose language widely used for automotive analysis, tooling, and automation.
FAQ
Practical questions before an investigation
- Can you analyze CAN logs without a DBC?
- Yes, depending on the investigation. A DBC makes signal-level interpretation easier, but message timing, identifiers, payload changes, event correlation, and communication behavior can still be analyzed without one.
- What file formats can I send?
- Typical inputs include BLF, ASC, MDF or MF4 measurements, raw CAN dumps, and related DBC files. The exact format depends on the recording environment.
- Can you investigate intermittent issues?
- Yes. Long recordings can be searched for recurring event signatures, signal combinations, timing conditions, and communication anomalies.
- Can the analysis be automated?
- Yes. When an investigation must be repeated across many recordings, focused Python tooling can detect events or extract the relevant signals automatically.
Discuss the evidence
Have a CAN recording you need investigated?
Send a short description of the issue and the available logs or DBC files. I can review the context and determine the most practical next step.