Data logging without gaps: Page 3 of 4

July 29, 2011 //By Jochen Neuffer, Vector Informatik
Data logging without gaps
In order to simulate real situations for the communication networks in a vehicle it is necessary to perform extensive test drives in a real environment. Large amounts of data need to be acquired, recorded and, afterwards, accessed. Here is how this can be achieved.
Processing the Data

 To reduce the volume of incoming data, even during the test drive, these loggers let users start logging only in response to pre-defined events. In triggered logging, data is continually written to a ring buffer. When the trigger event occurs, this ring buffer is closed, and the data is saved. Logging is then resumed in a new ring memory. This method substantially reduces data volumes compared to continual logging. Depending on the configuration of the ring buffer, logged data may be available for a time period before the trigger and possibly for a configurable post-trigger time after the trigger occurs. The ring buffer is usually configured with special software (Figure 2). 

 

Figure 2: For data loggers, a broad choice of interfaces is mandatory

 The special script language Logger Task Language (LTL) can be used to execute complex logging tasks. This can be illustrated by a simple programming example: Creating a classing table during logging. First, the symbolic signals Speed and Brake from a database are automatically converted to LTL code. The test engineer only needs to add supplemental code for classing with the CLASSIFY operator:

                        VAR  Speed  = CAN1 DATA 200h [2 3]

                                   Brake  = CAN1 DATA 100h [3(0)]

                        CLASSIFY

                                   MyClassify COUNT (Brake)

                                               OVER Speed (20 CLASSES OF 10 BASE 0)

In this example, the Variable Speed value is defined in km/h over 20 classes, each class has a width of 10 km/h, and 0 km/h is set as the start value of the first class. This yields the following class distribution:

 

Class

Value range [km/h]

1

 0 -    9

2

10 -  19

...

...

19

180 - 189

20

190 - .....

 

The data of each classing task is saved in text-based results tables that can easily be read into a program such as MS Excel for post-editing.

 

 

 

Figure 3 Trigger

Design category: 

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