I have this two models, which one is better? what is a better design??

Model 1:

+------+          +--------+
| type |-|--+     | Data   |
+------+    |     +--------+
| id   |    |     | Mach ID|
| type |    |     | Time   |
| len  |    +----&lt;| type ID|
+------+          | data   | (binary max 8)
                  +--------+


Model 2:                    

+---------+                 
| Data    |                 
+---------+                 
| Mach ID |                
| Time    |                 
| Type_1  |                 
| Type_2  |                
| ...     |                 
| Type_n  |                 
+---------+


(there are other tables too, like machine and client)

all types are previusly defined, ex:


id 08 is the temperature1 and its a float 4 bytes.
id 16 is a sensor and its an int 4 bytes.
id 125 is status 2 bytes.


there is only 22 types defined, but types can grow in the future.
the problem is not all MachID have all the types.
If i do the model 2 a lot of fields will have null.

All i have to do is store these data and display graphics per MachID.
And to make graphs, i only need time vs type. so i can make sql selects to get tables like this

+------+--------+--------+-----+--------+
| Time | type_1 | type_2 | ... | type_n |
+------+--------+--------+-----+--------+
|      |        |        |     |        |


and graph, but getting this is more difficult with Model 1.

thanks for your help :)

ps:
every month i get like 15000 records average per MachID.
and sorry for my bad english
I would use your second model.  The first might be more compact, but would require constantly CASTing your data; the second will have many NULLs, but they take up little space.

The second model looks denormalized at first, but if I understand your model right it's really not.  You're not really packing 22 records into one, rather you're storing reports each of which may have up to 22 distinct strongly-types measurements.  Some reports do not have every measurement, and that's fine.