Hi ,
i am working on fault detection of a plant and i need a normalised value of the residuals for fault detection using fuzzy. how and what formula or simulnk block can help me sort out this issue. i am working in simulink.
Regards:
Hello,
if you have a signal x that you want to normalize between 0 and 1, and an estimation of the maximum (possible) magnitude for this signal (e.g. the l-inf norm) you can do it in the SIMULINK environment through the following operation: xn= 1+ x/(2*MAX).
Where xn is the normalized signal and MAX is the estimation for the maximum value. You can saturate the signal (based on MAX) to ensure that the xn will not be out of the range.
Best regards
Hi Lury Bessa
My residual is generated during simulation in simulink , like as fault introduced , residual is generated and that residual is fed to fuzzy controller as an input , then fuzzy diagnose fault and its severity. so i need to normalise a signal during simulation so that i can set range for residual in fuzzy controller.
Regards
The t r a n s l a t i o n a l linear v e s t i b u l o o c u l a r reflex compensates most accurately for high frequencies of head translation, with response magnitude decreasing with declining stimulus frequency. However, studies of the perception of translation typically report robust responses even at low frequencies or during prolonged motion. This inconsistency may reflect the incorporation of n o n d i r e c t i o n a l sensory information associated with the vibration and noise that typically accompany translation, into motion perception. We investigated the perception of passive translation in humans while dissociating n o n d i r e c t i o n a l cues from actual head motion. In a cue-dissociation experiment, i n t e r a u r a l (IA) motion was generated using either a linear sled, the mechanics of which generated noise and vibration cues that were correlated with the motion profile, or a m u l t i a x i s technique that dissociated these cues from actual motion. In a trajectory-shift experiment, IA motion was interrupted by a sudden change in direction (±30° diagonal) that produced a change in linear acceleration while maintaining sled speed and therefore mechanical ( n o n d i r e c t i 0 o n a l ) cues. During multi-axis cue-dissociation trials, subjects reported erroneous translation perceptions that strongly reflected the pattern of n o n d i r e c t i o n a l cues, as opposed to nearly v e r i d i c a l p e r c e p t s when motion and n o n d i r e c t i o n a l cues coincided. During trajectory-shift trials, subjects' p e r c e p t s were initially accurate, but erroneous following the direction change. Results suggest that n o n d i r e c t i o n a l cues strongly influence the perception of linear motion, while the utility of cues directly related to t r a n s l a t i o n a l acceleration is limited. One key implication is that “path integration” likely involves complex mechanisms that depend on n o n d i r e c t i o n a l and contextual self-motion cues in support of limited and transient o t o l i t h -dependent acceleration input.
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