Multivariate fault isolation methods based on penalized regression are proposed.

The problem is transformed to a variable selection task for discriminant analysis.

The variable selection problem for DA is further linked to penalized regression.

The smearing effect is avoided.

Historical fault data or fault direction information is not required.