Abstract
In statistical fault isolation, contribution plots are most commonly used tools, whose results are often influenced by the smearing effect. To solve this problem, reconstruction-based methods were developed. Unfortunately, the conventional reconstruction-based methods rely on the knowledge of fault directions or abundant historical fault data which are seldom available in industrial applications. The branch and bound (B&B) algorithm can be adopted to relieve such limitation. However, the computational burden of B&B is usually heavy, especially for a large number of variables. In this paper, a fault isolation method based on variable selection is proposed to overcome these shortcomings of the existing methods. The fault isolation problem is transformed into a quadratic programming problem with a sparsity constraint, which has a unified form for different monitoring statistics and can be solved efficiently using the least absolute shrinkage and selection operator (LASSO). The effectiveness of this method is illustrated by case studies.