In high-tech plants, the manufacturing stability and product quality are monitored through periodic sampling. As for those non-sampled workpieces, their quality is commonly monitored by a fault detection and classification (FDC) method. Nevertheless, it may fail to detect out-of-control (OOC) products if their corresponding manufacturing process parameters are all in-spec. In other words, unless those certain defected workpieces are selected for sampling measurements, they may not be detected through simply monitoring all the individual manufacturing process parameters. We have proposed a product quality fault detection scheme (FDS), which utilizes the classification and regression tree (CART) to build a single failure model (FML) for identifying the relationship between process parameters and OOC products. However, all the failure modes (FMs) are contained in the single FML, which makes it difficult to understand the causes of defected products. To remedy this problem, this paper develops an advanced fault detection scheme (AFDS). The AFDS builds the corresponding FM by CART for each individual failure cause and generates a FM manager via support vector machine (SVM) to manage all the FMs. Finally, the dual-phase concept is adopted to run the AFDS for achieving on-line real-time fault detection.