This paper proposes a new approach for online fault diagnosis in dynamic systems, combining a Particle Filtering (PF) algorithm with a classic Fault Detection and Isolation (FDI) framework. Of the two methods, FDI provides deeper insight into a process; however, it cannot normally be computed online. Our approach uses a preliminary PF step to reduce the potential solution space, resulting in an online algorithm with the advantages of both methods. The PF step computes a posterior probability density to diagnose the most probable fault. If the desired confidence is not obtained, the classic FDI framework is invoked. The FDI framework uses recursive parametric estimation for the residual generation block and hypothesis testing and Statistical Process Control (SPC) criteria for the decision making block. We tested the individual methods with an industrial dryer.