We propose a new framework for spacecraft fault diagnosis based on combined parameter and mode online estimation using a sequential Monte Carlo method. Our method can detect and diagnose faults as parameter changes and hence can be considered as a probabilistic approach for the parameter-estimation-based fault diagnosis method which is one of the methodologies on quantitative model-based diagnosis. We derive an algorithm for spacecraft fault diagnosis by describing the parameter-estimation-based fault diagnosis method as a probabilistic inference problem and applying a modified sequential Monte Carlo method, obtained by incorporating fault-modes, risk-sensitivities on modes and kernel-smoothing techniques into the original method, to the problem. The proposed fault-diagnosis algorithm was applied to an artificial data simulating malfunctions of thrusters in rendezvous maneuver of spacecraft, and the feasibility of the method was confirmed.