Chemical process accidents have tremendous impacts on the environment, as well as the sustainability of the chemical industry. Fault detection and diagnosis (FDD) are important to ensure safety and stability of chemical processes. However, the scarcity of fault samples has limited the wide application of FDD methods in the industry. In this work, we present an artificial immune system (AIS)-based FDD approach for diagnosing faults in the chemical processes without historical fault samples. This approach mimics the vaccine transplant in the medicine discipline. Historical fault samples collected from other chemical processes of the same type are used to generate vaccines to help construct fault antibody libraries for the diagnosis objective process. Case studies on the Pensim process and laboratory-scale distillation columns illustrate the effectiveness of our approach.