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This paper develops a data-driven approach for incipient fault diagnosis based on ANFIS and Takagi–Sugeno (TS) interval observers. First, the nonlinear bioreactor system is identified using an adaptive neuro-fuzzy inference system (ANFIS), which results in a set of polytopic TS models derived from measurement data. Second, a bank of TS interval observers is deployed to detect sensor and process faults using adaptive thresholds. Unlike other works that require training fault data, only fault-free data is considered for ANFIS learning in this work. Fault insolation is based on residual generation and evaluated on a fault signal matrix (FSM). Parametric uncertainty and measurement noise are considered to guarantee the method's robustness. The effectiveness of the proposed method is tested on a well-known bioreactor Continuous stirred tank reactor system (CSTR) reference simulator. • The hybrid ANFIS/interval observer method diagnoses incipient faults in bioreactors. • ANFIS identifies bioreactor dynamics and makes convex Takagi–Sugeno models. • ANFIS learning is simplified using only free-fault data, enhancing model precision. • Interval observers with residual analysis achieve fault detection and isolation. [ABSTRACT FROM AUTHOR]