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The causes of biosensor signal-to-noise ratio decrease have been investigated by means of fault tree analysis (FTA) relying on fuzzy reasoning to account for uncertainty. Using the fuzzy FTA methodology proposed herein, all ultimate causes or combination of causes, attributed to the device components, the interaction of the device components or the human factor, that are responsible for or contribute to the top event, have been recognized and quantified based on 1) measurements for the deterministic contributors and 2) experience for the stochastic contributors. The tree structure has been designed by combining deduction and induction, top-down and bottom-up techniques, thus establishing a dialectic tradeoff which brings this method closer to scientific logic, permitting the introduction of deeper knowledge into the surface or experiential knowledge level characterizing FTA. The methodology has been implemented in the case of bilayer lipid membrane biosensors and has proven to be an efficient tool for internal diagnostics and fault compensation. The partitioning of the space of the variables and the determination of the fuzzy rules serving as the inference engine for diagnosis have been achieved by using experts' opinion through a modified four-stage Delphi method. An extended case example of the diagnostic operation is presented, exploiting the causes of "Loss of Response" of a biological oxygen demand (BOD) biosensor measuring under working conditions, proving the effectiveness of this method, which can minimize research and development expenditures for corrective action, i.e., increase of biosensor tolerability degree.