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To improve the speed and precise of online monitoring and diagnosis for satellite using satellite telemetry data.Design/methodology/approach – In monitoring system, a fuzzy range which gives the probability of alarm for telemetry channels using fuzzy reasoning is outlined. A failure confidence factor is presented to modify the traditional real‐time diagnosis algorithm based on multisignal model to describe the relative failure possibility for suspected components. According to the modified real‐time diagnosis algorithm based on multisignal model, it rapidly generates the states for all the components of the system such as good, bad, suspected and unknown. Then the failure probability for suspected components is obtained by Mamdani fuzzy reasoning algorithm.Findings – The experimental results reveal that the diagnosis system can not only improve diagnosis of speed but also can improve the diagnostic precision by giving failure probability for suspected fault components which may be potential failure components.Research limitations/implications – It requires the clear fault dependency relationship between components and tests.Practical implications – A very useful method for researchers and engineers who are engaged in satellite online monitoring and diagnosis.Originality/value – This paper presents a new method combining multisignal model and fuzzy theory to give the failure probability for suspected components which improves the speed and precision for fault diagnosis.

To improve accuracy and efficiency of multi‐fault recognition and localization for large‐scale system such as satellite.

First, fault propagations of a system are modeled by a digraph, which composes of nodes and arcs. Each arc is associated with information about propagation probability and propagation strength. Then, based on consistency‐based theory and semantic theory of abstractions, hierarchical diagnosis model of a system is built. Finally, according to a two‐way hierarchical diagnosis strategy, two incorporated algorithms are adopted which are the Lagrangian relaxation algorithm and the “method of propagation strength”.

Hierarchical model can greatly improve efficiency of diagnosis compared with un‐hierarchical one. The combined qualitative and quantitative knowledge can improve fault resolution.

The propagation probability and propagation strength must been known.

The method shows its superiority when it is applied to complex system such as spacecraft.

A novel hierarchical framework for large‐scale system multi‐fault diagnosis, which include some new ideas and algorithm is put forward.