Due to the shortcomings of traditional fault diagnosis system, such as too complicated hardware system and fault recognition algorithm, a distributed intelligent fault diagnosis system based on particle filter was proposed and studied. Real-time collection of distributed multi-variable parameters was realized by adopting ZigBee wireless sensor network, on-line processes variable data based on particle filter, and precise estimate about real system states were obtained based on simple pattern recognizing algorithm in order to realize the intelligent forecast and diagnose for system fault. The distributed fault diagnosis system includes ZigBee network, particle filter algorithm, system states model and malfunction mode recognition. Particle filter can filter data collected by sensor, suppress and eliminate the interference or significant error that affects the estimate of system states based on sequential importance sampling and Monte-Carlo method. Finding a system state model that has the minimum sum of residuals with an estimate curve about system states from a particle filter is the process of the malfunction mode recognition. Realization of the distributed intelligent fault diagnosis system and the result of the experiment show that the system can realize the remote monitor, accurate state estimation and fault diagnose, and it has the advantage of low cost, high reliability and easy to realize. The work can expand the application range of distributed sensor network and improve the diagnosis level of the fault diagnosis system.