Neurofuzzy methodologies for rotating machinery fault diagnosis

Although significant success has been demonstrated in employing neurofuzzy methodologies for rotating machinery fault diagnosis, most applications are still rather specialised and fragmented and considerable effort is still needed in order to move towards more generic and reliable diagnostic solutions. This paper describes some steps taken towards this goal. Work currently carried out is focused on developing a generic machinery off-line intelligent diagnostic system based on vibration data. Here, neurofuzzy techniques are shown to be applicable for diagnosing common faults such as unbalance, misalignment and various types of bearing faults, with both single and multiple fault scenarios and in the presence of varying levels of noise.