Intelligent fault diagnosis of synchronous generators

• A 3 kVA generator fault model is used to diagnose faults in a 5 kVA generator.
• The model is trained using 3 kVA generator data and 5 kVA generator (no-fault data).
• System-dependent dimensions are removed using nuisance attribute projection (NAP).
• Classification and regression tree (CART) is used as a back-end classifier with NAP.
• NAP improves the performance of the fault identification system.

Condition based maintenance (CBM) requires continuous monitoring of mechanical/electrical signals and various operating conditions of the machine to provide maintenance decisions. However, for expensive complex systems (e.g. aerospace), inducing faults and capturing the intelligence about the system is not possible. This necessitates to have a small working model (SWM) to learn about faults and capture the intelligence about the system, and then scale up the fault models to monitor the condition of the complex/prototype system, without ever injecting faults in the prototype system. We refer to this approach as scalable fault models.We check the effectiveness of the proposed approach using a 3 kVA synchronous generator as SWM and a 5 kVA synchronous generator as the prototype system. In this work, we identify and remove the system-dependent features using a nuisance attribute projection (NAP) algorithm to model a system-independent feature space to make the features robust across the two different capacity synchronous generators. The frequency domain statistical features are extracted from the current signals of the synchronous generators. Classification and regression tree (CART) is used as a back-end classifier. NAP improves the performance of the baseline system by 2.05%, 5.94%, and 9.55% for the R, Y, and B phase faults respectively.