Principal component analysis technique for early fault detection

Online condition monitoring and predictive maintenance are crucial for the safe operation of equipments. This paper highlights an unsupervised statistical algorithm based on principal component analysis (PCA) industrial induced draft (ID) fan. The high vibration issues in ID fans cause failure impellers and, sometimes, complete breakdown fan-motor system. system equipment should be reliable avoid such a sudden or faults equipment. proposed technique predicts fault system, being applicable other rotating equipment, also which data, historical is not available. major problem industry each every machinery individually. To this problem, three identical monitored together using technique. helps prediction faulty part time left forecasting schedule before breakdown. From results, it observed that PCA-based good fit early detection getting alarmed under as compared with conventional methods, including signal trend fast Fourier transform (FFT) analysis.