Rotating machinery such as induction motors and gears driven by shafts are widely used in industry. A variety of techniques have been employed over the past several decades for fault detection and identification in such machinery. However, there is no universally accepted set of practices with comprehensive diagnostic capabilities. This paper presents a new and sensitive approach, to detect faults in rotating machines; based on principal component techniques and residual matrix analysis (PCRMA) of the vibration measured signals. The residual matrix for machinery vibration is extracted using the PCA method, crest factors of this residual matrix is determined and then machinery condition is assessed based on comparing the crest factor amplitude with the base line (healthy) level. PCRMA method has been applied to vibration data sets collected from several kinds of rotating machinery: a wind turbine, a gearbox, and an induction motor. This approach successfully differentiated the signals from healthy system and systems containing gear tooth breakage, cracks in a turbine blade, and phase imbalance in induction motor currents. The achieved results show that the developed method is found very promising and Crest Factors levels were found very sensitive for machinery condition. 1. Introduction Detection of faults in rotating machinery remains a big challenge especially for complex mechanical systems. Despite substantial advances in sensing and signal processing technologies, many difficulties remain to the successful detection of faults at an early stage of their development to avoid catastrophic failure [1, 2]. It has been known for some time through both analytical and experimental investigations that some machine faults can be directly related to acoustic and vibration harmonics [3]. With wind turbines, Jüngert [4] used two different acoustic techniques for blade inspection; those were local resonance spectroscopy and audible sound. The results showed that information about the internal structure of the inspected area can be obtained using features extracted from these signals. Sajauskas et al. [5] used secondary longitudinal surface acoustic waves (LSAW II) to detect surface defects on the inaccessible inner surface of sheet products and showed that this method was particularly efficient in the investigation of regular shape defects (cracks) with predictable orientation. Ghoshal et al. [6] studied four different algorithms for detecting damage on a wind turbine blade based on the vibration response of the blade: transmittance function, resonant comparison,
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