Accelerometers, used as vibration pickups in machine health monitoring systems, need physical connection to the machine tool through cables, complicating physical systems. A non-contact laser based vibration sensor has been developed and used for bearing health monitoring in this article. The vibration data have been acquired under speed and load variation. Hilbert transform (HT) has been applied for denoising the vibration signal. An extraction of condition monitoring indicators from both raw and envelope signals has been made, and the dimensionality of these extracted indicators was deducted with principal component analysis (PCA). Sequential floating forward selection (SFFS) method has been implemented for ranking the selected indicators in order of significance for reduction in the input vector size and for finalizing the most optimal indicator set. Finally, the selected indicators are passed to k-nearest neighbor (kNN) and weighted kNN (WkNN) for diagnosing the bearing defects. The comparative analysis of the effectiveness of kNN and WkNN has been executed. It is evident from the experimental results that the vibration signals obtained from developed non-contact sensor compare adequately with the accelerometer data obtained under similar conditions. The performance of WkNN has been found to be slower compared to kNN. The proposed fault detection methodology compares very well with the other reported methods in the literature. The non-contact fault detection methodology has an enormous potential for automatic recognition of defects in the machine, which can provide early signals to avoid catastrophic failure and unplanned equipment shutdowns.

The development of surface modification technique has been the subject of the studies regarding the fatigue performance and biological characterization of the modified layers. In the present research work, powder mixed electric discharge machining (PMEDM) a novel nonconventional machining technique has been proposed for surface modification of β -Ti implant for orthopedics application. The surface topography and morphology like roughness, surface cracks, and recast layer thickness of each of the machined specimens were investigated using Mitutoyo surface roughness tester and field-emission scanning electron microscopy (FE-SEM), respectively. This study aims to investigate the effect of surface characteristics of PMEDM process on the fatigue performance and bioactivity of β -Ti implants and moreover a comparative analysis is made on the fatigue performance and biological activity of specimens machined with presently used machining methods like electric discharge machining (EDM) and mechanical polishing. The high cycle fatigue (HCF) performance of polished specimens was superior and had no adverse effect of microstructure on fatigue endurance. As expected, the fatigue behavior of β -Ti implant-based alloy, after undergoing EDM treatment, is poorly observed due to the microrough surface. The fatigue performance is dependent on microstructure and surface roughness of the specimens. Subsequent PMEDM process significantly improves the fatigue endurance of β -Ti implant-based alloy specimens. PMEDMed surface with micro-, sub-micro-, and nano-structured topography exhibited excellent bioactivity and improved biocompatibility. PMEDMed surface enabled better adhesion and growth of MG-63 when compared with the polished and EDMed substrate. Furthermore, the differentiation results indicated that a combination of nanoscale featured submicrorough PMEDMed surface promotes various osteoblast differentiation activities like alkaline phosphatase (ALP) activity, osteocalcin production, the local factor osteoprotegerin, which inhibits osteoclastogenesis.