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Bearing is regarded as one of the core elements in rotating machines and its fault diagnosis is essential for better reliability and availability of the rotating machines. This paper puts forward an intelligent vibration signal-based fault diagnosis approach for bearing faults identification at an early stage, irrespective of speed conditions. The proposed methodology comprises of a frequency shift-based hybrid signal processing technique that involves a combination of Hilbert Transform (HT) and Discrete Wavelet Transform (DWT) followed by sliding window-based feature extraction. Thereafter, a newly developed Henry Gas Solubility Optimization (HGSO) is implemented to select the relevant features. At last, the optimal attributes are used to train the Artificial Neural Network (ANN) model for the classification of the different bearing faults. To test the effectiveness of the speed independent model, experimental validation was done with constant and varying speed conditions. The results demonstrate that the proposed methodology has a tremendous potential to eliminate unplanned failures caused by bearing in rotating machinery.

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Nickel-based thick hardface coatings are employed in nuclear power plants because of their superior wear and high-temperature resistance properties. Unfortunately, fabrication of a crack-free coating with less dilution is difficult using the conventional hardfacing techniques like Plasma Transferred Arc (PTA), Metal Inert Gas (MIG) etc. A sound coating having optimum hardness and better wear resistance property is essential for reactor applications. The current work aims to investigate the wear behaviour of Ni-Cr-B-Si hardface coating deposited on 316LN stainless steel, where metal-cored filler wire was used as a consumable in the Cold Metal Transfer (CMT) welding process. The hardface coating was characterized for its hardness and microstructure. Apart from that, pin-on-disc wear tests were performed using the extracted pin specimens from the hardfaced substrate. From this experiment, a micro-hardness of 531.24 ± 73.15 HV0.5 was measured across the coating cross-section. The microstructure analysis revealed the presence of precipitates like borides and carbides in the coating. Further, a specific wear rate of the order of 10−14 m3/Nm was found from the wear tests. Confocal microscopy on the worn surfaces of the pin specimens revealed, the surface damages mostly occurred by ploughing and fracture. The investigation ensures that CMT can be used for depositing crack-free, low dilution and wear-resistant hardface coatings in nuclear industries.

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