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In day-to-day life 90% of industries use induction motors due toless maintenance, high efficiency, good Power factor and low cost. Maintenance of the induction motor is important for continuous operation in industries.40-60% of the fault in Induction motors is due to bearing failure. Unexpected bearing failures could cause industries to spend money on repairing and replacing the bearing, along with that other nearby components might damaged. Failure in bearing, decrease the plant’s operating efficiency, increases downtime, raises operating costs and in the worst case, it may cause injuries to workers. The proposed method detects and diagnoses the bearing fault using vibration signals. The fault gets detected by using the Machine learning classifier. The proposed method achieves high accuracy in detecting and diagnosing the bearing fault. The proposed work is implemented using Google Colab (colaboratory) software. The result demonstrates the usefulness of the suggested of strategy enhancing the maintenance of bearing in good condition and safe operation in the induction motor.

During major earthquakes, existing buildings have collapsed or suffered serious damage, resulting in number of losses, severe injuries, and deaths. Based on literature, the influence of this work reviewed the effects of interface with different materials and also to find how infilled frames behave in framed structure. This study's primary goal is to strengthen RC-framed structures and increase the ductility of infilled frames by using interface materials. The research offers a full range and points relevant to ductile parameters for more results in the field of infilled frames using interface materials. In parametric investigation the interface material with interface thickness and the combination of interface material with a particular frame, from that optimum value to be identified. This research benefits researchers, professionals, and specialists the behaviour of various structural systems, as well as innovative mitigation techniques that have been used in the literature to build progressive collapse resistance experimentally.

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Total of 56 bacterial isolates were isolated from five palm tree cultivated soil samples and screened their amylase production. In the preliminary screening, twenty four isolates exhibited amylase producing ability. Among these 24 isolates, an isolate KSU-6 showed maximum amylase enzyme production (37.52 U mL-1), and the isolate was identified as Bacillus licheniformis KSU-6. The maximum amylase production was optimized, and the conditions were pH 10.0, temperature 50°C, incubation period 44 h, inoculum concentration 2% (v/v), starch 0.2% (w/v) as carbon source, peptone 0.5% (w/v) along with malt extract 0.3% (w/v) as nitrogen sources for B. licheniformis KSU-6. The partially purified amylase stabled upto pH 12, temperature 80°C, 1-2 M salt concentration and 10 mM of different detergents. In this study conclude that the soil bacterium Bacillus licheniformis KSU-6 produced thermostable and alkaline amylase, which could be useful for industrial liquefaction and detergent industries. [ABSTRACT FROM AUTHOR]