Rotating machinery plays a vital role in modern factory operations; however, the occurrence of component faults resulting from various factors can precipitate system failures and compromise product precision. To mitigate this challenge, we propose an anomaly detection methodology grounded in Kernel Density Estimation (KDE), utilizing the Median Absolute Deviation (MAD) Bandwidth Parameter to effectively discern unknown faulty signals, thereby alleviating the interference of unexpected data and minimizing performance degradation. Using edge computing devices, we introduce a comprehensive hardware-efficient anomaly detection architecture to overcome bandwidth constraints, data errors, and transmission delays. Demonstrating superior performance across diverse datasets and fault classification stages, our proposed anomaly detection method exhibits remarkable accuracy rates, achieving 94.68% under the NYCU-CERES dataset and 96.59% under the CWRU dataset for fault classification. Furthermore, it shows robust specificity and sensitivity, registering averages of 97% and 94.22% on the NYCU-CERES dataset, and 96.78% and 96.82% on the CWRU dataset, respectively. Additionally, synthesis utilizing TSMC 40nm process technology yields substantial reductions in area and power consumption by 25.9% and 16.5% compared to existing methods, respectively.

Rice blast disease is strongly dependent on environmental and climate factors. Integration of rice blast disease severity prediction model based on climate factors provides decision support framework for farmers to overcome from the problems of climate change scenarios. Major contribution of the proposed study is to predict the severity of rice blast disease using Linear SVM model. Prediction of severity of rice blast disease is Severity of rice blast disease is divided into four classes 0,1,2 and 3. Data imbalance is the most difficult problem in multi-class classification. Proposed study has handled imbalanced data efficiently using k-means SMOTE and SMOTE over sampling techniques to make training and testing data balance. Finally cross location and cross year models are developed using linear support vector machine and predicted severity of rice blast disease to the classes 0,1,2,3 respectively. Cross year and cross location models are cross validated using 5-fold cross validation.