Random Forest
Assembly Line
Graph-based semi-supervised random forest for rotating machinery gearbox fault diagnosis
Random forest (RF) is an effective method for diagnosing faults of rotating machinery. However, the diagnosis accuracy enhancement under insufficient labeled samples is still one of the main challenges. Motivated by this problem, an improved RF algorithm based on graph-based semi-supervised learning (GSSL) and decision tree is proposed in this paper to improve the classification accuracy in the absence of labeled samples. The unlabeled samples are annotated by the GSSL and verified by the decision tree. The trained improved RF model is applied to the fault diagnosis for the rotating machinery gearbox. The effectiveness of the proposed algorithm is verified via hardware experiments using a wind turbine drivetrain diagnostics simulator (WTDDS). The results show that the proposed algorithm achieves better accuracy of classification than conventional methods in gearbox fault diagnosis. This study leads to further progress in the improvement of machine learning methods with insufficient and unlabeled samples.
The history of Amazon’s forecasting algorithm
Historical patterns can be leveraged to make decisions on inventory levels for products with predictable consumption patterns — think household staples like laundry detergent or trash bags. However, most products exhibit a variability in demand due to factors that are beyond Amazon’s control.
Today, Amazon’s forecasting team has drawn on advances in fields like deep learning, image recognition and natural language processing to develop a forecasting model that makes accurate decisions across diverse product categories. Arriving at this unified forecasting model hasn’t been the result of one “eureka” moment. Rather, it has been a decade-plus long journey.