Predictive Maintenance
Why this use case matters
Data science aids manufacturers to build better products. Engineering teams can predict the drivers of complex maintenance failures; prescribe the optimal design using predictive insights and utilizing simulations; ultimately delivering the optimal product to their end customer.
Techniques
Classification, Simulation, Anomaly Detection
Algorithms
SVM, Logistic Regression, Tree-Based Learning, Single-Shot Detection (SSD), Faster Region-based Convolutional Neural Nets (FR-CNN), Mask-R CNN, ResNet50, Retinanet, YOLO, RanSaC, TensorFlow Libraries, Filtering, Auto-Encoders
Outcome
Predictive maintenance assumes the likelihood of mechanical assets failure, so companies can optimize their maintenance response with scheduled, as opposed to unscheduled, maintenance.
Mosaic frequently helps customers extract actionable insights from sensor streams. In the blog below, we examine Aircraft Performance modeling.
Mosaic has compiled our industry expertise into a Machine Learning playbook for Manufacturing.
