AdvancedNovel Research60+ hours

AMNL: Deep Learning for Predictive Maintenance

State-of-the-Art Time Series Prediction with Novel Multi-Task Loss

Master time series prediction with a novel multi-task loss function. Build a CNN-BiLSTM-Attention model that achieves state-of-the-art on ALL NASA C-MAPSS benchmarks with +21% improvement over previous best methods.

Chapters

104

Sections

+21%

Improvement

Start Learning

Key Innovation: AMNL Loss

L_AMNL = 0.5 × L_RUL + 0.5 × L_health

Equal weighting provides superior regularization

+21.4% average improvement over DKAMFormer

First SOTA on ALL 4 NASA C-MAPSS datasets

+37% on complex multi-condition scenarios

5 sections

Introduction to Predictive Maintenance

Understanding the problem of Remaining Useful Life (RUL) prediction

6 sections

Mathematical Foundations

Essential mathematics for time series deep learning

AMNL: Deep Learning for Predictive Maintenance

Key Innovation: AMNL Loss

Foundations

Introduction to Predictive Maintenance

Mathematical Foundations

Data Pipeline

Architecture

Novel Loss (AMNL)

Training

Evaluation

Advanced

Production