Popular GitHub Repositories for Fault Tolerant

Weaviate is an open-source vector database that stores both objects and vectors, allowing for the combination of vector search with structured filtering with the fault tolerance and scalability of a cloud-native database?.

Fault-tolerant, highly scalable GPU orchestration, and a machine learning framework designed for training models with billions to trillions of parameters

[IJCAI-21] "Time-Series Representation Learning via Temporal and Contextual Contrasting"

智能故障诊断和寿命预测期刊（Journals of Intelligent Fault Diagnosis and Remaining Useful Life）

PyTorch per step fault tolerance (actively under development)

Bearing fault diagnosis model based on MCNN-LSTM

Python framework for building scalable multi-agent systems with built-in orchestration, LLM integration, and intelligent task processing. Features dynamic scaling, fault tolerance, and advanced load balancing.

?? Blades: A Unified Benchmark Suite for Attacks and Defenses in Federated Learning

Noise-Tolerant Paradigm for Training Face Recognition CNNs [Official, CVPR 2019]

Modern circuit breaker for Ruby & Rails. Thread-safe, fiber-ready async support. Built-in fallbacks, rich introspection, clean DSL. Memory-efficient with jitter & monitoring.

Machine learning applied to wind turbines incipient fault detection.

CNN applied to bearing signals for analysis

Python package that provides predictive models for fault detection, soft sensing, and process condition monitoring.

Implement GANs to generate time-series signals for imbalanced learning problem. The experiments are conducted using CWRU bearing data.

An official code for paper: TFPred: Learning discriminative representations from unlabeled data for few-label rotating machinery fault diagnosis

Vector Database implemented in Golang with support for full-text and vector search as well as fault tolerance via Raft.

Nerlnet is a framework for research and development of distributed machine learning models on IoT

[Infocom'22] Preemptive Migration Prediction Network for Proactive Fault Tolerant Edge Computing

Distributed, fault tolerant batch processing for Natural Language Applications and Search, using remote partitioning

Using DIgSILENT, a smart-grid case study was designed for data collection, followed by feature extraction using FFT and DWT. Post-extraction, feature selection. CNN-based and extensive machine learning techniques were then applied for fault detection.