Distributed Fault Detection Using RBM Neural Networks
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Solution Overview
Problem
Existing fault detection systems in computer systems face challenges such as high latency due to centralized data processing, the need for extensive labeled data for all possible fault scenarios, and increased response times during emergencies, which can lead to delayed fault handling and potential system damage.
Innovation Solution
A distributed fault detection system using Restricted Boltzmann Machine (RBM) neural networks deployed on fog nodes for decentralized processing, combined with federated learning, where each node updates its model locally and sends patches to a central server for aggregation, reducing the need for extensive data collection and enabling quick response to faults without relying on a central server for processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If centralized data processing is used for fault detection, then system architecture is simplified, but latency increases and response time deteriorates
Solution Approach 1:
The patent segments the centralized fault detection system into distributed fog nodes, each capable of independent fault detection and classification. This segmentation eliminates the single point of congestion while maintaining architectural clarity through modular deployment of identical nodes.
Solution Approach 2:
The patent transitions from a single centralized processing dimension to a multi-dimensional distributed architecture where fog nodes operate at the edge of the network. This dimensional expansion allows parallel processing across multiple locations, reducing latency without proportionally increasing complexity.
2Measurement precision
If extensive labeled data is collected for all possible fault scenarios, then fault detection accuracy is improved, but data collection requirements and system complexity increase
Solution Approach 1:
The patent applies partial action by training fog nodes on representative fault scenarios rather than all possible scenarios. Each node processes only the data relevant to its local environment, achieving sufficient accuracy for practical purposes without requiring exhaustive data collection for every conceivable fault type.
Solution Approach 2:
The patent implements local quality by allowing each fog node to develop fault detection capabilities tailored to its specific environment and fault history. This enables high accuracy for locally relevant faults without requiring the system to collect and process data for all possible fault scenarios across the entire network.
3Stability of the object's composition
If centralized processing is used, then model updates are unified, but response time during emergencies increases
Solution Approach 1:
The patent implements preliminary action by pre-configuring fog nodes with initial fault detection models and classification capabilities before emergencies occur. During critical events, nodes can immediately process and respond to faults using their pre-loaded knowledge, eliminating the delay of waiting for centralized model updates during the emergency response window.
Solution Approach 2:
The patent introduces dynamics by allowing fog nodes to autonomously update their local models based on real-time fault detection experience while maintaining the ability to synchronize with centralized updates. This dynamic capability enables fast local response during emergencies while preserving model consistency through periodic synchronization.
Data Source
AI summary
A first computing node of a system can receive sensor data about a physical environment. The first computing node can analyze the sensor data with a restricted Boltzmann machine (RBM) neural network to determine whether there is a fault condition in the physical environment, an identification of the fault condition being omitted from data used to train the RBM neural network. The first computing node can update the RBM neural network based on the sensor data to produce a first updated RBM neural network. The first computing node can send a first patch indicative of the first updated RBM neural network to a central server. The first computing node can receive, from the central server, information indicative of a second updated RBM neural network, the second updated RBM neural network being based on an aggregation of the first patch and of a second patch generated by a second computing node.