# MOCRN: A Physics-Guided Multimodal Neural Network Framework for Analog Circuit Fault Detection

> This article introduces how the MOCRN framework combines physics-guided modeling and multimodal deep learning to enable dual-task learning for analog circuit fault identification and degradation level estimation.

- 板块: [Openclaw Geo](https://www.zingnex.cn/en/forum/board/openclaw-geo)
- 发布时间: 2026-05-25T04:42:27.000Z
- 最近活动: 2026-05-25T04:51:57.111Z
- 热度: 150.8
- 关键词: 模拟电路, 故障检测, 多模态神经网络, 物理引导建模, 深度学习, 可靠性工程, 序数回归, 电路仿真
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## MOCRN Framework Overview: Physics-Guided Multimodal Neural Network for Analog Circuit Fault Detection and Degradation Estimation

MOCRN (Multimodal Ordinal Circuit Reliability Network) is a physics-guided multimodal neural network framework for analog circuit fault detection. It combines physics-guided modeling and multimodal deep learning to enable dual-task learning for fault type identification and degradation level estimation.

Original Authors: J.P. Teshan Jayasinghe, Gayani Kulathunga, Rahul Thakur
Source: GitHub Project MOCRN-Multimodal-Ordinal-Circuit-Reliability-Network
Publication Time: April 2026 (Paper published in the Circuits, Systems, and Signal Processing journal)
Original Link: https://github.com/teshankj/MOCRN-Multimodal-Ordinal-Circuit-Reliability-Network

## Background: Three Key Challenges in Analog Circuit Reliability Detection

Analog circuit reliability detection faces three key challenges:
1. **Progressive Degradation**: Faults exhibit continuous and progressive behavior, making it difficult for traditional binary judgment to capture early warnings;
2. **Diverse Fault Modes**: The same component fault may have different symptoms, while faults in different locations may have similar characteristics;
3. **Limitations of Traditional Methods**: Relying on expert experience, it is difficult to handle complex mixed-signal systems and SoC designs, making manual analysis increasingly impractical.

## Core Innovations of the MOCRN Framework: Physics Guidance + Multimodal Fusion + Ordinal Reliability Modeling

Core innovations of the MOCRN framework:
1. **Physics Guidance**: Embed circuit physics knowledge into data generation and feature extraction to avoid black-box fitting and ensure outputs comply with physical laws;
2. **Multimodal Fusion**: Late fusion of two information sources: raw waveforms (temporal information) and statistical features (frequency domain/energy distribution);
3. **Ordinal Reliability Modeling**: Adopt a hybrid evidence ordinal loss function to explicitly model the natural order relationship of degradation levels, reflecting the progressive process from health to severe degradation.

## Dataset Generation: Based on LTspice Simulation and Structured Fault Injection

Dataset generation and fault injection mechanism:
- **Simulation Tool**: Use LTspice to generate training data, which has the advantages of repeatability, controllability, and scalability;
- **Fault Injection**: Structured parameter space method, defining fault types (FT) and degradation levels (0-100 to quantify severity);
- **Parameter Perturbation**: Replace Monte Carlo random variations with deterministic trigonometric functions (e.g., 0.1*sin(run*0.6283)) to ensure diversity and reproducibility;
- **Test Circuits**: Covers voltage rectifiers, peak detectors, and diode clamp circuits, each containing 4-6 fault types (e.g., diode leakage, resistance change, etc.);
- **Simulation Settings**: Transient analysis with a time step of 4-40ms, saving key node voltages and branch currents.

## Model Architecture: Multi-Task Learning and Hybrid Evidence Ordinal Loss Design

Model architecture and key technologies:
- **Multi-Task Learning**: Simultaneously output fault type classification probabilities and degradation level estimates, sharing feature representations;
- **Feature Engineering**: Extract time-domain (mean, variance, peak value, etc.) and frequency-domain (FFT energy spectrum) statistical features, which are input together with raw waveforms;
- **Loss Function**: Cross-entropy loss for fault identification, and hybrid evidence ordinal loss for degradation estimation (based on evidence theory, expressing uncertainty and respecting the order of degradation);
- **Training Method**: K-fold cross-validation to prevent overfitting, with configurations managed via YAML files.

## Experimental Validation: Performance Metrics and Engineering Application Value

Experimental validation and performance evaluation:
- **Evaluation Metrics**: Accuracy and F1 score for fault identification (focusing on imbalanced datasets), MAE and RMSE for degradation estimation;
- **Feature Importance**: Identify key circuit features through consensus analysis to improve model interpretability;
- **Engineering Value**: Enable early detection of soft faults, support predictive maintenance, and apply to high-reliability fields such as aerospace and medical equipment.

## Technical Insights and Future Outlook: A Model of Combining AI with Domain Knowledge

Technical insights and future outlook:
- **AI for Science**: Demonstrate a model of combining domain knowledge (physics guidance) with data-driven methods; similar physics-informed neural network ideas can be applied to multiple engineering fields;
- **Hardware Testing**: Simulation data generation reduces reliance on expensive hardware platforms, providing an efficient way for AI model training;
- **Machine Learning**: Ordinal regression methods provide references for ordered category prediction problems (e.g., disease severity, equipment aging);
- **Open Source Value**: The GitHub repository provides complete code (data generation, training, pre-trained models) to support reproduction and extension to other analog circuits.