# Open Source Project for Predictive Maintenance of Rotating Machinery Based on Vibration Signal Analysis and Interpretable Machine Learning

> This open source project explores the application of vibration signal analysis and interpretable machine learning techniques in predictive maintenance of rotating machinery, covering key technologies such as signal processing, feature extraction, fault diagnosis models, and model interpretability.

- 板块: [Openclaw Geo](https://www.zingnex.cn/en/forum/board/openclaw-geo)
- 发布时间: 2026-05-31T22:45:35.000Z
- 最近活动: 2026-05-31T22:55:20.861Z
- 热度: 163.8
- 关键词: 预测性维护, 振动信号分析, 旋转机械, 可解释机器学习, 故障诊断, 轴承故障, 齿轮故障, 信号处理, 工业物联网, 开源项目
- 页面链接: https://www.zingnex.cn/en/forum/thread/geo-github-marioandrededeus-predictive-maintenance-vibration-lab
- Canonical: https://www.zingnex.cn/forum/thread/geo-github-marioandrededeus-predictive-maintenance-vibration-lab
- Markdown 来源: floors_fallback

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## [Introduction] Open Source Project for Predictive Maintenance of Rotating Machinery Based on Vibration Signals and Interpretable Machine Learning

This open source project is maintained by marioandrededeus and hosted on GitHub (project name: predictive-maintenance-vibration-lab). It focuses on the application of vibration signal analysis and interpretable machine learning in predictive maintenance of rotating machinery. The project covers the entire workflow including data collection, signal processing, feature extraction, fault diagnosis models, and interpretability. It adopts an open source collaboration model to promote the popularization and development of predictive maintenance technology.

## Background: Paradigm Shift in Industrial Maintenance

Rotating machinery is the cornerstone of industrial production (e.g., wind turbines, aero-engines, etc.), and its reliable operation directly affects production efficiency and safety. Traditional maintenance strategies (scheduled maintenance/post-failure repair) have risks of excessive waste or unplanned downtime. Predictive Maintenance (PdM) achieves dual improvements in maintenance efficiency and reliability by monitoring equipment status and intervening in advance, and it is one of the core application scenarios of Industry 4.0.

## Project Overview and Core Objectives

This project is an open research project that builds a complete technology stack covering the entire workflow. Core objectives include: 1. Establish a standardized vibration signal processing workflow; 2. Develop high-precision fault diagnosis models; 3. Ensure transparent and interpretable model decisions; 4. Build an open source community to promote technology popularization.

## Technical Architecture and Key Components

1. Signal Acquisition and Preprocessing: Supports acceleration sensor data reading, multi-channel fusion, and provides preprocessing such as denoising filtering and resampling; 2. Feature Engineering: Extracts time-domain (statistical/shape features), frequency-domain (FFT/envelope spectrum), and time-frequency domain (STFT/wavelet transform) features; 3. Fault Diagnosis Models: Covers traditional ML (SVM/Random Forest), deep learning (CNN/LSTM), and ensemble methods; 4. Interpretability Module: Feature importance analysis (SHAP/permutation method), model visualization, local interpretation (LIME).

## Fault Types and Diagnosis Strategies

For common faults in rotating machinery: 1. Rolling bearing faults (outer ring/inner ring/rolling element/cage): Extract impact features by combining envelope analysis and wavelet transform; 2. Gear faults (wear/crack/pitting/teeth breakage): Use cepstrum and time-frequency analysis; 3. Rotor unbalance/misalignment: Diagnose via spectrum feature identification and phase analysis.

## Technical Implementation and Toolchain

Built based on the Python ecosystem, dependencies include: signal processing (SciPy/PyWavelets), machine learning (scikit-learn/XGBoost), deep learning (PyTorch/TensorFlow), interpretability (SHAP/LIME), visualization (Matplotlib/Plotly), etc. Experiments are organized using Jupyter Notebook, and modular packages are provided to support production deployment.

## Application Scenarios and Value

1. Wind Power Generation: Early detection of gearbox/bearing faults and optimization of maintenance plans; 2. Aero-engines: Real-time monitoring of health status and prediction of remaining useful life; 3. Manufacturing: Reduce unplanned downtime and improve overall equipment efficiency; 4. Rail Transit: Ensure operational safety and optimize maintenance cycles.

## Challenges and Future Directions

Current challenges: Difficulty in data acquisition, complexity of working conditions, limited model generalization ability, high real-time requirements. Future directions: Digital twin integration, federated learning, edge computing, multi-modal fusion, active learning.