Okay, I need to create a Wikipedia-style document about "Predictive Maintenance" under the categories of Technology > Software > Open Source. Let me start by understanding what predictive maintenance is. From what I know, it's a method that uses data and analytics to predict when equipment might fail, allowing for timely maintenance. But since this is an open-source context, I should focus on software tools and platforms that support this.

First, the title should be "예측 유지보수" as specified. Then the introduction needs to explain what predictive maintenance is, its importance in technology, especially in software and open-source environments. Maybe mention how it's different from traditional maintenance approaches.

Next, I need sections. The user mentioned H2 and H3 headings. Let me think about possible sections. Maybe start with an overview of predictive maintenance, then move into technical aspects like data collection, machine learning models, tools used in open source. Also, include challenges and case studies.

Wait, the user wants it to be professional and informative. I should ensure that each section has enough depth. For example, under technical aspects, explain data collection methods, types of sensors, data processing steps. Then discuss machine learning algorithms like regression, neural networks, or anomaly detection models used in predictive maintenance.

Since it's open-source, I need to list specific tools and platforms. Maybe mention Apache Flink for real-time data processing, TensorFlow or PyTorch for ML models, Prometheus for monitoring, Grafana for visualization. Also, include projects like Open Predictive Maintenance (if any) or other relevant open-source initiatives.

I should also address challenges: data quality issues, model accuracy, integration with existing systems, and the need for continuous learning. Maybe add a section on best practices or future trends in predictive maintenance using open-source technologies.

Need to check if there are any specific open-source tools that are widely used. For example, InfluxDB for time-series data, ELK stack (Elasticsearch, Logstash, Kibana) for log analysis. Also, maybe mention Kubernetes for deploying ML models in a scalable way.

I should avoid being too technical without explanation. For instance, when mentioning TensorFlow, briefly explain what it is and why it's used here. Make sure the language is clear and accessible, even if some terms are technical.

Also, include references or related documents. Maybe link to open-source projects, research papers, or other resources that provide more details. But since this is a wiki-style document, maybe just list them in a "References" section.

Check for length: need at least 800 characters. The introduction alone should be around 200-300 words. Each section can have 150-200 words. Let me outline the sections:

1. Introduction
2. Overview of Predictive Maintenance
3. Definition and Importance
4. Differences from Traditional Methods
5. Technical Components
6. Data Collection and Sensors
7. Machine Learning Models
8. Open Source Tools and Platforms
9. Data Processing
10. ML Frameworks
11. Monitoring and Visualization
12. Challenges and Solutions
13. Case Studies or Examples
14. Future Trends
15. References

Wait, the user mentioned "필요시 참고 자료나 관련 문서 섹션" so maybe a references section is needed.

I need to make sure each H2 has sub-sections with H3 if necessary. For example, under Technical Components, have H3 for Data Collection and another for ML Models.

Also, use bullet points or lists where appropriate. Maybe in the tools section, list each tool with a brief description.

Avoid markdown formatting as per the user's request? Wait no, the user says to use markdown format. So I need to structure it properly with # for H1, ## for H2, ### for H3, and so on.

Check if all requirements are met: title, introduction, multiple sections with H2/H3, references, 800+ characters. Let me start drafting each part step by step.