Metropolis Predictive Maintenance AI

Inspired by the social stratification and machine worship in -Metropolis-, this project aims to prevent the human cost of machine failure prevalent in a poorly managed manufacturing environment. The system will also draw inspiration from the far-future predictive capabilities described in -Hyperion-, albeit on a much smaller scale.

Story: Small and medium-sized manufacturers (SMEs) often struggle with costly unplanned downtime due to machine failures. Existing MES solutions are expensive and complex. This project aims to create a more accessible and affordable AI-powered predictive maintenance system.

Concept: The system will collect data from existing machine sensors (vibration, temperature, pressure, etc.) commonly found in manufacturing equipment. Where sensors are unavailable or lacking, inexpensive Raspberry Pi-based sensor modules can be retrofitted. This data is then fed into an AI model (likely a time-series analysis model like LSTM or a gradient boosting model like XGBoost) trained to predict potential failures. The AI model will learn to identify patterns in the sensor data that precede machine breakdowns.

How it works:

1. Data Acquisition: Develop software to collect sensor data from existing MES or directly from sensors (Raspberry Pi with sensors as a fallback). Data will be stored in a time-series database (e.g., InfluxDB).
2. Data Preprocessing: Clean and preprocess the data. This includes handling missing values, smoothing data, and feature engineering (creating derived features like rolling averages, standard deviations, etc.).
3. Model Training: Train an AI model (LSTM or XGBoost) using historical sensor data and known machine failure events. The model will learn to predict the probability of a failure within a specified time window (e.g., next 24 hours, next week).
4. Prediction and Alerting: Continuously monitor real-time sensor data. Use the trained AI model to predict the probability of machine failure. If the probability exceeds a predefined threshold, trigger an alert (e.g., email, SMS, dashboard notification).
5. Dashboard: Create a user-friendly dashboard to visualize sensor data, predicted failure probabilities, and alert history. The dashboard will also provide insights into the root causes of failures and suggest preventative maintenance actions.

Niche and Low-Cost: Focus on SMEs who cannot afford expensive MES solutions. Use open-source software and readily available hardware components (Raspberry Pi). The system can be deployed on-premise or in the cloud, depending on the customer's needs.

High Earning Potential: The project can be monetized through a subscription-based model, offering different tiers based on the number of machines monitored and the level of support provided. Additional revenue streams can be generated through consulting services, helping customers implement and optimize the system. Furthermore, the predictive maintenance insights can be used to optimize production scheduling and reduce waste, leading to increased efficiency and profitability for the manufacturer.