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Smart predictive maintenance can reduce costs, improve reliability, optimize maintenance schedules, increase safety and enable data-driven decision making. By leveraging advanced technologies and data analytics, organizations can use smart predictive maintenance to predict equipment failures, schedule maintenance proactively and avoid unexpected breakdowns. This approach minimizes downtime, saves on emergency repairs and extends the equipment’s lifespan. Real-time monitoring and analysis of equipment health allow for early detection of abnormalities, ensuring prompt action to prevent major failures.
Smart predictive maintenance that uses Machine Learning (ML) at the edge implements predictive maintenance algorithms and models directly on edge devices, such as sensors or IoT devices, rather than relying on a centralized cloud- or server-based system. This approach brings several benefits, including reduced latency, improved real-time decision making, enhanced data privacy and reduced bandwidth requirements.
Implementing Smart Predictive Maintenance at the Edge
Here's a step-by-step overview of how you can implement smart predictive maintenance at the edge using MPLAB® Machine Learning Development Suite:
1. Data Collection
Edge devices equipped with sensors or IoT devices collect relevant data about the equipment or system being monitored. This data can include sensor readings, temperature, vibration, pressure or any other relevant parameters. This data is imported to MPLAB Machine Learning Development Suite as a raw or .csv file.
2. Data Preprocessing
In the MPLAB Machine Learning Development Suite, collected data is preprocessed to clean, normalize and transform it into a suitable format for analysis. This may involve removing outliers, handling missing values or scaling the data.
3. Feature Extraction
Relevant features are extracted from the preprocessed data. Feature extraction techniques can include statistical analysis, time series analysis, Fourier transforms, wavelet analysis or other domain-specific methods. This is also done within the development suite.
4. Model Development
ML models, such as classification or anomaly detection algorithms, are developed in the MPLAB Machine Learning Development Suite using the extracted features. This can involve techniques like decision trees, neural networks or ensemble methods. The MPLAB Machine Learning Development Suite automatically optimizes the model for memory size and accuracy depending on the MCU at hand.
5. Model Training and Updating
The ML model is trained using historical data and considers both normal and faulty operating conditions. The model can also be taught to recognize analogous conditions in the form of unseen data. The development suite makes it easy for you to periodically update or retrain the model with new data to adapt to changing operating conditions.
6. Model Deployment
The trained model is deployed on the edge device, allowing it to make real-time predictions or detect anomalies locally without relying on a centralized server. This enables faster response times and reduces dependence on network connectivity.
7. Alert Generation and Decision Making
Based on the predictions or detected anomalies from the deployed model, the model can generate alerts or notifications on the edge device. This allows for quick response and decision making, such as scheduling maintenance activities or taking corrective actions to prevent equipment failure.
8. Continuous Monitoring and Optimization
The edge device continues to monitor the equipment or system in real time and collects new data. Based on the shift between the baseline and new data, the ML model can be retrained and updated. This iterative process helps to continuously improve the accuracy and effectiveness of the predictive maintenance system.
We designed the MPLAB Machine Learning Development Suite to satisfy computational and storage capacity constraints on edge devices. Models that are implemented on the edge are much simpler than cloud-based solutions.