Industrial processes require precision, and anomalies in sensor data often signal issues like faulty components. AI-based anomaly detection aids engineers in early problem identification, enhancing maintenance and efficiency. With 86% of manufacturing executives viewing smart factories as critical for future competitiveness, AI’s role is pivotal.
Traditional methods struggle with modern factory complexity, where thousands of sensors generate data. Engineers can’t manually analyze this data and may miss complex patterns. AI overcomes this by processing large datasets to detect anomalies that humans might miss, leveraging AI’s scale alongside engineers’ expertise for robust solutions.
Designing an AI-Based Anomaly Detection Solution
Creating an AI-based solution involves planning, data gathering, deployment, and integration. Engineers need a deep understanding of algorithms and the operational environment to effectively identify potential issues.
Planning and Data Gathering
The process starts with defining the problem, assessing sensor data, and identifying potential anomalies. Organizations new to AI should begin with a proof-of-concept to demonstrate value. High-quality data is essential, requiring continuous monitoring and manual checks for accuracy.
Data Exploration and Preprocessing
Data, primarily from sensors, includes time series data like temperature and pressure. Initial steps involve organizing and preprocessing this data, handling missing values, and removing outliers. Choosing the right anomaly detection technique depends on data characteristics and available resources.
Model Selection and Training
Choosing the right training approach for an AI model involves experimenting with supervised and unsupervised learning based on data availability.
Supervised Learning
Used when historical data is labeled as normal or anomalous. Engineers manually label data, aligning it with logs or observations. Tools like Classification Learner in MATLAB® help test multiple machine learning methods. For example, Mondi Gronau used it to predict failures in plastics manufacturing. The model predicts if new data is normal or anomalous.
Unsupervised Learning
Necessary when labeled data is scarce. The model learns normal data characteristics, flagging deviations as anomalies. This approach identifies unusual patterns, even without previous labels.
Feature Engineering
Instead of training on raw data, extracting useful features enhances model efficiency. Predictive Maintenance Toolbox™ offers tools to extract and rank relevant features, improving model performance. For complex data like images, deep learning can automatically extract patterns. IMCORP used this to detect faults in power cables, though it requires larger datasets and resources.
Validation and Testing
AI model reliability is ensured by splitting data into training, validation, and test sets. Performance metrics like precision and recall help fine-tune models for specific problems.
Deployment and Integration
A trained AI model is valuable when deployed for real-time predictions. Engineers choose deployment environments based on computational needs and scalability, from edge devices to cloud platforms. MATLAB Compiler™ and MATLAB Coder™ assist in creating applications and code for integration. Aerzen Digital Systems deployed a cloud-based solution for detecting issues in industrial complexes.
Integration involves developing APIs and data pipelines to ensure the model receives properly formatted input, working seamlessly with other system components.
Conclusion
AI-based anomaly detection significantly enhances manufacturing efficiency. By reducing defects and optimizing maintenance, AI offers immense rewards in efficiency, cost savings, and competitiveness. As manufacturing evolves, AI’s role in driving innovation and excellence will grow.
(by Rachel Johnson, Principal Product Manager, MathWorks)
