The automotive manufacturing sector often grapples with the dual challenge of maintaining high product quality while controlling operating expenses. AI-based anomaly detection is emerging as a strategic solution to this issue. By identifying irregular patterns in machine data, this technology predicts potential problems before they occur, thereby enhancing process efficiency, reducing downtime, and improving product quality
Designing an AI-Based Anomaly Detection Solution
Developing an AI-based anomaly detection system involves a comprehensive workflow that includes planning, data gathering, algorithm development, and deployment. Engineers must grasp both algorithm development and the specific operational environment to create effective solutions.
Planning and Data Gathering
The design process starts with defining the problem and assessing available sensor data, components, and processes. Engineers must identify what constitutes an anomaly and gather data using sensors for continuous monitoring, complemented by manual checks for accuracy. In-line measurement systems collect extensive data during vehicle production, often linked to vehicle VINs, which can be used to train anomaly detection systems for predictive maintenance and quality control. However, processing large data volumes can be costly and time-consuming.
Synthetic data generation from simulations offers an alternative, especially when real data is scarce or difficult to obtain. By simulating machine operations, engineers can create anomalous data that might be hard to capture with real hardware. This approach requires a deep understanding of system physics to ensure simulations accurately model anomalies.
Designing an Anomaly Detection Algorithm
The algorithm design process begins with organizing and preprocessing data to prepare it for analysis. This includes reformatting, restructuring, handling missing values, and removing outliers. Engineers must then choose an appropriate anomaly detection technique based on the data’s characteristics, the nature of anomalies, and available computational resources.
Supervised and Unsupervised Learning
Supervised learning is used when historical data can be clearly labeled as normal or anomalous. For instance, Daihatsu, by utilizing machine learning and feature extraction capabilities in MATLAB®, automated the assessment of engine knocking sounds using AI, achieving accuracy comparable to skilled human workers. In cases where labeled data is unavailable, unsupervised learning is employed. Here, the model learns the characteristics of normal data and flags deviations as anomalies, allowing it to identify problems even if they haven’t been previously encountered.
Feature Engineering
Feature engineering involves extracting meaningful quantities from raw data, which helps AI models learn more efficiently from underlying patterns. Experienced engineers often know which features are critical for accurate anomaly detection.
Validation and Testing
Validating and testing AI models is crucial for ensuring their reliability and robustness. Engineers typically split data into training, validation, and test sets. The model is fine-tuned using training and validation data, while test data evaluates its performance on unseen data. Performance metrics, such as precision and recall, guide further refinements to meet specific anomaly detection needs.
Deployment and Integration
A trained AI model becomes valuable when deployed in an operational setting, where it makes real-time predictions on new data. Engineers consider computational needs, latency, and scalability when selecting deployment environments, ranging from edge devices for real-time detection to cloud platforms with vast computational power. Integration involves developing APIs for accessing model predictions and establishing data pipelines to ensure the model receives properly formatted input, maximizing its effectiveness within the application or system.
Conclusion
AI-based anomaly detection is revolutionizing manufacturing operations in the automotive industry. By processing sensor data and extracting patterns, AI models simplify issue identification, reduce defects, extend machine lifespan, and lower operational costs. This technology represents a significant step forward in achieving efficient, reliable, and high-quality automotive manufacturing.
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(by Rachel Johnson, Principal Product Manager, MathWorks)
