Drive Analytics AI With Physics-Informed Training Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing monitoring and analytics systems for drive systems and apparatuses face challenges in acquiring detailed and reliable data, leading to inefficient and unreliable predictive maintenance due to high data consumption, overfitting, and lack of consideration for physics-based domain knowledge, especially in resource-constrained environments.
Innovation Solution
Implementing domain-informed machine learning (ML)/artificial intelligence (AI) models, such as physics-informed neural networks (PINNs), that integrate physics-based knowledge with data-driven learning to enhance predictive maintenance and condition monitoring directly on the drive/gateway level, leveraging historical and simulation data to ensure physics-consistent analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high amounts of detailed data are acquired from deployed drive systems for training analytics algorithms, then model accuracy is improved, but data acquisition difficulty and resource consumption increase
Solution Approach 1:
The patent applies preliminary action by generating synthetic training data through physics-based simulations before deploying the model to actual drive systems. This pre-generation of training data using domain knowledge and physics models eliminates the need to collect large amounts of real-world data, thereby reducing data acquisition complexity while maintaining model accuracy through physically consistent synthetic datasets
Solution Approach 2:
The patent introduces physics-based domain knowledge and simulation models as intermediaries between real-world drive systems and machine learning algorithms. These intermediaries generate synthetic training data that bridges the gap between physical reality and computational models, reducing direct dependence on extensive real data collection while preserving physical consistency in the training process
2Reliability
If analytics algorithms are trained with sufficient data to ensure reliability, then predictive maintenance capability is improved, but data transfer and processing requirements increase
Solution Approach 1:
The patent extracts the essential physical principles and domain knowledge from complex real-world drive system operations and encapsulates them in simplified physics-based models. These extracted principles generate synthetic training data that captures the essential failure modes and operational characteristics without requiring transfer and processing of large volumes of raw sensor data, thereby maintaining reliability while reducing energy consumption
Solution Approach 2:
The patent creates synthetic copies of real drive system behavior through physics-based simulations. These synthetic data copies replicate the essential characteristics and failure patterns of actual drive systems, enabling training of reliable predictive maintenance models without the need to transfer, store, and process large amounts of real operational data, thus reducing energy requirements
3Stability of the object's composition
If domain knowledge is integrated into ML models, then model robustness to physics constraints is improved, but model complexity increases
Solution Approach 1:
The patent incorporates physics constraints by modifying the loss function parameters and training objectives of the machine learning models. Instead of changing the fundamental model architecture, physics knowledge is integrated through parameter adjustments in the optimization process, such as adding physics-based regularization terms or constraints to the loss function, thereby ensuring physics consistency without significantly increasing model structural complexity
Data Source
AI summary
A method for obtaining a domain-informed machine learning/artificial intelligence, ML/AI, model for drive analytics includes obtaining first data indicative of a set of data points, wherein each data point is associated with a behavior of a drive apparatus and/or drive system. The method further comprises obtaining second data indicative of domain knowledge comprising physics knowledge associated with a behavior of the drive apparatus and/or drive system and/or with an environment of the drive apparatus and/or drive system. The method further comprises training a machine learning/artificial intelligence, ML/AI, model by jointly utilizing the first data and the second data to obtain the domain-informed ML/AI model for drive analytics.