A Review of Physics-Informed Machine Learning Methods with Applications to Condition Monitoring and Anomaly Detection (2401.11860v1)

Abstract: This study presents a comprehensive overview of PIML techniques in the context of condition monitoring. The central concept driving PIML is the incorporation of known physical laws and constraints into machine learning algorithms, enabling them to learn from available data while remaining consistent with physical principles. Through fusing domain knowledge with data-driven learning, PIML methods offer enhanced accuracy and interpretability in comparison to purely data-driven approaches. In this comprehensive survey, detailed examinations are performed with regard to the methodology by which known physical principles are integrated within machine learning frameworks, as well as their suitability for specific tasks within condition monitoring. Incorporation of physical knowledge into the ML model may be realized in a variety of methods, with each having its unique advantages and drawbacks. The distinct advantages and limitations of each methodology for the integration of physics within data-driven models are detailed, considering factors such as computational efficiency, model interpretability, and generalizability to different systems in condition monitoring and fault detection. Several case studies and works of literature utilizing this emerging concept are presented to demonstrate the efficacy of PIML in condition monitoring applications. From the literature reviewed, the versatility and potential of PIML in condition monitoring may be demonstrated. Novel PIML methods offer an innovative solution for addressing the complexities of condition monitoring and associated challenges. This comprehensive survey helps form the foundation for future work in the field. As the technology continues to advance, PIML is expected to play a crucial role in enhancing maintenance strategies, system reliability, and overall operational efficiency in engineering systems.

• The paper presents integrated physics-informed approaches that leverage feature augmentation, data-driven corrections, and tailored neural architectures for effective monitoring.
• The study demonstrates that physics-informed regularization and specialized network designs enhance both interpretability and predictive accuracy.
• The findings suggest that combining physical simulations with machine learning advances anomaly detection while emphasizing the need for model fidelity.

Physics-Informed Machine Learning for Condition Monitoring and Anomaly Detection

Feature Space Augmentation

The incorporation of physics-informed knowledge into machine learning models often begins with the augmentation of the feature space. Recent advancements in this area have led to the generation of synthetic data, heavily reliant on physical simulations to overcome the dearth of real-world datasets that are robust and well-rounded. This approach, attractive for its ease of implementation, however, hinges on the fidelity of the physical models it employs. If the underlying physics is imperfectly understood or complex system intricacies are not captured, the effectiveness is compromised.

Data-Driven Corrections to Physical Models

Another intriguing method involves utilizing machine learning to correct the outputs from physics-based models. While this approach utilizes data-driven models to adjust physical predictions, it suffers from significant limitations. The machine learning component often models the errors instead of the system, potentially leading to non-physical corrections if training data fails to adequately embody the system's physics.

Physics-Informed Regularization

The field has witnessed innovations in optimizing neural networks with physics-informed regularization. By introducing physical principles into the loss function, which guides the optimization process, these models offer a more physically consistent representation of systems. The versatility of physics-informed regularization methods has been applied to solve differential equations governing system behaviors, providing a promising avenue for data-efficient learning and enhanced robustness. Nevertheless, complexities could arise from the added intricacies within the loss function landscape, potentially affecting the convergence and accuracy.

Tailored Neural Network Architectures

Tailored neural network architectures represent the pinnacle of integrating physical knowledge into machine learning models. The design of the architecture itself can be informed by physics, whereby parts of the network are designed to embody specific physical processes or principles. This could range from assigning interpretable layers within a network to modifying connections or nodes to align with physical laws. This approach stands out for its inherent transparency, as it directly embeds physical principles into the model's architecture, improving interpretability and enhancing the network's behavior predictability. However, the cost is an increase in computational demand and a potential reduction in the model's capability to generalize from the data.

Concluding Perspective

The convergence of machine learning and physics principles offers novel strategies for condition monitoring and fault detection, boasting improved generalization, interpretability, and robustness. While each methodology exhibits unique advantages, the future promises an increased focus on combining these strengths to compensate for their individual limitations. The pursuit to distill complex physical interactions into effective machine learning models continues to challenge both the depth of our physical understanding and the ingenuity of our algorithmic approaches in the field of condition monitoring and anomaly detection.