Predictive maintenance maximizes equipment uptime and reduces maintenance costs for customers and managers alike, but securely sharing and analyzing sensitive machine data is challenging. Aside from technical challenges, there are trust challenges to address. Duality’s platform enables manufacturers to collaborate across organizational boundaries while safeguarding IP and maintaining compliance with data regulations. With encrypted analytics and machine learning, manufacturers can prevent unplanned downtime, extend equipment’s useful life, and optimize operations while improving trust and data protections.
Sharing operational data for preventive maintenance across departments or with external vendors for maintenance predictions involves significant risks and effort. Companies must ensure compliance with regulations, especially if data includes personal or location-based information.
Predictive maintenance requires large amounts of sensitive data (e.g., machine performance logs, IoT sensor data) to be collected and analyzed, often in collaboration with external experts, equipment manufacturers, or service providers.
Predictive maintenance relies on analyzing historical data to identify patterns and predict failures. However, manufacturers face challenges in securely accessing and sharing this data across departments or partners, leading to missed opportunities for accurate predictions.
Duality enables manufacturers to securely collaborate on predictive maintenance data without exposing sensitive information. By using encrypted analytics, secure data processing, and machine learning techniques like federated learning, organizations can train models and analyze performance data while maintaining strict data privacy standards. Our predictive maintenance solution ensures that sensitive operational insights are protected even when analyzed in collaboration.
FHE allows computations on encrypted data without the need to decrypt it. This ensures that sensitive predictive maintenance machine data remains secure throughout its entire lifecycle—even when analyzed or shared.
A machine learning technique that allows training to be performed on disparate datasets without requiring centralization. Duality combines this technique with Confidential Computing to provide a secure and confidential architecture via software.
Also known as Trusted Execution Environment (TEE) or Private computing, this hardware segmentation guarantees a protected place to run large-scale analytics and model training.
Collaborate securely with external suppliers to analyze real-time and historical data, optimizing maintenance schedules and reducing reactive maintenance. Safeguard IP while improving asset performance and minimizing unexpected downtime in supply chain operations.
Use real-time data and predictive analytics to monitor potential issues in heavy machinery, identify anomaly detection patterns, and avoid costly repairs and unexpected downtime.
Use encrypted predictive models to detect potential failure early and schedule maintenance activities, reducing downtime and saving on costly repairs. This approach reduces overall maintenance and operational costs by preventing reactive maintenance.
Accurately predict equipment failures using predictive maintenance algorithms, minimizing costly unplanned downtime, and improving production reliability.
Collaborate securely with service providers and manufacturers using encrypted data, ensuring predictive maintenance without risking proprietary information or IP exposure.
Duality safeguards operational data during predictive analytics, ensuring secure collaboration with suppliers and partners without risking data breaches.
Duality enables manufacturers to securely pool data from various sources, using predictive models to detect maintenance needs without exposing sensitive operational data.
Homomorphic encryption allows manufacturers to run machine learning models on encrypted data, protecting sensitive operational insights during predictive maintenance.
Train AI models on encrypted machine data to predict equipment failures early, ensuring data privacy, reliability, and secure maintenance predictions through machine learning techniques and deep learning.
Duality’s platform is not just a predictive maintenance solution—it is a privacy-preserving collaborative data tool that allows manufacturers to securely run predictive maintenance algorithms, analyze real-time data, and improve equipment performance without compromising security. Manufacturers can confidently collaborate across global teams with our post-quantum-ready encryption, scalable infrastructure, and seamless integration.
Best of all, our privacy-preserving platform ensures that your equipment health data and IP are never compromised.
Leverage Duality’s federated infrastructure to train models and optimize your entire production lifecycle—without compromising privacy or compliance.
Start driving efficiency and innovation today.
