Digital twin for predictive maintenance with system for optimizing the carbon footprint

Abstract

Predictive Maintenance Digital Twin with Carbon Footprint Optimization System, consisting of: a digital twin framework for real-time asset monitoring, performance simulation, and failure prediction; an IoT-enabled data acquisition layer with sensors to collect operational and environmental data; an edge computing module for low-latency anomaly detection and diagnostics; a cloud-based AI engine that uses deep learning for predictive maintenance and remaining useful life (RUL) estimation; a carbon footprint optimization engine for tracking energy consumption, emissions, and sustainability metrics; an LLM-supported NLP interface for analyzing maintenance data, generating prescriptive recommendations, and optimizing CO2 efficiency; a predictive analytics engine for early fault detection, energy optimization, and automatic alerts; an interactive dashboard for visualizing asset health, predictive insights, and tracking carbon impacts; a hybrid cloud-edge architecture for secure, scalable, and efficient real-time processing.

Description

The present invention relates to predictive maintenance systems that utilize digital twins, artificial intelligence (AI), and machine learning (ML) to improve plant monitoring, failure prediction, and maintenance planning. In particular, CO2 fuß optimization is integrated to reduce environmental impact through the analysis of energy consumption, emissions, and sustainability metrics.Industries relying on complex machines are challenging to maintain operational efficiency, reduce downtime, and minimize environmental impact. Conventional maintenance strategies such as reactive maintenance (recovery from failures after their occurrence) and preventive maintenance (scheduled maintenance regardless of the actual condition of the plant) often result in excessive energy consumption, increased carbon emissions and high maintenance costs.Predictive maintenance (PdM) has evolved into an advanced solution that uses sensor data, artificial intelligence (AI), and machine learning (ML) to predict potential failures before they occur. However, existing PdM systems focus primarily on plant reliability and efficiency without incorporating environmental issues. Given increasing legal requirements and sustainability requirements, the industries need maintenance solutions that not only improve reliability, but also optimize energy consumption and reduce emissions. Digital twins - virtual representations of physical plants - have changed the PdM by allowing real-time monitoring, performance simulation and fault prediction. However, managing vast amounts of operational data and deriving useful insights remains a challenge. Moreover, conventional PdM approaches lack mechanisms for assessing the ecological footprint of maintenance work.The predictive maintenance digital twin with carbon footprint optimization system (PM-DT CFO) addresses these constraints by associating real-time monitoring of plants with sustainability metrics. By combining AI-controlled predictive maintenance with tracking of the CO2 fuß, this invention provides energy efficient plant management while reducing maintenance costs and environmental impact.To solve the problem, the present invention provides a digital twin for predictive maintenance with optimization of carbon footprint.The system improves predictive maintenance accuracy through the use of AI-controlled analyses and digital twins.The system reduces unscheduled downtime by early detection of signs of equipment degradation.The system optimizes energy consumption by performing carbon footprint analysis.The system minimizes environmental impact by incorporating sustainability metrics into the maintenance strategies.The system enables detection of anomalies in real-time by IoT sensors and edge computing.The system provides intelligent decision support through LLM-based natural language processing.The system improves life cycle management of plants by optimizing maintenance strategies based on AI-controlled estimates of remaining useful life (RUL).The system provides scalability and security with a hybrid cloud edge architecture.The system facilitates compliance with regulations by automatizing sustainability reporting and monitoring.In one embodiment, a digital twin for predictive maintenance is provided with a system for optimizing the CO2 fuß. The predictive maintenance digital twin with carbon footprint optimization system improves industrial plant management by integrating predictive analytics and sustainability monitoring. It uses digital twins to generate plant virtual replicas in real-time and uses IoT sensor data for precise fault predictions and maintenance planning.In one embodiment, the system's AI-controlled analysis engine applies machine learning (ML) for anomaly detection and residual useful life estimation, while edge computing enables real-time diagnostics with minimal latency. Unlike conventional PdM solutions, it involves optimizing the CO2 fuß by tracking energy consumption and CO2 emissionen to suggest energy efficient maintenance strategies.In one embodiment, an LLM-based NLP interface allows intuitive interaction while an interactive dashboard visualizes the state of the plants in real-time and tracks the carbon effects. The system is based on a hybrid cloud edge architecture and provides safe, scalable, and efficient processing in manufacturing, power, aerospace, and transport industries, thereby reducing cost and emissions and improving reliability.The invention is explained again below with reference to the figure. The following shows:FIG. 1 is a block diagram of a digital twin for predictive maintenance with a system for optimizing the CO2 footprint.Figure 1 shows a block diagram of a digital twin for predictive maintenance with a system for optimizing the CO2 footprint. The system consists of several modules connected to one another for improving predictive maintenance while simultaneously reducing environmental effects. The digital twin framework creates a virtual real-time representation of industrial plants that is continuously updated with IoT sensor data. This digital twin models the behavior of the installations, the operating conditions and the probability of failures and thus enables the dynamic generation of predictive findings. It allows performance simulations, anomaly detection and fault predictions by comparing real-time operating data to historical patterns.The "edge computing for real-time processing" module minimizes latency and improves response times through the integration of edge computing functions. This allows critical anomaly detection and diagnostics to be processed locally, reducing the dependency on cloud-based computations. Edge devices filter and analyze sensor data before forwarding important insights to the central AI engine. In contrast to conventional predictive maintenance systems, the invention includes a carbon footprint optimization engine. This module tracks energy consumption and emissions associated with plant performance, analyzes inefficiencies, and recommends energy efficient maintenance strategies based on AI-based findings. In addition, it ensures compliance with environmental regulations by creating automatic sustainability reports.The system integrates an LLM-based NLP interface to improve human interaction. The maintenance personnel can query natural language diagnostics, obtain AI-generated predictive maintenance recommendations, and analyze sustainability metrics for informed decisions. The system has an interactive dashboard for real-time visualization of the plant state, predictive maintenance warnings, energy consumption trends, and CO2 balance analyses. This interface allows operators to make data-based decisions and improve the efficiency of the overall system. To ensure scalability, security, and efficient data processing, the system uses a hybrid cloud edge architecture. This architecture processes real-time data for fast decision making, uses cloud-based AI engines for advanced predictive analyses, and supports cross-industry applications including manufacturing, power, aerospace, and transport. By combining predictive maintenance and optimizing the CO2 fuß, this system provides optimum plant performance, reduced cost, and ecological sustainability.List of reference characters100 System

Claims

A predictive maintenance digital twin with carbon footprint optimization system, comprising: a digital twin framework for real-time plant monitoring, performance simulation, and failure prediction; an IoT enabled data acquisition layer with sensors for sensing operational and environmental data; an edge computing module for anomaly detection and low latency diagnostics; a cloud-based AI engine employing deep learning for predictive maintenance and remaining useful life (RUL) estimation; an engine for optimizing the CO2 footprint for tracking energy consumption, emissions, and sustainability metrics; A LLM-based NLP interface for analysis of maintenance data, preparation of predictive recommendations, and optimization of CO2 efficiency; a predictive analysis engine for early fault detection, power optimization, and automatic alerts; an interactive dashboard for visualization of plant state, predictive findings, and tracking carbon effects; a hybrid cloud edge architecture for safe, scalable, and efficient real-time processing.The system (100) of claim 1, wherein the digital twin framework continuously updates the plant conditions using real-time sensor data and AI-controlled simulations to optimize performance and minimize downtime.The system (100) of claim 1, wherein the data acquisition layer supports multiple industrial communication protocols including MQTT, OPC UA, and Modbus for seamless sensor integration.The system (100) of claim 1, wherein the edge computing module enables real-time anomaly detection and local critical alert processing to reduce network latency.The system (100) of claim 1, wherein the cloud-based AI engine utilizes historical fault patterns and real-time operating data to dynamically refine predictive maintenance models.The system (100) of claim 1, wherein the LLM-controlled NLP interface enables maintenance personnel to query diagnostics, address problems, and obtain AI-controlled repair instructions.The system (100) of claim 1, wherein the predictive analysis engine continuously improves through self-learning AI models and adjusts to changing operating conditions to enable more accurate fault predictions.The system (100) of claim 1, wherein the interactive dashboard provides customizable visualizations that integrate real-time data, predictive insights, and automation of work orders for optimized maintenance planning.The system (100) of claim 1, wherein the hybrid cloud edge architecture provides secure data transmission, industry standard compliance, and scalable deployment in multiple industrial plants.

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