Prognostics and health monitoring
We take your overall infrastructure or other asset health very seriously. As a result, we have developed several robust prognostic and health monitoring programs, including our predictive maintenance plans, our online health monitoring technology and our timely anomaly detection software.
We use sophisticated models based on our extensive knowledge of our asset's performance under different working conditions and compare data from your asset against those models to determine if proactive maintenance is necessary.
Our Products
The Role of IoT & AI in Battery Management of Electric Vehicles
One way to improve battery performance is by implementing IoT and AI technologies. Trustworthy ML algorithms can be used to monitor the health of a battery over time by analyzing various factors, such as temperature and voltage levels. These algorithms can then recommend solutions that optimise performance or prevent damage from occurring entirely.
Digital Twins in Structural Health Monitoring
Digital twin is one of the most modern and promising technologies in realizing smart manufacturing and implementing Industry 4.0. Digital twin offers opportunity to integrate the physical world with digital world with a seamless data source.
Civil engineering industry, in general, is facing many challenges in the process of digital transformation to improve efficiency and technology to meet the current growth rate of the economy. Digital twin technology has the potential to transform an??d improve the exploitation and management of infrastructure in civil engineering, especially in the service phase.
Based on Digital twin model, managers and maintenance operators can test different scenarios, improve efficiency, and make accurate decisions in maintenance of the structure, leading to reduction of management and other regular monitoring costs, as well as accurate prediction of risks during the lifespan of the infrastructure.
AI and ML-based Anomaly Detection
In today’s digital environments, anomalies in data patterns occur almost every day. Affecting both data in transfer and static data, these anomalies can cause large interruptions if they are not intercepted.
In 2020,over 80% of companies worldwide faced some downtime due to undetected anomalies. A typical outage lasts around four hours and costs around $2 million.
Energy system: Energy production by wind farms and lack of predictive maintenance may lead to severe hardware failure. We provide anomaly detection techniques to stop a minor issue from becoming a widespread, time-consuming problems.
Telecom: Telecom operators tend to want to improve the network’s health and operation, so we help them actively apply tools for network anomaly detection.
Healthcare: We design machine learning to enhance the speed and accuracy of fraud detection and diagnosis procedures; for example, unsupervised anomaly detection is used in CT scanning image analysis.
Supply chain: Inefficiency of the supply chain can cause significant disruptions in many industries (e.g., retail) and also lower productivity and revenue. We provide machine learning-based anomaly detection for demand planning and provides better predictions than traditional analytics in most cases. We also provide AI tools to improve inventory management, automate root-cause analysis, and enhance supply and production planning.
We can develop new algorithms and approaches for physics-based or data-driven diagnostics & prognostics to meet your needs.
Resource-constrainted prognostics
Approaches for performing prognostics in resource-constrained environments, such as small spacecraft or a node in the Node-Edge-Cloud framework.
Uncertainty representation and management
Identification of sources of uncertainty, quantification and propagation of uncertainty, and representation of uncertainty in prediction.
Physics-based and data-driven diagnostics & prognostics tools
For example, fault identification and characterization, in-flight Systems Health Management of Unmanned Aerial Vehicle mechanical parts, damage progression and fatigue life analysis in composite material.
Nondestructive evaluation / structural health monitoring
Health-informed decision-making under uncertainty
Automated decision-making strategies utilizing health information (both diagnostics & prognostics) with uncertainty
