A method, system, and computer program product are disclosed for implementing enhanced noise impact on function (NIOF) analysis of an IC design having nets in multiple different variable voltage domains next to each other and modeling all multiple worst-case victim-aggressor voltage configurations in a single run leveraging noise abstracts characterized at a single voltage corner. The NIOF analysis enables accurately identifying incorrect victim switching or functional fails, effectively and efficiently providing design verification and the ability to sign-off an IC design with a single run, and enable modifying an integrated circuit design to fix NIOF failures, and fabricating an integrated circuit.

Fouling is formation of deposits on the heat exchanger surfaces that adversely affects operation of heat exchanger. Fouling can be approximated through a set of estimated heat exchanger parameters, which may not be accurate, leading to uncertainty in operation/maintenance decisions and hence the losses. A system and a method for identification and forecasting fouling of a plurality of heat exchangers in a refinery has been provided. The system comprises a digital replica of the heat exchanger network. The digital replica is configured to receive real-time sensor data from a plurality of data sources and provides real-time soft sensing of key parameters. The system is also configured to diagnose the reasons behind a specific condition of fouling. Further, an advisory is provided, that alerts and recommends corrective actions. The system provides estimate for the remaining useful life (RUL) of the heat exchangers and suggests the cleaning schedule.

In sensor data analytics, physics-based models generate high quality data. However, these models consume lot of time as they rely on physical simulations. On the other hand, generative learning takes much less time to generate data, and may be prone to error. Present disclosure provides system and method for generation of synthetic machine data for healthy and abnormal condition using hybrid of physics based and generative model-based approach. Finite Element Analysis (FEA) is used for simulating healthy and faulty parts in machinery with set of parameters and pre-condition(s). Small output data from FEA is fed into a generative model for generating synthesized data by learning data distribution knowledge and representing into latent space. Rule engine is built using statistical features wherein realistic bounds serve as faulty data indicators. Synthesized data which does not satisfies features bounds are discarded. Further, AI-based validation framework is used to analyze quality of synthesized data.