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Predictive analytics to reduce mechanical fallout by creating a model using the company’s historical data to determine when performance is failing and requires maintenance, saving the company time and money.
Provide a 150 words response to the post below, make sure you help your peer think further and suggest other readings by adding new references.
Post by Connor Brown
Through one peer-reviewed article, researchers examine the use of predictive modeling on the simulation of Power Electric Systems. Predictive modeling is particularly useful when testing or simulation in real-time is difficult, costly or impossible. “Simulation provides a way to test the control unit in terms of kinds of potential abnormalities and faults while avoiding high power testing environment… It reduces the design cost and minimizes the research period of the high-power electronic system (HPE)”. While data can be collected on the actual mechanical processing, it is generally transmitted and transformed into business insight with a delay. Using predictive modeling techniques to anticipate performance is a thoughtful and practical use of collected data and can save the company time and money by avoiding the use of a high power testing environment to establish a fix.
Researchers created “a modified system-level solver for real-time simulation” which determined the rate and voltage of the systems and considerably reduced the time it took to calculate the circuit element and switch network. The team considered this a great success as it took the model only 25ns to settle the calculation.
By fine tuning and refining their model, researchers were able to come up with a time-saving solution using predictive analytics. This is relevant to Studer Innotec’s goals of solar PV optimization; we will propose using predictive analytics to reduce mechanical fallout by creating a model using the company’s historical data to determine when performance is failing and requires maintenance, saving the company time and money.
Peer Reviewed Article:
Liu, C. (2020). 2019-2020 index IEEE Transactions on Industrial Electronics Vol. 67. IEEE Transactions on Industrial Electronics, 67(12), 10997–11201. https://doi.org/10.1109/tie.2020.3045338