- #EASE DIAGNOSTICS THROUGH EMISSIONS MACHINE DRIVERS#
- #EASE DIAGNOSTICS THROUGH EMISSIONS MACHINE UPDATE#
- #EASE DIAGNOSTICS THROUGH EMISSIONS MACHINE SOFTWARE#
These design techniques, popularized by the aerospace industry, minimize the weight of structural components without compromising strength. This allows designers to evaluate potential alternative materials (other than steel) that are lighter and more energy efficient. The availability of a wider breadth of analytical, modeling and development tools makes this task easier, including the ability to test the machine’s mechanical performance envelope using advanced stress and strain analysis techniques. The increased efficiency realized with direct drive technology also allows designers to use smaller servo drives, which in turn, use less energy.Ģ) Minimize mass of essential mechanical componentsĪfter eliminating all nonessential mechanical items, the next step is to minimize the mass of all remaining essential components. For one Rockwell Automation customer, replacing a motor-worm gearbox with a direct drive servo motor helped improve mechanical efficiency from 29 percent to 98 percent. The use of this technology creates more reliable, energy-efficient and accurate machines that are less expensive to maintain. Machine builders also use direct drive motion technology to improve mechanical efficiencies. This approach results in reduced energy consumption and maintenance costs, and improved uptime and reliability, which together reduce the machine’s total cost of ownership over its useful life. With these mechatronic tools, engineers can analyze energy usage, build virtual prototypes and select the best mechanical design to maximize machine performance.
#EASE DIAGNOSTICS THROUGH EMISSIONS MACHINE SOFTWARE#
Interactive AHU valve leakage map through the web application.Machine builders increasingly rely on sophisticated performance and simulation software to help eliminate many of these unnecessary components, including line shafts, and costly pneumatics and hydraulics. AHU chilled water valve leakage map of UT Austin main campus.
#EASE DIAGNOSTICS THROUGH EMISSIONS MACHINE UPDATE#
UT Austin facilities personnel can continue to monitor the fault detection and diagnostics results through a dashboard web application developed to automatically update with new data scraped from the building automation systems which manage the campus. Results indicating the AHU valves with leakage were field validated. The width of the plots represents the number of data observations at that feature correlation.
Violin plots showing the predictors versus the outcome classes of normal and leakage and chilled water and steam. Methods to improve the accuracy of classification models included preprocessing data with feature transformations, examination of feature collinearity and skewness, cross validation for resampling, parameter tuning by iterating over a range of input values, and validation of final models for heating and cooling data. Criteria used in supervised machine learning model selection and evaluation. The overall goal of detecting AHU leakage in a practical setting is to find a model that is accurate, and additionally meets the criteria of desired fault detection characteristics. Ten different supervised learning classification algorithms were analyzed as fits for leakage detection. AHU faults are aggregated for each campus building individual building results broken down by their AHUs can be found through the web application along with the results from the additional rule-set algorithms. Heatmap of faults detected through the APAR rule-set. Web application view of the 15-minute resolution datasets collected between July 2017 and December 2018 for each of the 776 AHUs and 107 UT Austin main campus buildings. Provide general fault detection for an AHU system using an expert rule-set which combines the AHU Performance Assessment Rules with rule expressions developed for the 107 buildings (776 AHUs) in the UT Austin main campus dataset.Determine the optimal fault detection methodology for AHU steam and chilled water leakage in order to prioritize maintenance and rehabilitation of valves, and to monitor and maintain improved AHU operation and energy efficiency.
#EASE DIAGNOSTICS THROUGH EMISSIONS MACHINE DRIVERS#
A data-driven FDD for AHUs on a university campus would fill a role in the reduction HVAC energy consumption, which remains one of the main drivers in total building energy use and consequently impacts the total global CO 2 emissions. Fault detection and diagnostics for HVAC systems can potentially reduce 10-40% of total building energy consumption. In particular, air handling units (AHU), devices that circulate air and regulate room temperature and humidity, are the primary focus of most HVAC FDD systems. To reduce HVAC energy inefficiencies, fault detection and diagnostics (FDD) has become a growing field of interest.
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