Demonstration of fault detection and diagnosis methods for air-handling units (ASHRAE 1020-RP)

Results are presented from controlled field tests of two methods for detecting and diagnosing faults in HVAC equipment. The tests were conducted in a unique research building that featured two air-handling units serving matched sets of unoccupied rooms with adjustable internal loads. Tests were also conducted in the same building on a third air handler serving areas used for instruction and by building staff. One of the two fault detection and diagnosis (FDD) methods used first-principles-based models of system components. The data used by this approach were obtained from sensors typically installed for control purposes. The second method was based on semiempirical correlations of submetered electrical power with flow rates or process control signals. Faults were introduced into the air-mixing, filter-coil, and fan sections of each of the three air-handling units. In the matched air-handling units, faults were implemented over three blind test periods (summer, winter, and spring operating conditions). In each test period, the precise timing of the implementation of the fault conditions was unknown to the researchers. The faults were, however, selected from an agreed set of conditions and magnitudes, established for each season. This was necessary to ensure that at least some magnitudes of the faults could be detected by the FDD methods during the limited test period. Six faults were used for a single summer test period involving the third air-handling unit. These fault conditions were completely unknown to the researchers and the test period was truly blind. The two FDD methods were evaluated on the basis of their sensitivity, robustness, the number of sensors required, and ease of implementation. Both methods detected nearly all of the faults in the two matched air-handling units but fewer of the unknown faults in the third air-handling unit. Fault diagnosis was more difficult than detection. The first-principles-based method misdiagnosed several faults. The electrical power correlation method demonstrated greater success in diagnosis, although the limited number of faults addressed in the tests contributed to this success. The first-principles-based models require a larger number of sensors than the electrical power correlation models, although the latter method requires power meters that are not typically installed. The first-principles-based models require training data for each subsystem model to tune the respective parameters so that the model predictions more precisely represent the target system. This is obtained by an open-loop test procedure. The electrical power correlation method uses polynomial models generated from data collected from “normal” system operation, under closed-loop control.Both methods were found to require further work in three principal areas: to reduce the number of parameters to be identified; to assess the impact of less expensive or fewer sensors; and to further automate their implementation. The first-principles-based models also require further work to improve the robustness of predictions

Improved Selectivity and Sensitivity of Gas Sensing Using a 3D Reduced Graphene Oxide Hydrogel with an Integrated Microheater

Low-cost, one-step, and hydrothermal synthesized 3D reduced graphene oxide hydrogel (RGOH) is exploited to fabricate a high performance NO₂ and NH₃ sensor with an integrated microheater. The sensor can experimentally detect NO₂ and NH₃ at low concentrations of 200 ppb and 20 ppm, respectively, at room temperature. In addition to accelerating the signal recovery rate by elevating the local silicon substrate temperature, the microheater is exploited for the first time to improve the selectivity of NO₂ sensing. Specifically, the sensor response from NH₃ can be effectively suppressed by a locally increased temperature, while the sensitivity of detecting NO₂ is not significantly affected. This leads to good discrimination between NO₂ and NH₃. This strategy paves a new avenue to improve the selectivity of gas sensing by using the microheater to raise substrate temperature

Highly Stretchable and Transparent Thermistor Based on Self-Healing Double Network Hydrogel

An ultrastretchable thermistor that combines intrinsic stretchability, thermal sensitivity, transparency, and self-healing capability is fabricated. It is found the polyacrylamide/carrageenan double network (DN) hydrogel is highly sensitive to temperature and therefore can be exploited as a novel channel material for a thermistor. This thermistor can be stretched from 0 to 330% strain with the sensitivity as high as 2.6%/°C at extreme 200% strain. Noticeably, the mechanical, electrical, and thermal sensing properties of the DN hydrogel can be self-healed, analogous to the self-healing capability of human skin. The large mechanical deformations, such as flexion and twist with large angles, do not affect the thermal sensitivity. Good flexibility enables the thermistor to be attached on nonplanar curvilinear surfaces for practical temperature detection. Remarkably, the thermal sensitivity can be improved by introducing mechanical strain, making the sensitivity programmable. This thermistor with tunable sensitivity is advantageous over traditional rigid thermistors that lack flexibility in adjusting their sensitivity. In addition to superior sensitivity and stretchability compared with traditional thermistors, this DN hydrogel-based thermistor provides additional advantages of good transparency and self-healing ability, enabling it to be potentially integrated in soft robots to grasp real world information for guiding their actions